Welcome to Flambé

Welcome to Flambé, a PyTorch-based library that allows users to:

• Run complex experiments with multiple training and processing stages.
• Search over an arbitrary number of parameters and reduce to the best trials.
• Run experiments remotely over many workers, including full AWS integration.
• Easily share experiment configurations, results and model weights with others.

Visit the github repo: https://github.com/Open-ASAPP/flambe

A simple Text Classification experiment

!Experiment

name: sst-text-classification

pipeline:

  # stage 0 - Load the Stanford Sentiment Treebank dataset and run preprocessing
  dataset: !SSTDataset
    transform:
      text: !TextField
      label: !LabelField

  # Stage 1 - Define a model
  model: !TextClassifier
      embedder: !Embedder
        embedding: !torch.Embedding  # automatically use pytorch classes
          num_embeddings: !@ dataset.text.vocab_size
          embedding_dim: 300
        embedding_dropout: 0.3
        encoder: !PooledRNNEncoder
          input_size: 300
          n_layers: !g [2, 3, 4]
          hidden_size: 128
          rnn_type: sru
          dropout: 0.3
      output_layer: !SoftmaxLayer
          input_size: !@ model.embedder.encoder.rnn.hidden_size
          output_size: !@ dataset.label.vocab_size

  # Stage 2 - Train the model on the dataset
  train: !Trainer
    dataset: !@ dataset
    model: !@ model
    train_sampler: !BaseSampler
    val_sampler: !BaseSampler
    loss_fn: !torch.NLLLoss
    metric_fn: !Accuracy
    optimizer: !torch.Adam
      params: !@ train.model.trainable_params
    max_steps: 10
    iter_per_step: 100

  # Stage 3 - Eval on the test set
  eval: !Evaluator
    dataset: !@ dataset
    model: !@ train.model
    metric_fn: !Accuracy
    eval_sampler: !BaseSampler

# Define how to schedule variants
schedulers:
  train: !tune.HyperBandScheduler

The experiment can be executed by running:

flambe experiment.yaml

Tip

All objects in the pipeline are subclasses of Component, which are automatically registered to be used with YAML. Custom Component implementations must implement run() to add custom behavior when being executed.

By defining a cluster:

!AWSCluster

name: my-cluster  # Make sure to name your cluster

factories_num: 2 # Number of factories to spin up, there is always just 1 orchestrator

factories_type: g3.4xlarge
orchestrator_type: t3.large

key: '/path/to/ssh/key'

...

Then the same experiment can be run remotely:

flambe experiment.yaml --cluster cluster.yaml

Progress can be monitored via the Report Site (with full integration with Tensorboard):

Getting Started

Check out our Installation Guide and Quickstart sections to get up and running with Flambé in just a few minutes!

Installation

Via pip

You can install the latest stable version of flambé as follows:

pip install flambe  # CPU Version
# or
pip install flambe[cuda]  # With GPU / CUDA support

From source

For the lastest version you can install from source:

git clone git@github.com:Open-ASAPP/flambe.git
cd flambe
pip install .

Hint

We recommend installing flambé in an isolated virtual environment

Quickstart

Flambé runs processes that are described using YAML files. When executing, Flambé will automatically convert these processes into Python objects and it will start executing them based on their behavior.

One of the processes that Flambé is able to run is an Experiment:

simple-exp.yaml

!Experiment

name: sst

pipeline:

  # stage 0 - Load the dataset object SSTDataset and run preprocessing
  dataset: !SSTDataset
    transform:
      text: !TextField  # Another class that helps preprocess the data
      label: !LabelField

This Experiment just loads the Stanford Sentiment Treebank dataset which we will use later.

Important

Note that all the keywords following ! are just Python classes (Experiment, SSTDataset, TextField, LabelField) whose keyword parameters are passed to the __init__ method.

Executing Flambé

Flambé can execute the previously defined Experiment by running:

flambe simple-exp.yaml

Because of the way Experiments work, flambé will start executing the pipeline sequentially. Once done, you should see the generated artifacts in flambe-output/output__sst/. Obviously, these artifacts are useless at this point. Let’s add a Text Classifier model and train it with this same dataset:

See also

For a better understanding of Experiment read the Experiments section.

A Simple Experiment

Lets add a second stage to the pipelin to declare a text classifier. We can use Flambé’s TextClassifier:

!Experiment

name: sst
pipeline:

  # stage 0 - Load the dataset object SSTDataset and run preprocessing
  [...]  # Same as before


  # stage 1 - Define the model
  model: !TextClassifier
    embedder: !Embedder
      embedding: !torch.Embedding
        num_embeddings: !@ dataset.text.vocab_size
        embedding_dim: 300
      encoder: !PooledRNNEncoder
        input_size: 300
        rnn_type: lstm
        n_layers: !g [2, 3, 4]
        hidden_size: 256
    output_layer: !SoftmaxLayer
      input_size: !@ model.embedder.encoder.rnn.hidden_size
      output_size: !@ dataset.label.vocab_size

By using !@ you can link to attributes of previously defined objects. Note that we take num_embeddings value from the dataset’s vocabulary size that it is stored in its text attribute. These are called Links (read more about them in Linking).

Important

When using !@ you can access attributes starting always from the top level object. For example:

input_size: !@ model.embedder.encoder.rnn.hidden_size

Note that the path starts from model (even if input_size is also declared inside the same object being referenced).

Always refer to the documentation of the object you’re linking to in order to understand what attributes it actually has when the link will be resolved.

Important

Flambé supports native hyperparameter search!

n_layers: !g [2, 3, 4]

Above we define 3 variants of the model, each containing different amount of n_layers in the encoder.

Now that we have the dataset and the model, we can add a training process. Flambé provides a powerful and flexible implementation called Trainer:

!Experiment

name: sst
pipeline:

  # stage 0 - Load the dataset object SSTDataset and run preprocessing
  [...]  # Same as before


  # stage 1 - Define the model
  [...]  # Same as before

  # stage 2 - train the model on the dataset
  train: !Trainer
    dataset: !@ dataset
    train_sampler: !BaseSampler
      batch_size: 64
    val_sampler: !BaseSampler
    model: !@ model
    loss_fn: !torch.NLLLoss  # Use existing PyTorch negative log likelihood
    metric_fn: !Accuracy  # Used for validation set evaluation
    optimizer: !torch.Adam
      params: !@ train.model.trainable_params
    max_steps: 20
    iter_per_step: 50

Tip

Flambé provides full integration with Pytorch object by using torch prefix. In this example, objects like NLLLoss and Adam are directly used in the configuration file!

Tip

Additionally we setup some Tune classes for use with hyperparameter search and scheduling. They can be accessed via !tune.ClassName tags. More on hyperparameter search and scheduling in Experiments.

Monitoring the Experiment

Flambé provides a powerful UI called the Report Site to monitor progress in real time. It has full integration with Tensorboard.

When executing the experiment (see Executing Flambé), flambé will show instructions on how to launch the Report Site.

See also

Read more about monitoring in Report Site section.

Artifacts

By default, artifacts will be located in flambe-ouput/ (relative the the current work directory). This behaviour can be overriden by providing a save_path parameter to the Experiment.

flambe-output/output__sst
├── dataset
│   └── 0_2019-07-23_XXXXXX
│       └── checkpoint
│           └── checkpoint.flambe
│               ├── label
│               └── text
├── model
│   ├── n_layers=2_2019-07-23_XXXXXX
│   │    └── checkpoint
│   │        └── checkpoint.flambe
│   │            ├── embedder
│   │            │   ├── embedding
│   │            │   └── encoder
│   │            └── output_layer
│   ├── n_layers=3_2019-07-23_XXXXXX
│   │    └── ...
│   └── n_layers=4_2019-07-23_XXXXXX
│       └── ...
└── trainer
    ├── n_layers=2_2019-07-23_XXXXXX
    │    └── checkpoint
    │        └── checkpoint.flambe
    │            ├── model
    │            │   ├── embedder
    │            │   │   └── ...
    │            │   └── output_layer
    │            └── dataset
    │                └── ...
    ├── n_layers=3_2019-07-23_XXXXXX
    │    └── ...
    └── n_layers=4_2019-07-23_XXXXXX
         └── ...

Note that the output is 100% hierarchical. This means that each component is isolated and reusable by itself.

load() is a powerful utility to load previously saved objects.

import flambe

path = "flambe-output/output__sst/train/n_layers=4_.../.../model/embedder/encoder/"
encoder = flambe.load(path)

Important

The output folder also reflects the variants that were speficied in the config file. There is one folder for each variant in model and in trainer. The trainer inherits the variants from the previous components, in this case the model. For more information on variant inheritance, go to Search Options.

Recap

You should be familiar now with the following concepts

• Experiments can be represented in a YAML format where a pipeline can be specified, containing different components that will be executed sequentially.
• Objects are referenced using ! + the class name. Flambé will compile this structure into a Python object.
• Flambé supports natively searching over hyperparameters with tags like !g (to perform Grid Search).
• References between components are done using !@ links.
• The Report Site can be used to monitor the Experiment execution, with full integration with Tensorboard.

Try it yourself!

Here is the full config we used in this tutorial:

simple-exp.yaml

!Experiment

name: sst
pipeline:

  # stage 0 - Load the dataset object SSTDataset and run preprocessing
  dataset: !SSTDataset
    transform:
      text: !TextField  # Another class that helps preprocess the data
      label: !LabelField


  # stage 1 - Define the model
  model: !TextClassifier
    embedder: !Embedder
      embedding: !torch.Embedding
        num_embeddings: !@ dataset.text.vocab_size
        embedding_dim: 300
      encoder: !PooledRNNEncoder
        input_size: 300
        rnn_type: lstm
        n_layers: !g [2, 3, 4]
        hidden_size: 256
    output_layer: !SoftmaxLayer
      input_size: !@ model.embedder.encoder.rnn.hidden_size
      output_size: !@ dataset.label.vocab_size

  # stage 2 - train the model on the dataset
  train: !Trainer
    dataset: !@ dataset
    train_sampler: !BaseSampler
      batch_size: 64
    val_sampler: !BaseSampler
    model: !@ model
    loss_fn: !torch.NLLLoss  # Use existing PyTorch negative log likelihood
    metric_fn: !Accuracy  # Used for validation set evaluation
    optimizer: !torch.Adam
      params: !@ train.model.trainable_params
    max_steps: 20
    iter_per_step: 50

We encourage you to execute the experiment and to start getting familiar with the artifacts and the report site.

Next Steps

• Components: SSTDataset, Trainer and TextClassifier are examples of Component. These objects are the core of the experiment’s pipeline.
• Runnables: flambé supports running multiple processes, not just Experiments. These objects must implement Runnable.
• Clusters: learn how to create clusters and run remote experiments.
• Extensions: flambé provides a simple and easy mechanism to declare custom Runnable and Component.
• Scheduling and Reducing Strategies: besides grid search, you might also want to try out more sophisticated hyperparameter search algorithms and resource allocation strategies like Hyperband.

Motivation

Flambé’s primary objective is to speed up all of the research lifecycle including model prototyping, hyperparameter optimization and execution on a cluster.

Why Flambé?

1. Running machine learning experiments takes a lot of continuous and tedious effort.
2. Standardizing data preprocessing and weights sharing across the community or within a team is difficult.

We’ve found that while there are several new libraries offering a selection of reliable model implementations, there isn’t a great library that couples these modules with an experimentation framework. Since experimentation (especially hyper-parameter search, deployment on remote machines, and data loading and preprocessing) is one of the most important and time-consuming aspects of ML research we decided to build Flambé.

An important component of Flambé is Ray, an open source distributed ML library. Ray has some of the necessary infrastructure to build experiments at scale; coupled with Flambé you could be tuning many variants of your already existing models on a large cluster in minutes! Flambé’s crucial contribution is to facilitate rapid iteration and experimentation where tools like Ray and AllenNLP alone require large development costs to integrate.

The most important contribution of Flambé is to improve the user experience involved in doing research, including the various phases of experimentation we outlined at the very beginning of this page. To do this well, we try to adhere to the following values:

Core values

• Practicality: customize functionality in code, and iterate over settings and hyperparameters in config files.
• Modularity & Composability: rapidly repurpose existing code for hyper-parameter optimization and new use-cases
• Reproducability: reproducible experiments, by anyone, at any time.

Components

The most important class in Flambé is Component which implements loading from YAML (using !ClassName notation) and saving state.

Loading and Dumping from YAML

A Component can be created from a YAML config representation, as seen the Quickstart example.

Lets take the previously used TextClassifier component:

model.yaml

  !TextClassifier
  embedder: !Embedder
    embedding: !torch.Embedding
      num_embeddings: 200
      embedding_dim: 300
    encoder: !PooledRNNEncoder
      input_size: 300
      rnn_type: lstm
      n_layers: 3
      hidden_size: 256
  output_layer: !SoftmaxLayer
    input_size: 256
    output_size: 10

Loading and dumping objects can be done using flambe.compile.yaml module.

from flambe.compile import yaml

# Loading from YAML into a Schema
text_classifier_schema = yaml.load(open("model.yaml"))
text_classifier = text_classifier_schema()  # Compile the Schema

# Dumping object
yaml.dump(text_classifier, open("new_model.yaml", "w"))

Important

Components compile to an intermediate state called Schema when calling yaml.load(). This partial representation can be compiled into the final object by calling obj() (ie executing __call__), as shown in the example above. For more information about this, go to Delayed Initialization.

See also

For more examples of the YAML representation of an object look at understanding-configuration_label

Saving and Loading State

While YAML represents the “architecture” or how to create an instance of some class, it does not capture the state. For state, Components rely on a recursive get_state() and load_state() methods that work similarly to PyTorch’s nn.Module.state_dict and nn.Module.load_state_dict:

from flambe.compile import yaml

# Loading from YAML into a Schema
text_classifier_schema = yaml.load(open("model.yaml"))
text_classifier = text_classifier_schema()  # Compile the Schema

state = text_classifier.get_state()

from flambe.nlp.classification import TextClassifier

another_text_classifier = TextClassifier(...)
another_text_classifier.load_state(state)

Semantic Versioning

In order to identify and describe changes in class definitions, flambé supports opt-in semantic class versioning. (If you’re not familiar with semantic versioning see this link).

Each class has a class property _flambe_version to prevent conflics when loading previously saved states. Initially, all versions are set to 0.0.0, indicating that class versioning should not be used. Once you increment the version, Flambé will then start comparing the saved class version with the version on the class at load-time.

See also

See Adding Custom State for more information about get_state() and load_state().

Delayed Initialization

When you load Components from YAML they are not initialized into objects immediately. Instead, they are precompiled into a Schema that you can think of as a blueprint for how to create the object later. This mechanism allows Components to use links and grid search options.

If you load a schema directly from YAML you can compile it into an instance by calling the schema:

from flambe.compile import yaml

schema = yaml.load('path/to/file.yaml')
obj = schema()

Core Components

Dataset

This object holds the training, validation and test data. Its only requirement is to have the three properties: train, dev and test, each pointing to a list of examples. For convenience we provide a TabularDataset implementation of the interface, which can load any csv or tsv type format.

from flambe.dataset import TabularDataset
import numpy as np

# Random dataset
train = np.random.random((2, 100))
val = np.random.random((2, 10))
test = np.random.random((2, 10))

dataset = TabularDataset(train, val, test)

Field

A field takes raw examples and produces a torch.Tensor (or tuple of torch.Tensor). We provide useful fields such as TextField, or LabelField which perform tokenization and numericalization.

from flambe.field import TextField
from flambe.tokenizer import WordTokenizer

import numpy as np

# Random dataset
data = np.array(['Flambe is awesome', 'This framework rocks!'])
text_field = TextField(WordTokenizer())

# Setup the entire dataset to build vocab.
text_field.setup(data)
text_field.vocab_size  # Returns to 9

text_field.process("Flambe rocks")  # Returns tensor([6, 1])

Sampler

A sampler produces batches of data, as an interator. We provide a simple BaseSampler implementation, which takes a dataset as input, as well as the batch size, and produces batches of data. Each batch is a tuple of tensors, padded to the maximum length along each dimension.

from flambe.sampler import BaseSampler
from flambe.dataset import TabularDataset
import numpy as np

dataset = TabularDataset(np.random.random((2, 10)))

sampler = BaseSampler(batch_size=4)
for batch in sampler.sample(dataset):
    # Do something with batch

Module

This object is the main model component interface. It must implement the forward method as PyTorch’s nn.Module requires.

We also provide additional machine learning components in the nn submodule, such as Encoder with many different implementations of these interfaces.

Trainer

A Trainer takes as input the training and dev samplers, as well as a model and an optimizer. By default, the object keeps track of the last and best models, and each call to run is considered to be an arbitrary of training iterations, and a single evaluation pass over the validation set. It implements the metric() method, which points to the best metric observed so far.

Evaluator

An Evaluator evaluates a given nn`Module over a Dataset and computes given metrics.

Script

A Script integrate a pre-written script with Flambé.

Important

For more detailed information about this Components, please refer to their documentation.

Custom Component

Custom Components should implement the run() method. This method performs a single computation step, and returns a boolean, indicating whether the Component is done executing (True iff there is more work to do).

class MyClass(Component):

    def __init__(self, a, b):
        super().__init__()
        ...

    def run(self) -> bool:
        ...
        return continue_flag

Tip

We recommend always extending from an implementation of Component rather than implementing the plain interface. For example, if implementing an autoencoder, inherit from Module or if implementing cross validation training, inherit from Trainer.

If you would like to include custom state in the state returned by get_state() method see the Adding Custom State section and the Component package reference.

Then in YAML you could do the following:

!MyClass
  a: val1
  b: val2

# or using the registrable_factory

Flambé also provides a way of registering factory methods to be used in YAML:

class MyClass(Component):

    ...

    @registrable_factory
    @classmethod
    def special_factory(cls, x, y):
        a, b = do_something(x, y)
        return cls(a, b)

Now you can do:

!MyClass.special_factory
  x: val1
  y: val2

For information on how to add your custom Component in the YAML files, go to Extensions

Runnables

Runnables are top level objects that flambé is able to run. Experiment is an example of a Runnable.

Implementing Runnables is as easy as implementing the Runnable interface. Esentially, it requires a single method run().

Hint

A Runnable object can be executed by flambé:

flambe runnable.yaml

For example, let’s imagine we want to implement an S3Pusher that takes an Experiment output folder and uploads the content to a specific S3 bucket:

from flambe.runnable import Runnable

class S3Pusher(Runnable):

    def __init__(self, experiment_path: str, bucket_name: str) -> None:
        super().__init__()
        self.experiment_path = experiment_path
        self.bucket_name = bucket_name

    def run(self, **kwargs) -> None:
        """Upload a local folder to a S3 bucket"""

        # Code to upload to S3 bucket
        S3_client = boto3.client("s3")
        for root, dirs, files in os.walk(self.experiment_path):
            for f in files:
                s3C.upload_file(os.path.join(root, f), self.bucketname, f)

This class definition can now be included in an extension (read more about extensions in Extensions) and used as a top level object in a YAML file.

ext: /path/to/S3Pusher/extension:
---

!ext.S3Pusher
    experiment_path: /path/to/my/experiment
    bucket_name: my-bucket

Then, simply execute:

flambe s3pusher.yaml

Providing secrets

All Runnables have access to secret information that the users can share via an ini file.

By executing the Runnable with a --secrets parameter, then the Runnable can access the secrets through the config attribute:

Let’s say that our S3Pusher needs to access the AWS_SECRET_TOKEN. Then:

from flambe.runnable import Runnable

class S3Pusher(Runnable):

    def __init__(self, experiment_path: str, bucket_name: str) -> None:
        # Same as before
        ...

    def run(self, **kwargs) -> None:
        """Upload a local folder to a S3 bucket"""

        # Code to upload to S3 bucket
        S3_client = boto3.client("s3", token=self.config['AWS']['AWS_SECRET_TOKEN'])
        for root, dirs, files in os.walk(self.experiment_path):
            for file in files:
                s3C.upload_file(os.path.join(root, file), self.bucketname, file)

Then if secrets.ini contains:

[AWS]
AWS_SECRET_TOKEN = ABCDEFGHI123456789

We can execute:

flambe s3pusher.yaml --secrets secret.ini

Automatic extensions installation

Important

To understand this section you should be familiar with extensions. For information about extensions, go to Extensions.

When executing a Runnable, it’s possible that extensions are being involved. For example:

ext: /path/to/extension
other_ext: http://github.com/user/some_extension
---

!ext.CustomRunnable
    ...
    param: !other_ext.CustomComponent

Flambé provides a -i / --install-extensions flag to automatically “pip” installs the extensions:

flambe custom_runnable.yaml -i

By default, this is not activated and the user needs to install the extensions beforehand.

Warning

Installing extensions automatically could possibly update libraries in the your environment because of a version reequirement. Flambé will output all libraries that are being updated.

Other flags

When executing flambé CLI passing a YAML file, this additional flags can be provided (among others):

--verbose / -v

All logs will be displayed in the console.

--force

Runnables may chose to accept this flag in its run() method to provide some overriding policies.

Experiments

The top level object in every configuration file must be a Runnable, the most common and useful being the Experiment class which facilitates executing a ML pipeline.

The Experiment’s most important parameter is the pipeline, where users can define a DAG of Components describing how dataset, models, training procedures, etc interact between them.

Attention

For a full specification of Experiment, see Experiment

The implementation of Experiment and its pipeline` uses Ray’s Tune under the hood.

Pipeline

A pipeline is defined as a list of Components that will be executed sequentiall. Each Component is identified by a key that can be used for later linking.

Let’s assume that we want to define an experiment that consists on:

1. Pick dataset A and preprocess it.
2. Train model A on dataset A.
3. Preprocess dataset B.
4. Finetune the model trained in 2. on dataset B.
5. Evaluate the fine tuned model on dataset A testset.

All these stages can be represented by a sequential pipeline in a simple and readable way:

pipeline:
    dataset_A: !SomeDataset
      ...

    model_A: !SomeModel
       ...

    trainer: !Trainer
       model: !@ model_A
       dataset: !@ dataset_A
       ...

    dataset_B: !Trainer
       ...

    fine_tunning: !Trainer
       model: !@ trainer.model
       dataset: !@ dataset_B
       ...

    eval: !Evaluator
       model: !@ trainer.model
       dataset: !@ dataset_A

Note how this represents a DAG where the nodes are the Components and the edges are the links to attributes of previouly defined Components.

Linking

As seen before in Quickstart, stagegs in the pipeline are connected using Links.

Links can be used anywhere in the pipeline to refer to earlier components or any of their attributes.

During the compilation that is described above in Delayed Initialization we actually resolve the links to their intended value, but cache the original link representation so that we can dump back to YAML with the original links later.

Search Options

Experiment supports declaring multiple variants in the pipeline by making use of the search tags:

!Experiment
...
pipeline:
    ...
    model: !TextClassifier
       ...
       n_layers: !g [2, 3, 4]

    ...

The value !g [2, 3, 4] indicates that each of the values should be tried. Flambé will create internally 3 variants of the model.

You can specify grid search options search for any parameter in your config, without changing your code to accept a new type of input! (in this case n_layers still receives an int )

Tip

You can also search over Components or even links:

!Experiment
...

pipeline:
  dataset: !SomeDataset
    transform:
      text: !g
      - !SomeTextField {{}}  # Double braces needed here
      - !SomeOtherTextField {{}}

Types of search options

!g

Previously shown. It grids over all its values

param: !g [1, 2, 3]  # grids over 1, 2 and 3.
param: !g [0.001, 0.01]  # grids over 0.001 and 0.01

!s

Yields k values from a range (low, high). If both low and high are int values, then !s will yield int values. Otherwise, it will yield float values.

param: !s [1, 10, 5]  # yiels 5 int values from 1 to 10
param: !s [1.5, 2.2, 5]  # yiels 5 float values from 1.5 to 2.2
param: !s [1.5, 2.2, 5, 2]  # yiels 5 float values from 1.5 to 2.2, rounded to 2 decimals

Combining Search tags

Search over different attributes at the same time will have a combinatorial effect.

For example:

!Experiment
...
pipeline:
    ...
    model: !TextClassifier
       ...
       n_layers: !g [2, 3, 4]
       hidden_size: !g [128, 256]

This will produce 6 variants (3 n_layers values times 2 hidden_size values)

Variants inheritance

Attention

Any object that links to an attribute of an object that describes multiple variants will inherit those variants.

!Experiment
...
pipeline:
    ...
    model: !TextClassifier
       n_layers: !g [2, 3, 4]
       hidden_size: !g [128, 256]
       ...
    trainer: !Trainer
       model: !@ model
       lr: !g [0.01, 0.001]
       ...

    evaluator: !Evaluator
       model: !@ trainer.model

The trainer will have 12 variants (6 from model times 2 for the lr). eval will run for 12 variants as it links to trainer.

Reducing

Experiment provides a reduce mechanism so that variants don’t flow down the pipeline. reduce is declared at the Experiment level and it can specify the number of variants to reduce to for each Component.

!Experiment
...
pipeline:
    ...
    model: !TextClassifier
       n_layers: !g [2, 3, 4]
       hidden_size: !g [128, 256]
    trainer: !Trainer
       model: !@ model
       lr: !g [0.01, 0.001]

    evaluator: !Evaluator
       ...
       model: !@ trainer.model

 reduce:
   trainer: 2

Flambé will then pick the best 2 variants before finishing executing ``trainer``. This means eval will receive the best 2 variants only.

Resources (Additional Files and Folders)

The resources argument lets users specify files that can be used in the Experiment (usually local datasets, embeddings or other files). In this section, you can put your resources under local or remote.

Local resources: The local section must include all local files.

Remote resources: The remote section must contain all files that are going to be located in the instances and must not be uploaded. This feature is only useful when running remotely (read Clusters)

!Experiment
...

resources:
    local:
        data: path/to/train.csv
        embeddings: path/to/embeddings.txt
    remote:
        remote_embeddings: /file/in/instance/
    ...

Attention

The remote section is only useful in remote experiments. If the user is running local experiments, then only the local section should be used.

resources can be referenced in the pipeline via linking:

!Experiment
...

resources:
    ...
    local:
        embeddings: path/to/embeddings.txt

pipeline:
    ...
      some_field: !@ embeddings

Scheduling and Reducing Strategies

When running a search over hyperparameters, you may want to run a more sophisticated scheduler. Using Tune, you can already use algorithms such as HyperBand, and soon more complex search algorithms like HyperOpt will be available.

schedulers:
    b1: !tune.HyperBandScheduler

pipeline:
    b0: !ext.TCProcessor
        dataset: !ext.SSTDataset
    b1: !Trainer
        train_sampler: !BatchSampler
            data: !@ b0.train
            batch_size: !g [32, 64, 128]
        model: ...
    b2: !Evaluator
        model: !@ b1.model

General Logging

We adopted the standard library’s logging module for logging:

import logging
logger = logging.getLogger(__name__)
...
logger.info("Some info here")
...
logger.error("Something went wrong here...")

The best part of the logging paradigm is that you can instantly start logging in any file in your code without passing any data or arguments through your object hierarchy.

Important

By default, only log statements at or above the INFO log level will be shown in the console. The rest of the logs will be saved in ~/.flambe/logs (more on this in Debugging)

In order to show all logs in the console, you can use the --vebose flag when running flambé:

flambe my_config_file.yaml --verbose

Tensorboard Logging

Flambé provides full integration with Tensorboard. Users can easily have data routed to Tensorboard through the logging interface:

from flambe import log
...
loss = ... # some calculation here
log('train loss', loss, step)

Where the first parameter is the tag which Tensorboard uses to name the value. The logging system will automatically detect the type and make sure it goes to the right Tensorboard function. See flambe.logging.log() in the package reference.

Flambé provides also logging special types of data:

• flambe.logging.log_image() for images
• flambe.logging.log_histogram() for distributions and histograms
• flambe.logging.log_pr_curves() for displaying PR curves
• flambe.logging.log_text() for displaying text

See the logging for more information on how to use this logging methods.

Script Usage

If you’re using the flambe.learn.Script object to wrap an existing piece of code with a command-line based interface, all of the logging information above still applies to you!

See more on Scripts in Converting a script to Flambé.

Checkpointint and Saving

As Quickstart explains, flambé saves an Experiment in a hierarchical way so that Components can be accessed independant to each other. Specifically, our save files are a directory by default, and include information about the class name, version, source code, and YAML config, in addition to the state that PyTorch normally saves, and any custom state that the implementer of the class may have included.

For example, if you initialize and use the following object as a part of your Experiment:

!TextClassifier
embedder: !Embedder
  embedding: !torch.Embedding
    input_size: !@ b0.text.vocab_size
    embedding_size: 300
  encoder: !PooledRNNEncoder
    input_size: 300
    rnn_type: lstm
    n_layers: 2
    hidden_size: 256
output_layer: !SoftmaxLayer
  input_size: !@ b1.model.encoder.encoder.rnn.hidden_size
  output_size: !@ b0.label.vocab_size

Then the save directory would look like the following:

save_path
├── state.pt
├── config.yaml
├── version.txt
├── source.py
├── embedder
│   ├── state.pt
│   ├── config.yaml
│   ├── version.txt
│   ├── source.py
│   ├── embedding
│   │   ├── state.pt
│   │   ├── config.yaml
│   │   ├── version.txt
│   │   └── source.py
│   └── encoder
│       ├── state.pt
│       ├── config.yaml
│       ├── version.txt
│       └── source.py
└── output_layer
    ├── state.pt
    ├── config.yaml
    ├── version.txt
    └── source.py

Note that each subdirectory is self-contained: if it’s possible to load that object on its own, you can load from just that subdirectory.

Important

As seen before, each variant of a Component will have it’s separate output folder.

Note

Flambé will save in this format automatically after each Component of the pipeline executes run(). As there are objects that execute run() multiple times (for example, Trainer), each time the state will be overriden by the latest one (checkpointing).

Resuming

Experiment has a way of resuming perviously run experiments:

!Experiment
resume: trainer
...
pipeline:
    ...
    model: !TextClassifier
       ...
       n_layers: !g [2, 3, 4]
       hidden_size: !g [128, 256]

    trainer: !Trainer
       ...
       model: !@ model
       lr: !g [0.01, 0.001]

    other_trainer: !Trainer
       ...
       model: !@ trainer.model

By providing a Component keyname (or a list of them) that belong to the pipeline, then flambé will resume all blocks up until the given one (or ones).

Debugging

Experiment has a debugging option that is only available in local executions (not remotely). This is activated by adding debug: True at the top level of the YAML.

When debugging is on, a debugger will appear before executing run on each Component.

Warning

Debugging is not enabled when running remote experiments.

Adding Custom State

Users can add other data to the state that is saved in the save directory. If you just want to have some additional instance attributes added, you can register them at the end of the __init__ method:

class MyModel(flambe.nn.Module):

    def __init__(self, x, ...):
        super().__init__(...)
        ...
        self.x = x,
        self.y = None
        self.register_attrs('x', 'y')

This will cause the get_state method to start including x and y in the state dict for instances of MyModel, and when you load state into instances of MyModel it will know to update these attributes.

If you want more flexibility to manipulate the state_dict or add computed properties you can override the _state() and _load_state() methods.

Report Site

The report site is a control center website where you are able to:

• View the progress of the experiment
• See live logs
• Download all models [remote]
• Access tensorboard [remote]

Attention

[remote] means that these features are only available in remote experiments.

In local experiments they are not needed.

Flambé’s report site comes included in the Flambé package. No further setup is required.

Important

The Report Site is currently compatible with Experiment.

How to launch the report site?

Local experiments

When running local experiments, the console output will give you the exact command to run in order to run the report site with the appropriate parameters.

Remote experiments

When running remote experiments, the report site will automatically start and you can find the URL in the console output. Remember that it may take a few moments for everything to start up particularly in a remote experiment.

Important

Users are responsible for making ports 49586 (Report Site) and 49556 (Tensorboard)

Screenshots

The Report Site displays a real time DAG of the experiment’s pipeline.

You can access more information by clicking on a block of the DAG.

Hint

The Report Site provides links to Tensorboard with the correct filters already applied. For example, you can access Tensorboard only for the train block or even for a specific variant.

Once the Experiment is over, you should see that all blocks are green. In addition, you will be able to download the artifacts (or just the logs).

Download can be also done at block or variant level.

The Report Site gives you full integration with Tensorboard:

And it also includes a live console for debugging.

Extensions

Flambé comes with many built-in Component and Runnable implementations. (for example Experiment, Trainer, TabularDataset and BaseSampler). However, users will almost always need to write new code and include that in their executions. This is done via our “extensions” mechanism.

Tip

At this point you should be familiar with the concepts of Components and Runnables.

Important

The same “extensions” mechanism serves for importing custom Components and Runnables.

Building Extensions

An extension is a pip installable package that contains valid Flambé objects as top level imports. These objects could be Runnables or Components. See here for more instructions on Python packages ready to be pip-installed.

Let’s assume we want to define a custom trainer called MyCustomTrainer that has a special behavior not implemented by the base Trainer. The extension could have the following structure:

extension
├── setup.py
└── my_ext
    ├── my_trainer.py  # Here lives the definition of MyCustomTrainer
    └── __init__.py

extension/setup.py

from setuptools import setup, find_packages

setup(
    name='my_extension-pkg-name',
    version='1.0.0',
    packages=find_packages(),  # This will install my_ext package
    install_requires=['extra_dependency==1.2.3'],
)

extension/my_ext/__init__.py

from my_ext.my_trainer import MyCustomTrainer

__all__ = ['MyCustomTrainer']

extension/my_ext/my_trainer.py

from flambe.learn import Trainer

class MyCustomTrainer(Trainer):

    ...

    def run(self):
          # Do something special here

Attention

If the extension was correctly built you should be able to pip install it and execute from my_ext import import MyCustomTrainer, which means that this object is at the top level import.

Using Extensions

You are able to use any extension in any YAML config by specifying it in the extensions section which precedes the rest of the YAML:

my_extension: /path/to/extension
---
!Experiment
... # use my_extension.MyCustomTrainer and other objects here

Each extension is declared using a key: value format.

Important

The key should be the top-level module name (not the package name).

The value can be:

• a local path pointing to the extension’s folder (like in the above example)
• a remote GitHub repo folder URLs.
• a PyPI package (alongside its version)

For example:

my_extension: /path/to/extension
my_other_extension: https://github.com/user/my_other_extension
another_extension: py-extensions==1.0
---
!Experiment

... # use my_extension.MyCustomTrainer and other objects here

Once an extension was added to the extensions section, all the extension’s objects become available using the module name as a prefix:

my_extension: /path/to/extension
my_other_extension: https://github.com/user/my_other_extension
---
pipeline:
    ...

    some_stage: !my_extension.MyCustomTrainer
       ...

    other_stage: !my_other_extension.AnotherCustomObject
       ...

Important

Remember to use the module name as a prefix

Hint

We support branches in GitHub extension repositories! Just use https://github.com/user/repo/tree/<BRANCH_NAME>/path/to/extension.

Tip

Using extensions is similar to Python import statements. At the top of the file, you declare the non-builtin structures that you wish to use later.

Python	Flambe YAML
from my_extension import MyCustomTrainer ... MyCustomTrainer(...)	my_extension: /path/to/extensions --- ... !my_extension.MyCustomTrainer ...

Clusters

Flambé supports running remote Runnables where jobs can be distributed across a cluster of workers.

Overall remote architecture

Flambé will create the following cluster when running a Cluster:

Orchestrator

The Orchestrator is the main machine in the cluster. The Orchestrator might host websites, run docker containers, etc. It can also collect artifacts like checkpoints or logs.

Attention

This machine doesn’t need to contain a GPU as it does not perform heavy computations.

Factories

The factories are instances that are capable of doing heavy computational work and likely need to have GPU resources (for example, if you’re running an Experiment with PyTorch and CUDA).

Important

Orchestrator and Factories have private SSH connection with a pair of keys that are create and distributed specially for the specific Cluster. More information about this here.

Launching a cluster

Cluster is a special type of Runnable implementation that handles clusters of machines (e.g. AWS instances) that are capable of running distributed jobs. As with any Runnable, you can run a cluster by executing flambé with the YAML config as an argument:

flambe cluster.yaml

Attention

Cluster is an abstract class because it depends on the cloud service provider, so users will need to use one of the provided implementations or create a custom one by overriding the abstract methods.

Important

We currently provide a full cluster implementation for AWS; see Using AWS

Setting the cluster up

All implementations of Cluster support setting setup_cmds, which are a list of bash commands that will run on all instances after creating the cluster:

!XXXCluster

name: my_cluster

...

setup_cmds:
  - sshfs user@host:/path/to/remote /path/to/local/mount/point  # Mount a remote filesystem
  - pip config set index_url https://my-custom-pypi.com  # Configure PyPI

Note that all commands will run sequentially in all the hosts of the cluster.

Tip

This could be useful for mounting volumes, configuring tools or install binaries.

Attention

If you need more complex setup you can also create your own base images for the hosts. AWSCluster supports specifying AMIs for both the Orchestrator and factories.

Submitting Jobs to a Cluster

A cluster is able to run any ClusterRunnable implementation, for example Experiment (more information ` in Cluster Runnables).

Given an experiment.yaml config file, running it remotely is as easy as:

flambe experiment.yaml --cluster cluster.yaml [--force]

Flambé will take care of preparing the cluster to run the ClusterRunnable (in this case an Experiment).

Important

--force option is necessary when an existing execution is taking place in the same cluster and the user wants to override it.

Important

There is no need to run flambe cluster.yaml before running a ClusterRunnable in it. If it’s the first time using the cluster, flambé will create it for you!

Using AWS

We provide full AWS integration using the AWSCluster implementation. When using this cluster, flambé will take care of:

• Building the cluster
• Preparing all instances (e.g. installing the version of flambé that matches what the user has locally)
• Automatically shutting the cluster down (if specified)

How to use AWSCluster?

A AWSCluster is like any flambé Runnable and therefore it can be specified in a YAML format:

!AWSCluster

name: name-of-the-cluster # Pick a unique identifier for the cluster

factories_num: 1  # The amount of factories

factories_type: g3.4xlarge  # The type of factories. GPU instances are recommended.
orchestrator_type: t3.large # The type of the orchestrator (GPU is not necessary).

orchestrator_timeout: -1  # # -1 means the orchestrator will have to be killed manually (recommended)
factories_timeout: -1 # Factories timeout after being unused for these many hours

creator: user@company
key_name: aws-key-name

tags:  # Extra tags to add to all instances
    company: my-company

key: /path/to/ssh/key

subnet_id: subnet-abcdef
volume_size: 100. # GBs of disk space for all instances

security_group: sg-0987654321

For a full description, see flambe.cluster.AWSCluster.

Automatic shutdown

This AWSCluster implementation provides a way of automatically shutting down all instances that have been created:

!AWSCluster

# rest of manager config

orchestrator_timeout: 5
factories_timeout: 0

These parameters specify how many hours the resources will persist with low CPU consumption.

In the above example, the Orchestrator will be terminated after 5 hours of low CPU usage. The Factories will be terminated as soon as CPU usage goes down.

Use -1 to keep the resources alive permanently, or until you manually stop them.

See also

For a full example of a configuration file for a Cluster, go here.

Intelligent versioning

When running ClusterRunnables remotely, the correct version of Flambé will be installed automatically, i.e. the version being used locally. For example, if the user has flambe==1.2 installed locally, then all instances (orchestrator and factories) will be using version 1.2!

Attention

This is also valid in developer mode. More on developer mode in Debugging.

Cluster Runnables

A ClusterRunnable is a special implementation of a Runnable that is able to execute on a flambé cluster.

The Experiment object, for example, is a ClusterRunnable.

Users are able to create custom ClusterRunnables by implementing its interface (which extends from the Runnable interface as well).

This new interface requires an additional implementation for the setup() method:

from flambe.runnable import ClusterRunnable

class MyClusterRunnable(ClusterRunnable):

   def setup(self, cluster: Cluster,
             extensions: Dict[str, str],
             force: bool, **kwargs) -> None:
          # code to setup the cluster

The setup() method should prepare the cluster (which is received as a parameter) to run the Runnable remotely. This usually involves creating folders, downloading resources, running docker containers, etc.

Important

All Cluster implementations provides basic functionality that allow directory creation, running bash commands, rsyncing folders, running docker containers and much more. See its documentation for more information about this.

Hint

It’s highly likely that you will need to change some instance attributes in the object in the setup() method. For doing this, you should use set_serializable_attr() to ensure that the attribute change is serializable.

How to run a ClusterRunnable

For running a ClusterRunnable remotely, you will need to provide a cluster configuration:

flambe cluster_runnable.yaml --cluster cluster.yaml

Because of being Runnable, it can still be executed locally:

flambe cluster_runnable.yaml

Remote Experiments

Users can get the most of performance by running Experiments in a Cluster.

In remote Experiments, a ray cluster will be created connecting all instances in the cluster. The Orchestrator will host Tensorboard and the Report Site (the URL will be provided in the console) and the Factories will do the heavy work executing the pipeline.

Additionally, when running remote Experiments, flambé will take care of uploading the local resources that were specified, making them available to all instances.

Builders

A Builder is a simple Runnable that can be used to create any Component post- Experiment, and export it to a local or remote location.

Builders decouple the inference logic with the experimentation logic, allowing users to iterate through inference contracts independantly without the need of rerunning an Experiment.

Hint

A Builder should be used to build inference engines that rely on previous experiments’ artifacts.

Motivation

Let’s assume that a user wants to train a binary classifier using an Experiment:

ext: /path/to/my/extensions
---
!Experiment
..
pipeline:
    ...
    model: !ext.MyBinaryClassifier

Now, the user needs to implement an inference object ClassifierEngine that has a method predict that performs the forward pass on the trained model:

from flambe.nn import Module
from flambe.compile import Component

class ClassifierEngine(Component):

   def __init__(self, model: Module):
      self.model = model

   def predict(self, **kwargs):
      # Custom code (for example, requests to APIs)
      p = self.model(feature)
      return {"POSITIVE": p, "NEGATIVE": 1-p}

By implementing Component, the user can use a Builder to build this object:

ext: /path/to/my/extensions
---
!Builder

storage: s3
destination: my-bucket

..
component: !ClassifierEngine
    ...
    model: !ext.MyBinaryClassifier.load_from_path:
      path: /path/to/saved/modeel

The inference object will be saved in s3://my-bucket. Then the user can:

import flambe

inference_engine = flambe.load("s3://my-bucket")
inference_engine.predict(...)
# >> {"POSITIVE": 0.9, "NEGATIVE": 0.1}

Important

Note that the inference logic is decoupled from the Experiment. If in the future the inference logic changes, there is no need of rerunning it.

Note

Why not just implement a plain Python class and use flambe.compile.serialization.load() to get the model? Because of being a Component, this object will have all the features Component has (YAML serialization, versioning, compatibility with other Runnable implementations, among others).

How to use a builder

Usage is really simple. The most important parameters for a Builder are the Component and the destination:

!Builder

storage: [ local | s3 ]
destination: path/to/location

..
component: !MyComponent
    params1: value1
    params2: value2
    ...
    paramsN: valueN

Important

For a full list of parameters, go to Builder.

Hint

If storage is “s3”, then the destination can be an S3 bucket folder. Flambé will take care of uploading the built artifacts.

Future Work

The goal is to develop builders for different technologies. For example, a DockerBuilder that is able to build a Docker container based on a Component.

Security

When creating clusters, sending information to instances or even pulling extensions from GitHub, flambé needs to deal with user’s protected data.

Important

Flambé will always rely on the default local configuration users have for all services flambé uses. This means that when using services like github or AWS, flambé will rely on the standard authentication mechanisms each service requires.

There is no need for users to configure special auth mechanisms for flambé.

Secrets

As explained in Providing secrets, users have the posibility of providing secrets information to the Runnable objects that will be executed.

This is done via an ini file. For example:

[SERVICE]
SECRET_TOKEN = ABCDEFGHI123456789


[OTHER]
PASSWORD = 0987654321

When calling:

flambe runnable.yaml --secrets secrets.ini [--cluster cluster.yaml]

Then the Runnable will have access to the secrets through its attribute config.

Clusters

Some important items related to Security when dealing with clusters:

• Flambé will use SSH for all communications with the instances. It will use only the key specified in the config.
• All resources that need to be uploaded to the instances are done via rsync with the given key.
• A copy of the secrets file will be sent to all instances using rsync with the given key.
• The key provided in the config is never uploaded to the instances.
• When loading the cluster, flambé will distribute a special key pair (created exclusively for the cluster) to all instances. This key will be used for internal communication (ie the communication betweeen the instances)

Important

All flow between the local process and the instances is done in a secure way using SSH protocols.

Attention

Flambé won’t configure any instances security policies (eg firewalls, security groups, etc). The user is responsible for configuring this to ensure clusters work correctly. This involves:

• Allowing private communication between the hosts created in the same subnet.
• Opening port 22 for SSH connection in all hosts.
• Opening ports 49556 and 49558 for the Report Site (in case of running an Experiment).

AWS

When using AWSCluster for creating an AWS EC2 cluster, flambé will rely on the local configuration for authentication (it uses boto3 under the hood). Users are going to be able to create clusters as long as they follow the AWS standards for storing credentials/tokens (for example, having ~/.aws/credentials file or having AWS_* environment variables defined).

Extensions

As explained in Automatic extensions installation, flambé will install the extensions when -i is specified. For all extensions that are git based URLs (from GitHub or BitBucket for example), then flambé will try to clone/pull them using the local configuration. This means that if for example a user wants to use an extensions from its private GitHub account, then it needs to have local configuration that allows pulling from this GitHub account. Flambé will not provide any special SSH keys to authenticate with these services.

Hint

git URLs for extensions support both HTTPS/SSH protocols:

extensions: ssh://git@github.com:user/repo.git
other_extension: https://github.com/user/repo/tree/my_branch/extensions
---
!Runnable
...

Advanced

Developer Mode

By using pip install -e ., you enable the developer mode, which allows you to use Flambé in editable mode.

By installing in developer mode (see starting-install-dev_label) Flambé will automatically use the current code in your local copy of the repo that you installed, including remote experiments.

Cache Git-based Extensions

As explained in Automatic extensions installation, flambé will install the extensions when -i is specified.

For all extensions that are git based URLs (from GitHub or BitBucket for example), flambé will clone the repositories into ~/.flambe/extensions/ folder the first time those extensions are being used. After this, every time one of those extensions is being used flambé will pull instead of cloning again.

This allows Runnables to install extensions much faster in case they are heavy sized git repos.

Attention

Flambé will warn once the size of ~/.flambe/extensions/ get bigger than 100MB.

Debugging

Flambé will save all logs in ~/.flambe/logs folder using a rotating mechanism.

Hint

The latest logs will be available in ~/.flambe/logs/log.log.

Custom YAML Tags

Aliases

Sometimes the best name for a class isn’t the best or most convenient name to use in a YAML config file. We provide an alias() class decorator that can give your class alternative aliases for use in the config.

Usage

from flambe.compile import alias

@alias('cool_tag')
class MyClass(...):
    ...

Then start using your class as !cool_tag instead of MyClass in the config. Both options will still work though. This combines seemlessly with extensions namespaces; if your extension’s module name is “ext” then the new alias will be !ext.cool_tag.

Registrables

While you will normally subclass Component to use some class in a YAML configuration file, there may be situations where you don’t want all the functionality described in :ref:’understanding-component_label’ such as delayed initialization, and recursive compilation. For these situations you can instead subclass the Registrable class which only defines the necessary functionality for loading and dumping into YAML. You will have to implement your own from_yaml() and to_yaml() methods.

Example

Let’s say you want to create a new wrapper class around an integer that tracks its name and a history of its values. First you would have to write your class

from flambe.compile import Registrable

class SmartInt(Registrable):

    def __init__(self, name: str, initial_value: int):
        self.name = name
        self.initial_value = initial_value  # For dumping later
        self.val = initial_value

    ...  # Rest of implementation here

Then you’ll want to implement your from_yaml and to_yaml in a way that makes sense to you. Here, let’s say the name and initial value should be separated by a dash character:

@classmethod
def to_yaml(cls, representer: Any, node: Any, tag: str) -> Any:
    str_rep = f"{self.name}-{self.val}"
    representer.represent_str(tag, str_rep)

@classmethod
def from_yaml(cls, constructor: Any, node: Any, factory_name: str) -> Any:
    str_rep = constructor.construct_str(node)
    name, initial_value = str_rep.split()
    return cls(name, initial_value)

Finally you can now use your new Registrable object in YAML.

!Experiment
...
pipeline:
  stage_0: !Trainer
    param: !SmartInt my_param-9

Attention

You will need to make sure your code is part of an extension so that Flambé knows about your new class. See Extensions

See also

The official ruamel.yaml documentation for information about from_yaml and to_yaml

See also

MappedRegistrable can be referenced as another example or used if you just want a basic Registrable that can load from a dictionary of kwargs but doesn’t have the other features of Component like delayed initialization

Converting a script to Flambé

In many situations, you may already have a script that performs your full training routine, and you would like to avoid doing any intergation work to leverage some of the tools in Flambé, namely launching variants of the script on a large cluster, and using the Flambé logging system.

For this particular use case, Flambé offers a Script component, which takes as input an executable module (your script), and the relevant arguments. The script should read arguments from sys.argv, which means that traditional scripts, and scripts that use tools such as argparse are supported.

Attention

The Script object only consists of a single step, and is therefore not compatible with checkpointing or trial schedulers such as Hyperband. It is however possible to use with hyperparameter search algorithms.

Wrapping your script in a pip installable

Say you have the following directory structure for your project:

my_project
├── model.py
├── processing.py
└── train.py

Where train.py is your target script which uses argparse to read arguments. The first step is to convert your project into a pip installable.

my_pip_installable
├── setup.py
└── my_project
    ├── __init__.py
    ├── model.py
    ├── processing.py
    └── train.py

Your setup should contain all of the external library requirements, used by your script. Once this is done, you should make an attempt at running your script using the -m argument, which will treat train.py as an executable module. You can do so by running:

python -m my_project.train --arg1 value1 --arg2 value2

Attention

You will need to modify the imports in your script to use either relative imports or import from the top level package. In this example, this can be done by replacing import model by import .model. Note that you only need to perform this change once and will still be able to run your script, normally, regardless of Flambé, using python -m.

Writing a config file

Once you have done the above step, you can use your script in Flambé as follows:

my_project: /path/to/my_pip_installable

---

!Experiment

pipeline:
    stage_0: !Script
        script: my_project.train  # my_project is the name of the module
        args:
            arg1: !g [1, 5]  # Run a grid search over any arguments to your script
            arg2: 'foo'

That’s it! You can now execute this configuration file, with the regular command:

In order to see tensorboard logs, simply import the logger, and use it anywhere in your script:

Using Custom Code in Flambé

While Flambé offers a large number of Component objects to use in experiments, researchers will typically need to use their own code, or modify one of our current component object.

Writing your custom code

Flambé configurations support any python object that inherits from Component. You can decide to inherit from one of our base classes such as Module or Dataset, or you can inherit from Component directly.

A Component must simply implement a run() method which returns a boolean indicating whether execution should continue or not (useful for multi-step components such as a Trainer).

Additionally, if you would like to run hyperparameter search on your custom component, you must implement the metric() method which returns the current best metric.

Setting up your extension

Flambé offers a simple mechanism to inject custom code in configs. To do so, your code must be wrapped in a pip installable. If you are not familiar with python packages, you can follow these 3 simple steps:

1. Make sure all your code is organised in a python module. In the example below, my_module is the name of the module containing our custom model and dataset objects.

my_module # The name of your module
├── model.py
├── dataset.py
...

2. Next, we wrap the module in another folder, representing our package.

my_package # This is the name of your package
└── my_module # This is the name of your module
    ├── __init__.py
    ├── model.py
    ├── dataset.py
    └── ...

3. Finally, we write our setup.py file, which will make our package installable. This file is crucial as it indicates the external dependencies of your custom code. Below is a template for your setup file.

from setuptools import setup, find_packages

setup(
    name = 'my_package', # the name of your package
    version = '1.0.0', # the version of your extension (optional)
    packages = find_packages(),
    install_requires = ['numpy >= 1.11.1', 'matplotlib >= 1.5.1'], # Dependencies here
)

After you add the setup file to your package, your final folder structure should look like the one below:

my_package
├── setup.py # This file makes your package installable
└── my_module
    ├── __init__.py
    ├── model.py
    ├── dataset.py
    └── ...

Using your extension

You have built your first extension! You can now use it freely in any configuration, whether that’d be for an Experiment, a Cluster or any other Runnable.

To do so, simply add them at the top of your extension, mapping the name of the module your built (my_module in the example) to the location of the package (my_package in the example).

Important

The name of the module is used as a prefix in your configurations. Not the name of your package.

my_module: path/to/my_package  # Must map from module to package path

---  # Note the 3 dashes here

!Experiment

pipeline:
    dataset: !my_module.MyDataset # We use the name of your custom module as prefix

Tip

The path to the package may be a local path, a github URL, or the name of package one pypi. The latter allows you to specify a specific version of your extension. For github, we also support links to specific commit or branches.

Flambé will require your extension to be installed. You can do so manually by running:

pip install my_package

or Flambé can install all the extensions specified in your configuration automatically while executing your config when using the -i flag:

flambe -i config.yaml

Writing a multistage pipeline: BERT Fine-tuning + Distillation

It is common to want to train a model on a particular task, and reuse that model or part of the model in a fine-tuning stage on a different dataset. Flambé allows users to link directly to objects in a previous stage in the pipeline without having to run two different experiments (more information on linking here)

For this tutorial, we look at a recent use case in natural language processing, namely fine-tuning a BERT model on a text classification task, and applying knowledge distillation on that model in order to obtain a smaller model of high performance. Knowledge distillation is interesting as BERT is relativly slow, which can hinder its use in production systems.

First step: BERT fine-tuning

We start by taking a pretrained BERT encoder, and we fine-tune it on the SSTDataset by adding a linear output layer on top of the encoder. We start with the dataset, and apply a special TextField object which can load the pretrained vocabulary learned by BERT.

The SSTDataset below inherits from our TabularDataset component. This object takes as input a transform dictionary, where you can specify Field objects. A Field is considered a featurizer: it can take an arbitrary number of columns and return an any number of features.

Tip

You are free to completely override the Dataset object and not use Field, as long as you follow its interface: Dataset.

In this example, we apply a BERTTextField and a LabelField.

dataset: !SSTDataset
    transform:
        text: !BERTTextField.from_alias
            alias: 'bert-base-uncased'
            lower: true
        label: !LabelField

Tip

By default, fields are aligned with the input columns, but one can also make an explicit mapping if more than one feature should be created from the same column:

transform:
    text:
        columns: 0
        field: !BERTTextField.from_alias
            alias: 'bert-base-uncased'
            lower: true
    label:
        columns: 1
        field: !LabelField

Next we define our model. We use the TextClassifier object, which takes an Embedder, and an output layer:

teacher: !TextClassifier

  embedder: !Embedder
    embedding: !BERTEmbeddings.from_alias
      path: 'bert-base-uncased'
      embedding_freeze: True
    encoder: !BERTEncoder.from_alias
      path: 'bert-base-uncased'
      pool_last: True

  output_layer: !SoftmaxLayer
    input_size: !@ model.embedder.encoder.config.hidden_size
    output_size: !@ dataset.label.vocab_size  # We link the to size of the label space

Finally we put all of this in a Trainer object, which will execute training.

Tip

Any component can be specified at the top level in the pipeline or be an argument

to another Component objects. A Component has a run method which for many objects consists of just a pass statement, meaning that using them at the top level is equivalent to declaring them. The Trainer however executes training through its run method, and will therefore be both declared and executed.

finetune: !Trainer

dataset: !@ dataset train_sampler: !BaseSampler

batch_size: 16

val_sampler: !BaseSampler

batch_size: 16

model: !@ teacher loss_fn: !torch.NLLLoss metric_fn: !Accuracy optimizer: !AdamW

params: !@ finetune.model.trainable_params lr: 0.00005

Second step: Knowledge distillation

We now introduce a second model, which we will call the student model:

student: !TextClassifier

  embedder: !Embedder
    embedding: !BERTEmbeddings.from_alias
      path: 'bert-base-uncased'
      embedding_freeze: True
    encoder: !PooledRNNEncoder
      rnn_type: sru
      n_layers: 2
      hidden_size: 256
      pooling: last
  output_layer: !SoftmaxLayer
    input_size: !@ student.embedder.encoder.hidden_size
    output_size: !@ dataset.label.vocab_size

Attention

Note how this new model is way less complex than the original layer, being more appropriate for productions systems.

In the above example, we decided to reuse the same embedding layer, which allows us not to have to provide a new Field to the dataset. However, you may also decide to perform different preprocessing for the student model:

dataset: !SSTDataset
    transform:
        teacher_text: !BERTTextField.from_alias
            alias: 'bert-base-uncased'
            lower: true
        label: !LabelField
        student_text: !TextField

We can now proceed to the final step of our pipeline which is the DistillationTrainer. The key here is to link to the teacher model that was obtained in the finetune stage above.

Tip

You can specify to the DistillationTrainer which columns of the dataset to pass to the teacher model, and which to pass to the student model through the teacher_columns and student_columns arguments.

distill: !DistillationTrainer
  dataset: !@ dataset
  train_sampler: !BaseSampler
    batch_size: 16
  val_sampler: !BaseSampler
    batch_size: 16
  teacher_model: !@ finetune.model
  student_model: !@ student
  loss_fn: !torch.NLLLoss
  metric_fn: !Accuracy
  optimizer: !torch.Adam
    params: !@ distill.student_model.trainable_params
    lr: 0.00005
  alpha_kl: 0.5
  temperature: 1

Attention

Linking to the teacher model directly would use the model pre-finetuning, so we link to the model inside the finetune stage. Note that for these links to work, it’s important for the Trainer object to have the model as instance attribute.

That’s it! You can find the full configuration below.

Full configuration

dataset: !SSTDataset
    transform:
        text: !BERTTextField.from_alias
            alias: 'bert-base-uncased'
            lower: true
        label: !LabelField

teacher: !TextClassifier
  embedder: !Embedder
    embedding: !BERTEmbeddings.from_alias
      path: 'bert-base-uncased'
      embedding_freeze: True
    encoder: !BERTEncoder.from_alias
      path: 'bert-base-uncased'
      pool_last: True
  output_layer: !SoftmaxLayer
    input_size: !@ model.embedder.encoder.config.hidden_size
    output_size: !@ dataset.label.vocab_size  # We link the to size of the label space

student: !TextClassifier
  embedder: !Embedder
    embedding: !BERTEmbeddings.from_alias
      path: 'bert-base-uncased'
      embedding_freeze: True
    encoder: !PooledRNNEncoder
      rnn_type: sru
      n_layers: 2
      hidden_size: 256
      pooling: last
  output_layer: !SoftmaxLayer
    input_size: !@ student.embedder.encoder.hidden_size
    output_size: !@ dataset.label.vocab_size

finetune: !Trainer
  dataset: !@ dataset
  train_sampler: !BaseSampler
    batch_size: 16
  val_sampler: !BaseSampler
    batch_size: 16
  model: !@ teacher
  loss_fn: !torch.NLLLoss
  metric_fn: !Accuracy
  optimizer: !AdamW
    params: !@ finetune.model.trainable_params
    lr: 0.00005

distill: !DistillationTrainer
  dataset: !@ dataset
  train_sampler: !BaseSampler
    batch_size: 16
  val_sampler: !BaseSampler
    batch_size: 16
  teacher_model: !@ finetune.model
  student_model: !@ student
  loss_fn: !torch.NLLLoss
  metric_fn: !Accuracy
  optimizer: !torch.Adam
    params: !@ distill.student_model.trainable_params
    lr: 0.00005
  alpha_kl: 0.5
  temperature: 1

Creating a cluster with existing instances

Flambé provides a Cluster implementation called SSHCluster that is able to build a cluster from existing instances.

Important

As described in Clusters, all clusters have an orchestrator host and a set of factories hosts.

Instances in a cloud service provider

Let’s assume that the user contains the following cluster:

Tip

It’s not required that the factories contain GPU.

Important

It is required that:

• All instances are in same private LAN.
• All host have the same username.
• All host are accessible with the same private key.

Implementing an SSHCluster is as simple as:

ssh-cluster.yaml

!SSHCluster

name: my-cluster

orchestrator_ip: [53.10.21.32, 10.150.0.1]
factories_ips:
  - [53.10.21.54, 10.150.0.2]
  - [53.10.21.73, 10.150.0.3]

key: /path/to/my/key

username: ubuntu

Note that all hosts have information about both the public IP and the private IP.

Instances in the private LAN

If the instances do not have a public IP because they are running on-premise, then SSHCluster supports providing private IPs only.

For example:

ssh-cluster.yaml

!SSHCluster

name: my-cluster

orchestrator_ip: 10.150.0.10
factories_ips:
  - 10.150.0.20
  - 10.150.0.30

key: /path/to/my/key

username: ubuntu

More information

Refer to the Clusters section or checkout the documentation of SSHCluster.

Creating a cluster using Amazon Web Services (AWS)

AWSCluster is a Cluster implementation that uses AWS as the cloud service provider.

This tutorial will guide you step by step to create your first AWS-based cluster in flambé.

Setting up your AWS account

Important

If you are already familiar with AWS main concepts (Subnets, type of instances, security groups, etc) and you have your AWS account set up, then feel free to skip this section. Consider that your Account should be able to:

• Have a key pair to access instances.
• Create instances with automatic public IPs.
• Connect through SSH from the outside world.
• Have the security credentials and configuration files locally.

If any of this requirements is not met, please review the following steps.

You will first need to create your AWS account here. Once done, go into the console (https://console.aws.amazon.com). You should see something like:

Attention

AWS provides a free tier. If users use this option, the timeout feature may not be available and only basic CPU instances are going to be available.

Create key-pair

Important

If you already have a key pair feel free to ignore this section.

A key pair will be used to communicate with the instances.

In order to create a Key Pair, go to the Services -> EC2:

On the left side list, go to Key Pairs:

Create a key pair and notice that a .pem file will be downloaded:

Important

Pick a recognazible name because you will use it later.

Important

Save your .pem file in a safe location as AWS will not give you access again to the file.

Warning

Set the right permissions to the pem file so only the root user can read it:

chmod 400 /path/to/my-pair.pem

Create security credentials

Important

If you already have security credentials, feel free to skip this section.

Security credentials are a way of authentication additionally to user/password information. For more information about this, go here

In order to create the Security Credentials, go to the right top section that contains your name. Press on My Security Credentials:

Go to Access Keys and click Create New Access Key.

When creating them, you should see something like:

Important

Download the file and make sure you save it in a safe location. Note that you won’t be able to access this information again from the console.

Basic local configuration

Having access now to your AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY, you will need to configure 2 configuration files:

Tip

This is an initial and basic configuration. More information here.

Important

At this point, you should have full access to AWS from your local computer through the Security Credentials. This snippet should run without raising errors:

import boto3
sess = boto3.Session()
sess.client("ec2").describe_instances()  # This may return no content if you have no instances

Create VPC and Subnet

You will need to create a VPC and a Subnet where your instances will be running.

Tip

For more information about these topics, go here

1: Create VPC

In order to create a VPC, go to Services -> VPC. On the left side, go to VPC:

Click on Create VPC and choose some values. For example:

2: Create Subnet

In order to create a Subnet, go to Services -> VPC. On the left side, go to Subnet:

Click on Create Subnet and choose some values. Make sure to reference the VPC you just created:

3: Enable auto-assign public IPs

This feature allows AWS to automatically assign public IPs to hosts that are created.

Important

This feature needs to be enabled for flambé.

First, go into your VPC section and select the VPC you created in the first step. select Actions -> Edit DNS Hostnames:

Check on enable and click Save.

After that, go to your Subnet section and select the Subnet you created in step 2. select Actions -> Modify auto-assign IP settings:

Enable the feature and click Save.

3: Configure Internet Gateways and Routes

Go to Services -> VPC and choose Internet Gateways. Verify that there is an internet gateway attached to your VPC. Otherwise, choose Create Internet Gateway:

After creating the internet gateway, go to Actions -> Attach to VPC. Follow the instructions to attach it to the created VPC:

Finally, go to Subnet section and select your Subnet. On the Route Table tab, verify that there is a route with 0.0.0.0/0 as the destination and the internet gateway for your VPC as the target.

Otherwise, choose the ID of the route table (rtb-xxxxxxxx) to navigate to the Route Table. On the Routes tab, choose Edit routes. Choose Add route, use 0.0.0.0/0 as the destination and the internet gateway as the target. Choose Save routes.

Create Security Group (SG)

Security groups define security policies for the instaces. For more information go here

In order to create a SG, go to Services -> EC2. Click Security Groups on the left panel and then Create Security Group .

Important

The SG must have at least SSH access using standard port 22.

Tip

The above image shows the SG allows ssh traffic from 0.0.0.0/0 (which means from everywhere). If you are under static public IP or VPN, you can make more secure rules.

Important

If this cluster will be running remote Experiment, you may also want to open HTTP ports 49556 and 49558 for the Report Site and Tensorboard.

Creating a AWSCluster

At this point you should be ready to create your AWSCluster. You will need:

• The name of the key pair
• The location of the pem file and make sure that it has only reading permissions for root.
• The appropriate Security Group’s ID
• The Subnet ID you wish all instances to live in.

Template:

aws-cluster.yaml

!AWSCluster

name: my-cluster

factories_num: 2

# Type of machines.
factories_type: t3.small
orchestrator_type: t3.small

# Set timeouts for autmatic shutdown
orchestrator_timeout: -1
factories_timeout: -1

creator: user@company.com  # Pick whatever you want here

# Name of my key pair
key_name: my-pair

# Specify you pem location
key: /path/to/my-pair.pem

# You can add additional tags. This is OPTIONAL.
tags:
    project: my-project
    company: my-company

# Specify the Subnet ID
subnet_id: subnet-XXXXXXXXXXXXXXX

# The amount of GB for each instance.
volume_size: 100

# Specify the SG ID
security_group: sg-XXXXXXXXXXXXXXX

Create the cluster by executing:

flambe aws-cluster.yaml

You should see something like:

If everything is successfull, you should see you instances in your EC2 console:

Reusing a AWSCluster

As long as the cluster name hasn’t change, you can reuse the same cluster. So if after creating a cluster like the previous one you execute again:

flambe aws-cluster.yaml

Then flambé will automatically detect an existing cluster and it will reuse it:

Tip

This is particularly useful when running Experiment objects in the cluster. You can run:

flambe experiment.yaml -c my-cluster.yaml
flambe other_experiment.yaml -c my-cluster.yaml

In the same cluster!

flambe.dataset

Submodules

flambe.dataset.dataset

Module Contents

class flambe.dataset.dataset.Dataset

Bases: flambe.Component

Base Dataset interface.

Dataset objects offer the main interface to loading data into the experiment pipepine. Dataset objects have three attributes: train, dev, and test, each pointing to a list of examples.

Note that Datasets should also be “immutable”, and as such, __setitem__ and __delitem__ will raise an error. Although this does not mean that the object will not be mutated in other ways, it should help avoid issues now and then.

train :Sequence[Sequence]

Returns the training data as a sequence of examples.

val :Sequence[Sequence]

Returns the validation data as a sequence of examples.

test :Sequence[Sequence]

Returns the test data as a sequence of examples.

__setitem__(self)

Raise an error.

__delitem__(self)

Raise an error.

flambe.dataset.tabular

Module Contents

class flambe.dataset.tabular.DataView(data: np.ndarray, transform_hooks: List[Tuple[Field, Union[int, List[int]]]], cache: bool)

TabularDataset view for the train, val or test split. This class must be used only internally in the TabularDataset class.

A DataView is a lazy Iterable that receives the operations from the TabularDataset object. When __getitem__ is called, then all the fields defined in the transform are applied.

This object can cache examples already transformed. To enable this, make sure to use this view under a Singleton pattern (there must only be one DataView per split in the TabularDataset).

raw

Returns an subscriptable version of the data

__getitem__(self, index)

Get an item from an index and apply the transformations dinamically.

is_empty(self)

Return if the DataView has data

cols(self)

Return the amount of columns the DataView has.

__len__(self)

Return the length of the dataview, ie the amount of examples it contains.

__setitem__(self)

Raise an error as DataViews are immutable.

__delitem__(self)

Raise an error as DataViews are immutable.

class flambe.dataset.tabular.TabularDataset(train: Iterable[Iterable], val: Optional[Iterable[Iterable]] = None, test: Optional[Iterable[Iterable]] = None, cache: bool = True, named_columns: Optional[List[str]] = None, transform: Dict[str, Union[Field, Dict]] = None)

Bases: flambe.dataset.Dataset

Loader for tabular data, usually in csv or tsv format.

A TabularDataset can represent any data that can be organized in a table. Internally, we store all information in a 2D numpy generic array. This object also behaves as a sequence over the whole dataset, chaining the training, validation and test data, in that order. This is useful in creating vocabularies or loading embeddings over the full datasets.

train

The list of training examples

Type:np.ndarray

val

The list of validation examples

Type:np.ndarray

test

The list of text examples

Type:np.ndarray

train :np.ndarray

Returns the training data as a numpy nd array

val :np.ndarray

Returns the validation data as a numpy nd array

test :np.ndarray

Returns the test data as a numpy nd array

raw :np.ndarray

Returns all partitions of the data as a numpy nd array

cols :int

Returns the amount of columns in the tabular dataset

_set_transforms(self, transform: Dict[str, Union[Field, Dict]])

Set transformations attributes and hooks to the data splits.

This method adds attributes for each field in the transform dict. It also adds hooks for the ‘process’ call in each field.

ATTENTION: This method works with the _train, _val and _test hidden attributes as this runs in the constructor and creates the hooks to be used in creating the properties.

classmethod from_path(cls, train_path: str, val_path: Optional[str] = None, test_path: Optional[str] = None, sep: Optional[str] = 't', header: Optional[str] = 'infer', columns: Optional[Union[List[str], List[int]]] = None, encoding: Optional[str] = 'utf-8', transform: Dict[str, Union[Field, Dict]] = None)

Load a TabularDataset from the given file paths.

Parameters:

• train_path (str) – The path to the train data
• val_path (str, optional) – The path to the optional validation data
• test_path (str, optional) – The path to the optional test data
• sep (str) – Separator to pass to the read_csv method
• header (Optional[Union[str, int]]) – Use 0 for first line, None for no headers, and ‘infer’ to detect it automatically, defaults to ‘infer’
• columns (List[str]) – List of columns to load, can be used to select a subset of columns, or change their order at loading time
• encoding (str) – The encoding format passed to the pandas reader
• transform (Dict[str, Union[Field, Dict]]) – The fields to be applied to the columns. Each field is identified with a name for easy linking.

classmethod autogen_val_test(cls, data_path: str, seed: Optional[int] = None, test_ratio: Optional[float] = 0.2, val_ratio: Optional[float] = 0.2, sep: Optional[str] = 't', header: Optional[str] = 'infer', columns: Optional[Union[List[str], List[int]]] = None, encoding: Optional[str] = 'utf-8', transform: Dict[str, Union[Field, Dict]] = None)

Generate a test and validation set from the given file paths, then load a TabularDataset.

Parameters:

• data_path (str) – The path to the data
• seed (Optional[int]) – Random seed to be used in test/val generation
• test_ratio (Optional[float]) – The ratio of the test dataset in relation to the whole dataset
• val_ratio (Optional[float]) – The ratio of the validation dataset in relation to the training dataset (whole - test)
• sep (str) – Separator to pass to the read_csv method
• header (Optional[Union[str, int]]) – Use 0 for first line, None for no headers, and ‘infer’ to detect it automatically, defaults to ‘infer’
• columns (List[str]) – List of columns to load, can be used to select a subset of columns, or change their order at loading time
• encoding (str) – The encoding format passed to the pandas reader
• transform (Dict[str, Union[Field, Dict]]) – The fields to be applied to the columns. Each field is identified with a name for easy linking.

classmethod _load_file(cls, path: str, sep: Optional[str] = 't', header: Optional[str] = 'infer', columns: Optional[Union[List[str], List[int]]] = None, encoding: Optional[str] = 'utf-8')

Load data from the given path.

The path may be either a single file or a directory. If it is a directory, each file is loaded according to the specified options and all the data is concatenated into a single list.

Parameters:

• path (str) – Path to data, could be a directory or a file
• sep (str) – Separator to pass to the read_csv method
• header (Optional[Union[str, int]]) – Use 0 for first line, None for no headers, and ‘infer’ to detect it automatically, defaults to ‘infer’
• columns (Optional[Union[List[str], List[int]]]) – List of columns to load, can be used to select a subset of columns, or change their order at loading time
• encoding (str) – The encoding format passed to the pandas reader

Returns:

A tuple containing the list of examples (where each example is itself also a list or tuple of entries in the dataset) and an optional list of named columns (one string for each column in the dataset)

Return type:

Tuple[List[Tuple], Optional[List[str]]]

__len__(self)

Get the length of the dataset.

__iter__(self)

Iterate through the dataset.

__getitem__(self, index)

Get the item at the given index.

Package Contents

class flambe.dataset.Dataset

Bases: flambe.Component

Base Dataset interface.

Dataset objects offer the main interface to loading data into the experiment pipepine. Dataset objects have three attributes: train, dev, and test, each pointing to a list of examples.

Note that Datasets should also be “immutable”, and as such, __setitem__ and __delitem__ will raise an error. Although this does not mean that the object will not be mutated in other ways, it should help avoid issues now and then.

train :Sequence[Sequence]

Returns the training data as a sequence of examples.

val :Sequence[Sequence]

Returns the validation data as a sequence of examples.

test :Sequence[Sequence]

Returns the test data as a sequence of examples.

__setitem__(self)

Raise an error.

__delitem__(self)

Raise an error.

class flambe.dataset.TabularDataset(train: Iterable[Iterable], val: Optional[Iterable[Iterable]] = None, test: Optional[Iterable[Iterable]] = None, cache: bool = True, named_columns: Optional[List[str]] = None, transform: Dict[str, Union[Field, Dict]] = None)

Bases: flambe.dataset.Dataset

Loader for tabular data, usually in csv or tsv format.

A TabularDataset can represent any data that can be organized in a table. Internally, we store all information in a 2D numpy generic array. This object also behaves as a sequence over the whole dataset, chaining the training, validation and test data, in that order. This is useful in creating vocabularies or loading embeddings over the full datasets.

train

The list of training examples

Type:np.ndarray

val

The list of validation examples

Type:np.ndarray

test

The list of text examples

Type:np.ndarray

train :np.ndarray

Returns the training data as a numpy nd array

val :np.ndarray

Returns the validation data as a numpy nd array

test :np.ndarray

Returns the test data as a numpy nd array

raw :np.ndarray

Returns all partitions of the data as a numpy nd array

cols :int

Returns the amount of columns in the tabular dataset

_set_transforms(self, transform: Dict[str, Union[Field, Dict]])

Set transformations attributes and hooks to the data splits.

This method adds attributes for each field in the transform dict. It also adds hooks for the ‘process’ call in each field.

ATTENTION: This method works with the _train, _val and _test hidden attributes as this runs in the constructor and creates the hooks to be used in creating the properties.

classmethod from_path(cls, train_path: str, val_path: Optional[str] = None, test_path: Optional[str] = None, sep: Optional[str] = 't', header: Optional[str] = 'infer', columns: Optional[Union[List[str], List[int]]] = None, encoding: Optional[str] = 'utf-8', transform: Dict[str, Union[Field, Dict]] = None)

Load a TabularDataset from the given file paths.

Parameters:

• train_path (str) – The path to the train data
• val_path (str, optional) – The path to the optional validation data
• test_path (str, optional) – The path to the optional test data
• sep (str) – Separator to pass to the read_csv method
• header (Optional[Union[str, int]]) – Use 0 for first line, None for no headers, and ‘infer’ to detect it automatically, defaults to ‘infer’
• columns (List[str]) – List of columns to load, can be used to select a subset of columns, or change their order at loading time
• encoding (str) – The encoding format passed to the pandas reader
• transform (Dict[str, Union[Field, Dict]]) – The fields to be applied to the columns. Each field is identified with a name for easy linking.

classmethod autogen_val_test(cls, data_path: str, seed: Optional[int] = None, test_ratio: Optional[float] = 0.2, val_ratio: Optional[float] = 0.2, sep: Optional[str] = 't', header: Optional[str] = 'infer', columns: Optional[Union[List[str], List[int]]] = None, encoding: Optional[str] = 'utf-8', transform: Dict[str, Union[Field, Dict]] = None)

Generate a test and validation set from the given file paths, then load a TabularDataset.

Parameters:

• data_path (str) – The path to the data
• seed (Optional[int]) – Random seed to be used in test/val generation
• test_ratio (Optional[float]) – The ratio of the test dataset in relation to the whole dataset
• val_ratio (Optional[float]) – The ratio of the validation dataset in relation to the training dataset (whole - test)
• sep (str) – Separator to pass to the read_csv method
• header (Optional[Union[str, int]]) – Use 0 for first line, None for no headers, and ‘infer’ to detect it automatically, defaults to ‘infer’
• columns (List[str]) – List of columns to load, can be used to select a subset of columns, or change their order at loading time
• encoding (str) – The encoding format passed to the pandas reader
• transform (Dict[str, Union[Field, Dict]]) – The fields to be applied to the columns. Each field is identified with a name for easy linking.

classmethod _load_file(cls, path: str, sep: Optional[str] = 't', header: Optional[str] = 'infer', columns: Optional[Union[List[str], List[int]]] = None, encoding: Optional[str] = 'utf-8')

Load data from the given path.

The path may be either a single file or a directory. If it is a directory, each file is loaded according to the specified options and all the data is concatenated into a single list.

Parameters:

• path (str) – Path to data, could be a directory or a file
• sep (str) – Separator to pass to the read_csv method
• header (Optional[Union[str, int]]) – Use 0 for first line, None for no headers, and ‘infer’ to detect it automatically, defaults to ‘infer’
• columns (Optional[Union[List[str], List[int]]]) – List of columns to load, can be used to select a subset of columns, or change their order at loading time
• encoding (str) – The encoding format passed to the pandas reader

Returns:

A tuple containing the list of examples (where each example is itself also a list or tuple of entries in the dataset) and an optional list of named columns (one string for each column in the dataset)

Return type:

Tuple[List[Tuple], Optional[List[str]]]

__len__(self)

Get the length of the dataset.

__iter__(self)

Iterate through the dataset.

__getitem__(self, index)

Get the item at the given index.

flambe.cluster

Subpackages

flambe.cluster.instance

Submodules

flambe.cluster.instance.errors

Module Contents

exception flambe.cluster.instance.errors.RemoteCommandError

Bases: Exception

Error raised when any remote command/script fail in an Instance.

exception flambe.cluster.instance.errors.SSHConnectingError

Bases: Exception

Error raised when opening a SSH connection fails.

exception flambe.cluster.instance.errors.MissingAuthError

Bases: Exception

Error raised when there is missing authentication information.

exception flambe.cluster.instance.errors.RemoteFileTransferError

Bases: Exception

Error raised when sending a local file to an Instance fails.

flambe.cluster.instance.instance

This modules includes base Instance classes to represent machines.

All Instance objects will be managed by Cluster objects (flambe.cluster.cluster.Cluster).

This base implementation is independant to the type of instance used.

Any new instance that flambe should support should inherit from the classes that are defined in this module.

Module Contents

flambe.cluster.instance.instance.logger

flambe.cluster.instance.instance.InsT

class flambe.cluster.instance.instance.Instance(host: str, private_host: str, username: str, key: str, config: ConfigParser, debug: bool, use_public: bool = True)

Bases: object

Encapsulates remote instances.

In this context, the instance is a running computer.

All instances used by flambe remote mode will inherit Intance. This class provides high-level methods to deal with remote instances (for example, sending a shell command over SSH).

Important: Instance objects should be pickeable. Make sure that all child classes can be pickled.

The flambe local process will communicate with the remote instances using SSH. The authentication mechanism will be using private keys.

Parameters:

• host (str) – The public DNS host of the remote machine.
• private_host (str) – The private DNS host of the remote machine.
• username (str) – The machine’s username.
• key (str) – The path to the ssh key used to communicate to the instance.
• config (ConfigParser) – The config object that contains useful information for the instance. For example, config[‘SSH’][‘SSH_KEY’] should contain the path of the ssh key to login the remote instance.
• debug (bool) – True in case flambe was installed in dev mode, False otherwise.
• use_public (bool) – Wether this instance should use public or private IP. By default, the public IP is used. Private host is used when inside a private LAN.

fix_relpaths_in_config(self)

Updates all paths to be absolute. For example, if it contains “~/a/b/c” it will be change to /home/user/a/b/c (the appropiate $HOME value)

__enter__(self)

Method to use Instance instances with context managers

Returns:The current instance Return type:Instance

__exit__(self, exc_type: Optional[Type[BaseException]], exc_value: Optional[BaseException], traceback: Optional[TracebackType])

Exit method for the context manager.

This method will catch any uprising exception and raise it.

Returns:If true, exceptions will not be raised. Return type:Optional[bool]

prepare(self)

Runs all neccessary processes to prepare the instances.

The child classes should implement this method according to the type of instance.

wait_until_accessible(self)

Waits until the instance is accesible through SSHClient

It attempts const.RETRIES time to ping SSH port to See if it’s listening for incoming connections. In each attempt, it waits const.RETRY_DELAY.

Raises:ConnectionError – If the instance is unaccesible through SSH

is_up(self)

Tests wether port 22 is open to incoming SSH connections

Returns:True if instance is listening in port 22. False otherwise. Return type:bool

_get_cli(self)

Get an SSHClient in order to execute commands.

This will cache an existing SSHClient to optimize resource. This is a private method and should only be used in this module.

Returns:The client for latter use. Return type:paramiko.SSHClient Raises:SSHConnectingError – In case opening an SSH connection fails.

_run_cmd(self, cmd: str, retries: int = 1, wd: str = None)

Runs a single shell command in the instance through SSH.

The command will be executed in one ssh connection. Don’t expect calling several time to _run_cmd expecting to keep state between commands. To use mutliple commands, use: _run_script

Important: when running docker containers, don’t use -it flag!

This is a private method and should only be used in this module.

Parameters:

• cmd (str) – The command to execute.
• retries (int) – The amount of attempts to run the command if it fails. Default to 1.
• wd (str) – The working directory to ‘cd’ before running the command

Returns:

A RemoteCommand instance with success boolean and message.

Return type:

RemoteCommand

Examples

To get $HOME env

>>> instance._run_cmd("echo $HOME")
RemoteCommand(True, "/home/ubuntu")

This will not work

>>> instance._run_cmd("export var=10")
>>> instance._run_cmd("echo $var")
RemoteCommand(False, "")

This will work

>>> instance._run_cmd("export var=10; echo $var")
RemoteCommand(True, "10")

Raises:RemoteCommandError – In case the cmd failes after retries attempts.

_run_script(self, fname: str, desc: str)

Runs a script by copyinh the script to the instance and executing it.

This is a private method and should only be used in this module.

Parameters:

• fname (str) – The script filename
• desc (str) – A description for the script purpose. This will be used for the copied filename

Returns:

A RemoteCommand instance with success boolean and message.

Return type:

RemoteCommand

Raises:

RemoteCommandError – In case the script fails.

_remote_script(self, host_fname: str, desc: str)

Sends a local file containing a script to the instance using Paramiko SFTP.

It should be used as a context manager for latter execution of the script. See _run_script on how to use it.

After the context manager exists, then the file is removed from the instance.

This is a private method and should only be used in this module.

Parameters:

• host_fname (str) – The local script filename
• desc (str) – A description for the script purpose. This will be used for the copied filename

Yields:

str – The remote filename of the copied local file.

Raises:

RemoteCommandError – In case sending the script fails.

run_cmds(self, setup_cmds: List[str])

Execute a list of sequential commands

Parameters:setup_cmds (List[str]) – The list of commands Returns:In case at least one command is not successful Return type:RemoteCommandError

send_rsync(self, host_path: str, remote_path: str, filter_param: str = '')

Send a local file or folder to a remote instance with rsync.

Parameters:

• host_path (str) – The local filename or folder
• remote_path (str) – The remote filename or folder to use
• filter_param (str) – The filter parameter to be passed to rsync. For example, “–filter=’:- .gitignore’”

Raises:

RemoteFileTransferError – In case sending the file fails.

get_home_path(self)

Return the $HOME value of the instance.

Returns:The $HOME env value. Return type:str Raises:RemoteCommandError – If after 3 retries it is not able to get $HOME.

clean_containers(self)

Stop and remove all containers running

Raises:RemoteCommandError – If command fails

clean_container_by_image(self, image_name: str)

Stop and remove all containers given an image name.

Parameters:image_name (str) – The name of the image for which all containers should be stopped and removed. Raises:RemoteCommandError – If command fails

clean_container_by_command(self, command: str)

Stop and remove all containers with the given command.

Parameters:command (str) – The command used to stop and remove the containers Raises:RemoteCommandError – If command fails

install_docker(self)

Install docker in a Ubuntu 18.04 distribution.

Raises:RemoteCommandError – If it’s not able to install docker. ie. then the installation script fails

install_extensions(self, extensions: Dict[str, str])

Install local + pypi extensions.

Parameters:extension (Dict[str, str]) – The extensions, as a dict from module_name to location Raises:errors.RemoteCommandError – If could not install an extension

install_flambe(self)

Pip install Flambe.

If dev mode is activated, then it rsyncs the local flambe folder and installs that version. If not, downloads from pypi.

Raises:RemoteCommandError – If it’s not able to install flambe.

is_docker_installed(self)

Check if docker is installed in the instance.

Executes command “docker –version” and expect it not to fail.

Returns:True if docker is installed. False otherwise. Return type:bool

is_flambe_installed(self, version: bool = True)

Check if flambe is installed and if it matches version.

Parameters:version (bool) – If True, also the version will be used. That is, if flag is True and the remote flambe version is different from the local flambe version, then this method will return False. If they match, then True. If version is False this method will return if there is ANY flambe version in the host. Returns: Return type:bool

is_docker_running(self)

Check if docker is running in the instance.

Executes the command “docker ps” and expects it not to fail.

Returns:True if docker is running. False otherwise. Return type:bool

start_docker(self)

Restart docker.

Raises:RemoteCommandError – If it’s not able to restart docker.

is_node_running(self)

Return if the host is running a ray node

Returns: Return type:bool

is_flambe_running(self)

Return if the host is running flambe

Returns: Return type:bool

existing_dir(self, _dir: str)

Return if a directory exists in the host

Parameters:_dir (str) – The name of the directory. It needs to be relative to $HOME Returns:True if exists. Otherwise, False. Return type:bool

shutdown_node(self)

Shut down the ray node in the host.

If the node is also the main node, then the entire cluster will shut down

shutdown_flambe(self)

Shut down flambe in the host

create_dirs(self, relative_dirs: List[str])

Create the necessary folders in the host.

Parameters:relative_dirs (List[str]) – The directories to create. They should be relative paths and $HOME of each host will be used to add the prefix.

remove_dir(self, _dir: str, content_only: bool = True)

Delete the specified dir result folder.

Parameters:

• _dir (str) – The directory. It needs to be relative to the $HOME path as it will be prepended as a prefix.
• content_only (bool) – If True, the folder itseld will not be erased.

contains_gpu(self)

Return if this machine contains GPU.

This method will be used to possibly upgrade this factory to a GPUFactoryInstance.

class flambe.cluster.instance.instance.CPUFactoryInstance

Bases: flambe.cluster.instance.instance.Instance

This class represents a CPU Instance in the Ray cluster.

CPU Factories are instances that can run only one worker (no GPUs available). This class is mostly useful debugging.

Factory instances will not keep any important information. All information is going to be sent to an orchestrator machine.

prepare(self)

Prepare a CPU machine to be a worker node.

Checks if flambe is installed, and if not, installs it.

Raises:RemoteCommandError – In case any step of the preparing process fails.

launch_node(self, redis_address: str)

Launch the ray worker node.

Parameters:redis_address (str) – The URL of the main node. Must be IP:port Raises:RemoteCommandError – If not able to run node.

num_cpus(self)

Return the number of CPUs this host contains.

num_gpus(self)

Get the number of GPUs this host contains

Returns:The number of GPUs Return type:int Raises:RemoteCommandError – If command to get the number of GPUs fails.

class flambe.cluster.instance.instance.GPUFactoryInstance

Bases: flambe.cluster.instance.instance.CPUFactoryInstance

This class represents an Nvidia GPU Factory Instance.

Factory instances will not keep any important information. All information is going to be sent to an Orchestrator machine.

prepare(self)

Prepare a GPU instance to run a ray worker node. For this, it installs CUDA and flambe if not installed.

Raises:RemoteCommandError – In case any step of the preparing process fails.

install_cuda(self)

Install CUDA 10.0 drivers in an Ubuntu 18.04 distribution.

Raises:RemoteCommandError – If it’s not able to install drivers. ie if script fails

is_cuda_installed(self)

Check if CUDA is installed trying to execute nvidia-smi

Returns:True if CUDA is installed. False otherwise. Return type:bool

class flambe.cluster.instance.instance.OrchestratorInstance

Bases: flambe.cluster.instance.instance.Instance

The orchestrator instance will be the main machine in a cluster.

It is going to be the main node in the ray cluster and it will also host other services. TODO: complete

All services besides ray will run in docker containers.

This instance does not needs to be a GPU machine.

prepare(self)

Install docker and flambe

Raises:RemoteCommandError – In case any step of the preparing process fails.

launch_report_site(self, progress_file: str, port: int, output_log: str, output_dir: str, tensorboard_port: int)

Launch the report site.

The report site is a Flask web app.

Raises:RemoteCommandError – In case the launch process fails

is_tensorboard_running(self)

Return wether tensorboard is running in the host as docker.

Returns:True if Tensorboard is running, False otherwise. Return type:bool

is_report_site_running(self)

Return wether the report site is running in the host

Returns: Return type:bool

remove_tensorboard(self)

Removes tensorboard from the orchestrator.

remove_report_site(self)

Remove report site from the orchestrator.

launch_tensorboard(self, logs_dir: str, tensorboard_port: int)

Launch tensorboard.

Parameters:

• logs_dir (str) – Tensorboard logs directory
• tensorboard_port (int) – The port where tensorboard will be available

Raises:

RemoteCommandError – In case the launch process fails

existing_tmux_session(self, session_name: str)

Return if there is an existing tmux session with the same name

Parameters:session_name (str) – The exact name of the searched tmux session Returns: Return type:bool

kill_tmux_session(self, session_name: str)

Kill an existing tmux session

Parameters:session_name (str) – The exact name of the tmux session to be removed

launch_flambe(self, config_file: str, secrets_file: str, force: bool)

Launch flambe execution in the remote host

Parameters:

• config_file (str) – The config filename relative to the orchestrator
• secrets_file (str) – The filepath containing the secrets for the orchestrator
• force (bool) – The force parameters that was originally passed to flambe

launch_node(self, port: int)

Launch the main ray node in given sftp server in port 49559.

Parameters:port (int) – Available port to launch the redis DB of the main ray node Raises:RemoteCommandError – In case the launch process fails

worker_nodes(self)

Returns the list of worker nodes

Returns:The list of worker nodes identified by their hostname Return type:List[str]

rsync_folder(self, _from, _to, exclude=None)

Rsyncs folders or files.

One of the folders NEEDS to be local. The remaining one can be remote if needed.

Package Contents

class flambe.cluster.instance.Instance(host: str, private_host: str, username: str, key: str, config: ConfigParser, debug: bool, use_public: bool = True)

Bases: object

Encapsulates remote instances.

In this context, the instance is a running computer.

All instances used by flambe remote mode will inherit Intance. This class provides high-level methods to deal with remote instances (for example, sending a shell command over SSH).

Important: Instance objects should be pickeable. Make sure that all child classes can be pickled.

The flambe local process will communicate with the remote instances using SSH. The authentication mechanism will be using private keys.

Parameters:

• host (str) – The public DNS host of the remote machine.
• private_host (str) – The private DNS host of the remote machine.
• username (str) – The machine’s username.
• key (str) – The path to the ssh key used to communicate to the instance.
• config (ConfigParser) – The config object that contains useful information for the instance. For example, config[‘SSH’][‘SSH_KEY’] should contain the path of the ssh key to login the remote instance.
• debug (bool) – True in case flambe was installed in dev mode, False otherwise.
• use_public (bool) – Wether this instance should use public or private IP. By default, the public IP is used. Private host is used when inside a private LAN.

fix_relpaths_in_config(self)

Updates all paths to be absolute. For example, if it contains “~/a/b/c” it will be change to /home/user/a/b/c (the appropiate $HOME value)

__enter__(self)

Method to use Instance instances with context managers

Returns:The current instance Return type:Instance

__exit__(self, exc_type: Optional[Type[BaseException]], exc_value: Optional[BaseException], traceback: Optional[TracebackType])

Exit method for the context manager.

This method will catch any uprising exception and raise it.

Returns:If true, exceptions will not be raised. Return type:Optional[bool]

prepare(self)

Runs all neccessary processes to prepare the instances.

The child classes should implement this method according to the type of instance.

wait_until_accessible(self)

Waits until the instance is accesible through SSHClient

It attempts const.RETRIES time to ping SSH port to See if it’s listening for incoming connections. In each attempt, it waits const.RETRY_DELAY.

Raises:ConnectionError – If the instance is unaccesible through SSH

is_up(self)

Tests wether port 22 is open to incoming SSH connections

Returns:True if instance is listening in port 22. False otherwise. Return type:bool

_get_cli(self)

Get an SSHClient in order to execute commands.

This will cache an existing SSHClient to optimize resource. This is a private method and should only be used in this module.

Returns:The client for latter use. Return type:paramiko.SSHClient Raises:SSHConnectingError – In case opening an SSH connection fails.

_run_cmd(self, cmd: str, retries: int = 1, wd: str = None)

Runs a single shell command in the instance through SSH.

The command will be executed in one ssh connection. Don’t expect calling several time to _run_cmd expecting to keep state between commands. To use mutliple commands, use: _run_script

Important: when running docker containers, don’t use -it flag!

This is a private method and should only be used in this module.

Parameters:

• cmd (str) – The command to execute.
• retries (int) – The amount of attempts to run the command if it fails. Default to 1.
• wd (str) – The working directory to ‘cd’ before running the command

Returns:

A RemoteCommand instance with success boolean and message.

Return type:

RemoteCommand

Examples

To get $HOME env

>>> instance._run_cmd("echo $HOME")
RemoteCommand(True, "/home/ubuntu")

This will not work

>>> instance._run_cmd("export var=10")
>>> instance._run_cmd("echo $var")
RemoteCommand(False, "")

This will work

>>> instance._run_cmd("export var=10; echo $var")
RemoteCommand(True, "10")

Raises:RemoteCommandError – In case the cmd failes after retries attempts.

_run_script(self, fname: str, desc: str)

Runs a script by copyinh the script to the instance and executing it.

This is a private method and should only be used in this module.

Parameters:

• fname (str) – The script filename
• desc (str) – A description for the script purpose. This will be used for the copied filename

Returns:

A RemoteCommand instance with success boolean and message.

Return type:

RemoteCommand

Raises:

RemoteCommandError – In case the script fails.

_remote_script(self, host_fname: str, desc: str)

Sends a local file containing a script to the instance using Paramiko SFTP.

It should be used as a context manager for latter execution of the script. See _run_script on how to use it.

After the context manager exists, then the file is removed from the instance.

This is a private method and should only be used in this module.

Parameters:

• host_fname (str) – The local script filename
• desc (str) – A description for the script purpose. This will be used for the copied filename

Yields:

str – The remote filename of the copied local file.

Raises:

RemoteCommandError – In case sending the script fails.

run_cmds(self, setup_cmds: List[str])

Execute a list of sequential commands

Parameters:setup_cmds (List[str]) – The list of commands Returns:In case at least one command is not successful Return type:RemoteCommandError

send_rsync(self, host_path: str, remote_path: str, filter_param: str = '')

Send a local file or folder to a remote instance with rsync.

Parameters:

• host_path (str) – The local filename or folder
• remote_path (str) – The remote filename or folder to use
• filter_param (str) – The filter parameter to be passed to rsync. For example, “–filter=’:- .gitignore’”

Raises:

RemoteFileTransferError – In case sending the file fails.

get_home_path(self)

Return the $HOME value of the instance.

Returns:The $HOME env value. Return type:str Raises:RemoteCommandError – If after 3 retries it is not able to get $HOME.

clean_containers(self)

Stop and remove all containers running

Raises:RemoteCommandError – If command fails

clean_container_by_image(self, image_name: str)

Stop and remove all containers given an image name.

Parameters:image_name (str) – The name of the image for which all containers should be stopped and removed. Raises:RemoteCommandError – If command fails

clean_container_by_command(self, command: str)

Stop and remove all containers with the given command.

Parameters:command (str) – The command used to stop and remove the containers Raises:RemoteCommandError – If command fails

install_docker(self)

Install docker in a Ubuntu 18.04 distribution.

Raises:RemoteCommandError – If it’s not able to install docker. ie. then the installation script fails

install_extensions(self, extensions: Dict[str, str])

Install local + pypi extensions.

Parameters:extension (Dict[str, str]) – The extensions, as a dict from module_name to location Raises:errors.RemoteCommandError – If could not install an extension

install_flambe(self)

Pip install Flambe.

If dev mode is activated, then it rsyncs the local flambe folder and installs that version. If not, downloads from pypi.

Raises:RemoteCommandError – If it’s not able to install flambe.

is_docker_installed(self)

Check if docker is installed in the instance.

Executes command “docker –version” and expect it not to fail.

Returns:True if docker is installed. False otherwise. Return type:bool

is_flambe_installed(self, version: bool = True)

Check if flambe is installed and if it matches version.

Parameters:version (bool) – If True, also the version will be used. That is, if flag is True and the remote flambe version is different from the local flambe version, then this method will return False. If they match, then True. If version is False this method will return if there is ANY flambe version in the host. Returns: Return type:bool

is_docker_running(self)

Check if docker is running in the instance.

Executes the command “docker ps” and expects it not to fail.

Returns:True if docker is running. False otherwise. Return type:bool

start_docker(self)

Restart docker.

Raises:RemoteCommandError – If it’s not able to restart docker.

is_node_running(self)

Return if the host is running a ray node

Returns: Return type:bool

is_flambe_running(self)

Return if the host is running flambe

Returns: Return type:bool

existing_dir(self, _dir: str)

Return if a directory exists in the host

Parameters:_dir (str) – The name of the directory. It needs to be relative to $HOME Returns:True if exists. Otherwise, False. Return type:bool

shutdown_node(self)

Shut down the ray node in the host.

If the node is also the main node, then the entire cluster will shut down

shutdown_flambe(self)

Shut down flambe in the host

create_dirs(self, relative_dirs: List[str])

Create the necessary folders in the host.

Parameters:relative_dirs (List[str]) – The directories to create. They should be relative paths and $HOME of each host will be used to add the prefix.

remove_dir(self, _dir: str, content_only: bool = True)

Delete the specified dir result folder.

Parameters:

• _dir (str) – The directory. It needs to be relative to the $HOME path as it will be prepended as a prefix.
• content_only (bool) – If True, the folder itseld will not be erased.

contains_gpu(self)

Return if this machine contains GPU.

This method will be used to possibly upgrade this factory to a GPUFactoryInstance.

class flambe.cluster.instance.CPUFactoryInstance

Bases: flambe.cluster.instance.instance.Instance

This class represents a CPU Instance in the Ray cluster.

CPU Factories are instances that can run only one worker (no GPUs available). This class is mostly useful debugging.

Factory instances will not keep any important information. All information is going to be sent to an orchestrator machine.

prepare(self)

Prepare a CPU machine to be a worker node.

Checks if flambe is installed, and if not, installs it.

Raises:RemoteCommandError – In case any step of the preparing process fails.

launch_node(self, redis_address: str)

Launch the ray worker node.

Parameters:redis_address (str) – The URL of the main node. Must be IP:port Raises:RemoteCommandError – If not able to run node.

num_cpus(self)

Return the number of CPUs this host contains.

num_gpus(self)

Get the number of GPUs this host contains

Returns:The number of GPUs Return type:int Raises:RemoteCommandError – If command to get the number of GPUs fails.

class flambe.cluster.instance.GPUFactoryInstance

Bases: flambe.cluster.instance.instance.CPUFactoryInstance

This class represents an Nvidia GPU Factory Instance.

Factory instances will not keep any important information. All information is going to be sent to an Orchestrator machine.

prepare(self)

Prepare a GPU instance to run a ray worker node. For this, it installs CUDA and flambe if not installed.

Raises:RemoteCommandError – In case any step of the preparing process fails.

install_cuda(self)

Install CUDA 10.0 drivers in an Ubuntu 18.04 distribution.

Raises:RemoteCommandError – If it’s not able to install drivers. ie if script fails

is_cuda_installed(self)

Check if CUDA is installed trying to execute nvidia-smi

Returns:True if CUDA is installed. False otherwise. Return type:bool

class flambe.cluster.instance.OrchestratorInstance

Bases: flambe.cluster.instance.instance.Instance

The orchestrator instance will be the main machine in a cluster.

It is going to be the main node in the ray cluster and it will also host other services. TODO: complete

All services besides ray will run in docker containers.

This instance does not needs to be a GPU machine.

prepare(self)

Install docker and flambe

Raises:RemoteCommandError – In case any step of the preparing process fails.

launch_report_site(self, progress_file: str, port: int, output_log: str, output_dir: str, tensorboard_port: int)

Launch the report site.

The report site is a Flask web app.

Raises:RemoteCommandError – In case the launch process fails

is_tensorboard_running(self)

Return wether tensorboard is running in the host as docker.

Returns:True if Tensorboard is running, False otherwise. Return type:bool

is_report_site_running(self)

Return wether the report site is running in the host

Returns: Return type:bool

remove_tensorboard(self)

Removes tensorboard from the orchestrator.

remove_report_site(self)

Remove report site from the orchestrator.

launch_tensorboard(self, logs_dir: str, tensorboard_port: int)

Launch tensorboard.

Parameters:

• logs_dir (str) – Tensorboard logs directory
• tensorboard_port (int) – The port where tensorboard will be available

Raises:

RemoteCommandError – In case the launch process fails

existing_tmux_session(self, session_name: str)

Return if there is an existing tmux session with the same name

Parameters:session_name (str) – The exact name of the searched tmux session Returns: Return type:bool

kill_tmux_session(self, session_name: str)

Kill an existing tmux session

Parameters:session_name (str) – The exact name of the tmux session to be removed

launch_flambe(self, config_file: str, secrets_file: str, force: bool)

Launch flambe execution in the remote host

Parameters:

• config_file (str) – The config filename relative to the orchestrator
• secrets_file (str) – The filepath containing the secrets for the orchestrator
• force (bool) – The force parameters that was originally passed to flambe

launch_node(self, port: int)

Launch the main ray node in given sftp server in port 49559.

Parameters:port (int) – Available port to launch the redis DB of the main ray node Raises:RemoteCommandError – In case the launch process fails

worker_nodes(self)

Returns the list of worker nodes

Returns:The list of worker nodes identified by their hostname Return type:List[str]

rsync_folder(self, _from, _to, exclude=None)

Rsyncs folders or files.

One of the folders NEEDS to be local. The remaining one can be remote if needed.

Submodules

flambe.cluster.aws

Implementation of a Cluster with AWS EC2 as the cloud provider

Module Contents

flambe.cluster.aws.logger

flambe.cluster.aws.T

class flambe.cluster.aws.AWSCluster(name: str, factories_num: int, factories_type: str, orchestrator_type: str, key_name: str, security_group: str, subnet_id: str, creator: str, key: str, volume_type: str = 'gp2', region_name: Optional[str] = None, username: str = 'ubuntu', tags: Dict[str, str] = None, orchestrator_ami: str = None, factory_ami: str = None, dedicated: bool = False, orchestrator_timeout: int = -1, factories_timeout: int = 1, volume_size: int = 100, setup_cmds: Optional[List[str]] = None)

Bases: flambe.cluster.cluster.Cluster

This Cluster implementation uses AWS EC2 as the cloud provider.

This cluster works with AWS Instances that are defined in: flambe.remote.instance.aws

Parameters:

• name (str) – The unique name for the cluster
• factories_num (int) – The amount of factories to use. This is not the amount of workers, as each factories can contain multiple GPUs and therefore, multiple workers.
• factories_type (str) – The type of instance to use for the Factory Instances. GPU instances are required for AWS the AWSCluster. “p2” and “p3” instances are recommended.
• factory_ami (str) – The AMI to be used for the Factory instances. Custom Flambe AMI are provided based on Ubuntu 18.04 distribution.
• orchestrator_type (str) – The type of instance to use for the Orchestrator Instances. This may not be a GPU instances. At least a “t2.small” instance is recommended.
• key_name (str) – The key name that will be used to connect into the instance.
• creator (str) – The creator should be a user identifier for the instances. This information will create a tag called ‘creator’ and it will also be used to retrieve existing hosts owned by the user.
• key (str) – The path to the ssh key used to communicate to all instances. IMPORTANT: all instances must be accessible with the same key.
• volume_type (str) – The type of volume in AWS to use. Only ‘gp2’ and ‘io1’ are currently available. If ‘io1’ is used, then IOPS will be fixed to 5000. IMPORTANT: ‘io1’ volumes are significantly more expensive than ‘gp2’ volumes. Defaults to ‘gp2’.
• region_name (Optional[str]) – The region name to use. If not specified, it uses the locally configured region name or ‘us-east-1’ in case it’s not configured.
• username (str) – The username of the instances the cluster will handle. Defaults to ‘ubuntu’. IMPORTANT: for now all instances need to have the same username.
• tags (Dict[str, str]) – A dictionary with tags that will be added to all created hosts.
• security_group (str) – The security group to use to create the instances.
• subnet_id (str) – The subnet ID to use.
• orchestrator_ami (str) – The AMI to be used for the Factory instances. Custom Flambe AMI are provided based on Ubuntu 18.04 distribution.
• dedicated (bool) – Wether all created instances are dedicated instances or shared.
• orchestrator_timeout (int) – Number of consecutive hours before terminating the orchestrator once the experiment is over (either success of failure). Specify -1 to disable automatic shutdown (the orchestrator will stay on until manually terminated) and 0 to shutdown when the experiment is over. For example, if specifying 24, then the orchestrator will be shut down one day after the experiment is over. ATTENTION: This also applies when the experiment ends with an error. Default is -1.
• factories_timeout (int) – Number of consecutive hours to automatically terminate factories once the experiment is over (either success or failure). Specify -1 to disable automatic shutdown (the factories will stay on until manually terminated) and 0 to shutdown when the experiment is over. For example, if specifying 10, then the factories will be shut down 10 hours after the experiment is over. ATTENTION: This also applies when the experiment ends with an error. Default is 1.
• volume_size (int) – The disk size in GB that all hosts will contain. Defaults to 100 GB.
• setup_cmds (Optional[List[str]]) – A list of commands to be run on all hosts for setup purposes. These commands can be used to mount volumes, install software, etc. Defaults to None. IMPORTANT: the commands need to be idempotent and they shouldn’t expect user input.

_get_boto_session(self, region_name: Optional[str])

Get the boto3 Session from which the resources and clients will be created.

This method is called by the contructor.

Parameters:region_name (Optional[str]) – The region to use. If None, boto3 will resolve to the locally configured region_name or ‘us-east-1’ if not configured. Returns:The boto3 Session to use Return type:boto3.Session

load_all_instances(self)

Launch all instances for the experiment.

This method launches both the orchestrator and the factories.

_existing_cluster(self)

Whether there is an existing cluster that matches name.

The cluster should also match all other tags, including Creator)

Returns:Returns the (boto_orchestrator, [boto_factories]) that match the experiment’s name. Return type:Tuple[Any, List[Any]]

_get_existing_tags(self, boto_instance: boto3.resources.factory.ec2.Instance)

Gets the tags of a EC2 instances

Parameters:boto_instance (BotoIns) – The EC2 instance to access the tags. Returns:Key, Value for the specified tags. Return type:Dict[str, str]

flambe_own_running_instances(self)

Get running instances with matching tags.

Yields:Tuple[‘boto3.resources.factory.ec2.Instance’, str] – A tuple with the instance and the name of the EC2 instance.

name_hosts(self)

Name the orchestrator and factories.

_get_all_tags(self)

Get user tags + default tags to add to the instances and volumes.

update_tags(self)

Update user provided tags to all hosts.

In case there is an existing cluster that do not contain all the tags, by executing this all hosts will have the user specified tags.

This won’t remove existing tags in the hosts.

_update_tags(self, boto_instance: boto3.resources.factory.ec2.Instance, tags: Dict[str, str])

Create/Overwrite tags on an EC2 instance and its volumes.

Parameters:

• boto_instance ('boto3.resources.factory.ec2.Instance') – The EC2 instance
• tags (Dict[str, str]) – The tags to create/overwrite

name_instance(self, boto_instance: boto3.resources.factory.ec2.Instance, name: str)

Renames a EC2 instance

Parameters:

• boto_instance ('boto3.resources.factory.ec2.Instance') – The EC2 instance
• name (str) – The new name

_create_orchestrator(self)

Create a new EC2 instance to be the Orchestrator instance.

This new machine receives all tags defined in the *.ini file.

Returns:The new orchestrator instance. Return type:instance.AWSOrchestratorInstance

_create_factories(self, number: int = 1)

Creates new AWS EC2 instances to be the Factory instances.

These new machines receive all tags defined in the *.ini file. Factory instances will be named using the factory basename plus an index. For example, “seq2seq_factory_0”, “seq2seq_factory_1”.

Parameters:number (int) – The number of factories to be created. Returns:The new factory instances. Return type:List[instance.AWSGPUFactoryInstance]

_generic_launch_instances(self, instance_class: Type[T], number: int, instance_type: str, instance_ami: str, role: str)

Generic method to launch instances in AWS EC2 using boto3.

This method should not be used outside this module.

Parameters:

• instance_class (Type[T]) – The instance class. It can be AWSOrchestratorInstance or AWSGPUFactoryInstance.
• number (int) – The amount of instances to create
• instance_type (str) – The instance type
• instance_ami (str) – The AMI to be used. Should be an Ubuntu 18.04 based AMI.
• role (str) – Wether is ‘Orchestrator’ or ‘Factory’

Returns:

The new Instances.

Return type:

List[Union[AWSOrchestratorInstance, AWSGPUFactoryInstance]]

terminate_instances(self)

Terminates all instances.

rollback_env(self)

Rollback the environment.

This occurs when an error is caucht during the local stage of the remote experiment (i.e. creating the cluster, sending the data and submitting jobs), this method handles cleanup stages.

parse(self)

Checks if the AWSCluster configuration is valid.

This checks that the factories are never terminated after the orchestrator is. Avoids the scenario where the cluster has only factories and no orchestrator, which is useless.

Raises:errors.ClusterConfigurationError – If configuration is not valid.

_get_boto_instance_by_host(self, public_host: str)

Returns the instance id given the public host

Parameters:public_host (str) – The host in IP format of DNS format Returns:The id if found else None Return type:Optional[boto3.resources.factory.ec2.Instance]

_get_instance_id_by_host(self, public_host: str)

Returns the instance id given the public host

Parameters:public_host (str) – The host in IP format of DNS format Returns:The id if found else None Return type:Optional[str]

_get_alarm_name(self, instance_id: str)

Get the alarm name to be used for the given instance.

Parameters:instance_id (str) – The id of the instance Returns:The name of the corresponding alarm Return type:str

has_alarm(self, instance_id: str)

Whether the instance has an alarm set.

Parameters:instance_id (str) – The id of the instance Returns:True if an alarm is set. False otherwise. Return type:bool

remove_existing_events(self)

Remove the current alarm.

In case the orchestrator or factories had an alarm, we remove it to reset the new policies.

create_cloudwatch_events(self)

Creates cloudwatch events for orchestrator and factories.

_delete_cloudwatch_event(self, instance_id: str)

Deletes the alarm related to the instance.

_put_fake_cloudwatch_data(self, instance_id: str, value: int = 100, points: int = 10)

Put fake CPU Usage metric in an instance.

This method is useful to avoid triggering alarms when they are created. For example, is an instance was idle for 10 hours and an termination alarm is set for 5 hours, it will be triggered immediately. Adding a fake point will allow the alarms to start the timer from the current moment.

Parameters:

• instance_id (str) – The ID of the EC2 instance
• value (int) – The CPU percent value to use. Defaults to 100
• points (int) – The amount of past minutes from the current time to generate metric points. For example, if points is 10, then 10 data metrics will be generated for the past 10 minutes, one per minute.

_create_cloudwatch_event(self, instance_id: str, mins: int = 60, cpu_thresh: float = 0.1)

Create CloudWatch alarm.

The alrm is used to terminate an instance based on CPU usage.

Parameters:

• instance_id (str) – The ID of the EC2 instance
• mins (int) – Number of minutes to trigger the termination event. The evaluation preriod will be always one minute.
• cpu_thresh (float) – Percentage specifying upper bound for triggering event. If mins is 60 and cpu_thresh is 0.1, then this instance will be deleted after 1 hour of average CPU below 0.1.

_get_images(self)

Get the official AWS public AMIs created by Flambe.

ATTENTION: why not just search the tags? We need to make sure the AMIs we pick were created by the Flambe team. Because of tags values not being unique, anyone can create a public AMI with ‘Creator: flambe@asapp.com’ as a tag. If we pick that AMI, then we could potentially be Creating instances with unknown AMIs, causing potential security issues. By filtering by our acount id (which can be public), then we can make sure that all AMIs that are being scanned were created by Flambe team.

Returns:The boto3 API response Return type:Dict

_get_ami(self, _type: str, version: str)

Given a type and a version, get the correct Flambe AMI.

IMPORTANT: we keep the version logic in case we add versioned AMIs in the future.

Parameters:

• _type (str) – It can be either ‘factory’ or ‘orchestrator’. Note that the type is lowercase in the AMI tag.
• version (str) – For example, “0.2.1” or “2.0”.

Returns:

Return type:

The ImageId if it’s found. None if not.

_find_default_ami(self, _type: str)

Returns an AMI with version 0.0.0, which is the default. This means that doesn’t contain flambe itself but it has some heavy dependencies already installed (like pytorch).

Parameters:_type (str) – Wether is “orchestrator” or “factory” Returns:The ImageId or None if not found. Return type:Optional[str]

flambe.cluster.cluster

This module contains the base implementation of a Cluster.

A Cluster is in charge of dealing with the different Instance objects that will be part of the remote runnable.

Module Contents

flambe.cluster.cluster.logger

flambe.cluster.cluster.GPUFactoryInsT

flambe.cluster.cluster.CPUFactoryInsT

flambe.cluster.cluster.FactoryInsT

class flambe.cluster.cluster.Cluster(name: str, factories_num: int, key: str, username: str, setup_cmds: Optional[List[str]] = None)

Bases: flambe.runnable.Runnable

Basic implementation of a Cluster.

The cluster is in charge of creating the cluster of instances where one host is the Orchestrator while the other ones are Factories.

This implementation should not be used by an end user. In order to give support to a cloud service provider (ex: AWS), a child class must be implemented inheriting from the Cluster class.

Important: when possible, Clusters should context managers

Parameters:

• name (str) – The name of the cluster, used to name the remote instances.
• factories_num (int) – The amount of factories to use. Note that this differs from the number of workers, as each factories can contain multiple GPUs and therefore, multiple workers.
• key (str) – The path to the ssh key used to communicate to all instances. IMPORTANT: all instances must be accessible with the same key.
• username (str) – The username of the instances the cluster will handle. IMPORTANT: for now all instances need to have the same username.
• setup_cmds (Optional[List[str]]) – A list of commands to be run on all hosts for setup purposes. These commands can be used to mount volumes, install software, etc. Defaults to None. IMPORTANT: the commands need to be idempotent and they shouldn’t expect user input.

__enter__(self)

A Cluster should be used with a context cluster to handle all possible errors in a clear way.

Examples

>>> with cluster as cl:
>>>     cl.launch_orchestrator()
>>>     cl.build_cluster()
>>>     ...

__exit__(self, exc_type: Optional[Type[BaseException]], exc_value: Optional[BaseException], tb: Optional[TracebackType])

Exit method for the context cluster.

This method will catch any exception, log it and return True. This means that all exceptions produced in a Cluster (used with the context cluster) will not continue to raise.

Returns:True, as an exception should not continue to raise. Return type:Optional[bool]

get_orchestrator_name(self)

Get the orchestrator name.

The name is given by name with the ‘_orchestrator’ suffix. For example, if name is ‘seq2seq-en-fr’, then the orchestrator name will be ‘seq2seq-en-fr_orchestrator’.

This is an auxiliary method that can be used in child classes.

Returns:The orcehstrator name Return type:str

get_factory_basename(self)

Get the factory base name.

The name is name with the ‘_factory’ suffix. For example, if name is ‘seq2seq-en-fr’, then the factory basename will be ‘seq2seq-en-fr_factory’.

The base name can be used to generate all the factories’ names (for example, by also appending an index to the basename).

This is an auxiliary method that can be used in child classes.

Returns:The factory basename Return type:str

load_all_instances(self)

Method to make all hosts accessible.

Depending on the Cluster type, it behaves differently. For example, AWSCluster or GCPCluster can create the instances in this step. The SSHCluster does nothing (the machines are already created).

_get_all_hosts(self)

Auxiliary method to get all the hosts in a list.append(

create_dirs(self, relative_dirs: List[str])

Create folders in all hostss.

If some of the already exist, it will do nothing.

Parameters:relative_dirs (List[str]) – The directories to create. They should be relative paths and $HOME of each host will be used to add the prefix.

prepare_all_instances(self)

Prepare all the instances (both orchestrator and factories).

This method assumes that the hosts are running and accesible. It will call the ‘prepare’ method from all hosts.

run(self, force: bool = False, **kwargs)

Run a cluster and load all the instances.

After this metho runs, the orchestrator and factories objects will be populated.

If a runnable is provided, then the cluster will execute the runnable remotely in the cluster. Currently, only ClusterRunnable is supported.

This method should be idempotent (ie if called N times with the same configuration, only one cluster will be created.)

Parameters:force (bool, defaults to False) – If true, current executions of the same runnable in the cluster will be overriden by a new execution.

run_cmds(self, setup_cmds: List[str])

Run setup commands in all hosts

Parameters:setup_cmds (List[str]) – The list of commands Raises:errors.RemoteCommandError – If at least one commands is not successful in at least one host.

get_orchestrator(self, ip: str, private_ip: str = None, use_public: bool = True)

Get an orchestrator instance

get_orch_home_path(self)

Return the orchestrator home path

Returns: Return type:str

get_factory(self, ip: str, private_ip: str = None, use_public: bool = True)

Get an CPU factory instance

get_gpu_factory(self, ip: str, private_ip: str = None, use_public: bool = True)

Get an GPU factory instance

launch_ray_cluster(self)

Create a ray cluster.

The main node is going to be located in the orchestrator machine and all other nodes in the factories.

The main node is executed with –num-cpus=0 flag so that it doesn’t do any work and all work is done by the factories.

check_ray_cluster(self)

Check if ray cluster was build successfully.

Compares the name of workers available with the requested ones.

Returns:Whether the number of workers in the node matches the number of factories Return type:bool

shutdown_ray_cluster(self)

Shut down the ray cluster.

Shut down the main node running in the orchestrator.

existing_ray_cluster(self)

Return a list of the nodes in the Ray cluster.

Returns:The list of nodes Return type:List[Instance]

existing_flambe_execution(self)

Return a list of the hosts that are running flambe.

Returns:The list of nodes Return type:List[Instance]

shutdown_flambe_execution(self)

Shut down any flambe execution in the hosts.

existing_dir(self, _dir: str)

Determine if _dir exists in at least one host

is_ray_cluster_up(self)

Return if the ray cluster is running.

Returns: Return type:bool

rollback_env(self)

Rollback the enviornment.

When an error occures during the local stage of the remote runnable (i.e. creating the cluster, sending the data and submitting jobs), this method may be used to destroy the cluster that has been built.

parse(self)

Parse the cluster object.

Look for configurations mistakes that don’t allow the remote runnable to run. Each different cluster will have it’s own policies. For example, AWSCluster could check the instance types that are allowed. By default, checks nothing.

Raises:man_errors.ClusterConfigurationError – In case the Runnable is not able to run.

send_local_content(self, content: Dict[str, str], dest: str, all_hosts: bool = False)

Send local content to the cluster

Parameters:

• content (Dict[str, str]) – The dict of key -> name
• dest (str) – The orchestator’s destination folder
• all_hosts (bool) – If False, only send the content to the orchestrator. If True, send to all factories.

Returns:

The new dict of content with orchestrator’s paths.

Return type:

Dict[str, str]

rsync_orch(self, folder)

Rsync the orchestrator’s folder with all factories

Parameters:folder (str) – The folder to rsync. It should be a relative path. $HOME value will be automatically added.

send_secrets(self, whitelist: List[str] = None)

Send the secrets file to the orchestrator.

This file will be located in $HOME/secrets.ini The injected secrets file will be used.

Parameters:whitelist (List[str]) – A list of sections to filter. For example: [“AWS”, “GITHUB”]

execute(self, cluster_runnable, extensions: Dict[str, str], new_secrets: str, force: bool)

Execute a ClusterRunnable in the cluster.

It will first upload the runnable file + extensions to the orchestrator (under $HOME/flambe.yaml) and then it will execute it based on the provided secrets

Parameters:

• cluster_runnable (ClusterRunnable) – The ClusterRunnable to run in the cluster
• extensions (Dict[str, str]) – The extensions for the ClusterRunnable
• new_secrets (str) – The path (relative to the orchestrator) where the secrets are located. IMPORTANT: previous to calling this method, the secrets should have been uploaded to the orchestrator
• force (bool) – The force parameter provided when running flambe locally

remove_dir(self, _dir: str, content_only: bool = True, all_hosts: bool = True)

Remove a directory in the ClusterError

Parameters:

• _dir (str) – The directory to remove
• content_only (bool) – To remove the content only or the folder also. Defaults to True.
• all_hosts (bool) – To remove it in all hosts or only in the Orchestrator. Defaults to True (in all hosts).

cluster_has_key(self)

Whether the cluster already contains a valid common key.

The key must be in all hosts.

Returns:If the cluster has a key in all hosts. Return type:bool

distribute_keys(self)

Create a new key pair and distributes it to all hosts.

Ensure that the hosts have a safe communication. The name of the key is the cluster’s name

contains_gpu_factories(self)

Return if the factories contain GPU.

For now, all factories are same machine type, so as soon as a GPU is found, then this method returns.

get_max_resources(self)

Return the max common CPU/GPU devices in the factories

For example, if one factory contains 32 CPU + 1 GPU and the other factory contains 16 CPU + 2 GPU, this method will return {“cpu”: 16, “gpu”: 1} available

Returns:The devices, in {“cpu”: N, “gpu”: M} format Return type:Dict[str, int]

install_extensions_in_orchestrator(self, extensions: Dict[str, str])

Install local + pypi extensions in the orchestrator

Parameters:

extension (Dict[str, str]) – The extensions, as a dict from module_name to location

Raises:

• errors.RemoteCommandError – If could not install an extension.
• man_errors.ClusterError – If the orchestrator was not loaded.

install_extensions_in_factories(self, extensions: Dict[str, str])

Install local + pypi extensions in all the factories.

Parameters:extension (Dict[str, str]) – The extensions, as a dict from module_name to location Raises:errors.RemoteCommandError – If could not install an extension

get_remote_env(self)

Get the RemoteEnvironment for this cluster.

The IPs stored will be the private IPs

Returns:The RemoteEnvironment with information about this cluster. Return type:RemoteEnvironment

flambe.cluster.const

Module Contents

flambe.cluster.const.SOCKET_TIMEOUT = 50

flambe.cluster.const.RETRY_DELAY = 1

flambe.cluster.const.RETRIES = 60

flambe.cluster.const.TENSORBOARD_IMAGE = tensorflow/tensorflow:latest

flambe.cluster.const.RAY_REDIS_PORT = 12345

flambe.cluster.const.PRIVATE_KEY = ray_bootstrap_key.pem

flambe.cluster.const.PUBLIC_KEY = ray_bootstrap_key.pub

flambe.cluster.const.REPORT_SITE_PORT = 49558

flambe.cluster.const.TENSORBOARD_PORT = 49556

flambe.cluster.const.AWS_FLAMBE_ACCOUNT = 808129580301

flambe.cluster.errors

Module Contents

exception flambe.cluster.errors.ClusterError

Bases: Exception

Error raised in case of any unexpected error in the Ray cluster.

exception flambe.cluster.errors.ClusterConfigurationError

Bases: Exception

Error raised when the configuration of the Cluster is not valid.

flambe.cluster.ssh

Implementation of the Manager for SSH hosts

Module Contents

flambe.cluster.ssh.logger

flambe.cluster.ssh.FactoryT

class flambe.cluster.ssh.SSHCluster(name: str, orchestrator_ip: Union[str, List[str]], factories_ips: Union[List[str], List[List[str]]], key: str, username: str, remote_context=None, use_public: bool = True, setup_cmds: Optional[List[str]] = None)

Bases: flambe.cluster.cluster.Cluster

The SSH Manager needs to be used when having running instances.

For example when having on-prem hardware or just a couple of AWS EC2 instances running.

When using this cluster, the user needs to specify the IPs of the machines to use, both the public one and private one.

load_all_instances(self, exp_name: str = None, force: bool = False)

This manager assumed that instances are running.

This method loads the Python objects to the manager’s variables.

Parameters:

• exp_name (str) – The name of the experiment
• force (bool) – Whether to override the current experiment of the same name

rollback_env(self)

rsync_hosts(self)

Rsyncs the host’s result folders.

First, it rsyncs all worker folders to the orchestrator main folder. After that, so that every worker gets the last changes, the orchestrator rsync with all of them.

flambe.cluster.utils

Module Contents

flambe.cluster.utils.RemoteCommand

Package Contents

class flambe.cluster.Cluster(name: str, factories_num: int, key: str, username: str, setup_cmds: Optional[List[str]] = None)

Bases: flambe.runnable.Runnable

Basic implementation of a Cluster.

The cluster is in charge of creating the cluster of instances where one host is the Orchestrator while the other ones are Factories.

This implementation should not be used by an end user. In order to give support to a cloud service provider (ex: AWS), a child class must be implemented inheriting from the Cluster class.

Important: when possible, Clusters should context managers

Parameters:

• name (str) – The name of the cluster, used to name the remote instances.
• factories_num (int) – The amount of factories to use. Note that this differs from the number of workers, as each factories can contain multiple GPUs and therefore, multiple workers.
• key (str) – The path to the ssh key used to communicate to all instances. IMPORTANT: all instances must be accessible with the same key.
• username (str) – The username of the instances the cluster will handle. IMPORTANT: for now all instances need to have the same username.
• setup_cmds (Optional[List[str]]) – A list of commands to be run on all hosts for setup purposes. These commands can be used to mount volumes, install software, etc. Defaults to None. IMPORTANT: the commands need to be idempotent and they shouldn’t expect user input.

__enter__(self)

A Cluster should be used with a context cluster to handle all possible errors in a clear way.

Examples

>>> with cluster as cl:
>>>     cl.launch_orchestrator()
>>>     cl.build_cluster()
>>>     ...

__exit__(self, exc_type: Optional[Type[BaseException]], exc_value: Optional[BaseException], tb: Optional[TracebackType])

Exit method for the context cluster.

This method will catch any exception, log it and return True. This means that all exceptions produced in a Cluster (used with the context cluster) will not continue to raise.

Returns:True, as an exception should not continue to raise. Return type:Optional[bool]

get_orchestrator_name(self)

Get the orchestrator name.

The name is given by name with the ‘_orchestrator’ suffix. For example, if name is ‘seq2seq-en-fr’, then the orchestrator name will be ‘seq2seq-en-fr_orchestrator’.

This is an auxiliary method that can be used in child classes.

Returns:The orcehstrator name Return type:str

get_factory_basename(self)

Get the factory base name.

The name is name with the ‘_factory’ suffix. For example, if name is ‘seq2seq-en-fr’, then the factory basename will be ‘seq2seq-en-fr_factory’.

The base name can be used to generate all the factories’ names (for example, by also appending an index to the basename).

This is an auxiliary method that can be used in child classes.

Returns:The factory basename Return type:str

load_all_instances(self)

Method to make all hosts accessible.

Depending on the Cluster type, it behaves differently. For example, AWSCluster or GCPCluster can create the instances in this step. The SSHCluster does nothing (the machines are already created).

_get_all_hosts(self)

Auxiliary method to get all the hosts in a list.append(

create_dirs(self, relative_dirs: List[str])

Create folders in all hostss.

If some of the already exist, it will do nothing.

Parameters:relative_dirs (List[str]) – The directories to create. They should be relative paths and $HOME of each host will be used to add the prefix.

prepare_all_instances(self)

Prepare all the instances (both orchestrator and factories).

This method assumes that the hosts are running and accesible. It will call the ‘prepare’ method from all hosts.

run(self, force: bool = False, **kwargs)

Run a cluster and load all the instances.

After this metho runs, the orchestrator and factories objects will be populated.

If a runnable is provided, then the cluster will execute the runnable remotely in the cluster. Currently, only ClusterRunnable is supported.

This method should be idempotent (ie if called N times with the same configuration, only one cluster will be created.)

Parameters:force (bool, defaults to False) – If true, current executions of the same runnable in the cluster will be overriden by a new execution.

run_cmds(self, setup_cmds: List[str])

Run setup commands in all hosts

Parameters:setup_cmds (List[str]) – The list of commands Raises:errors.RemoteCommandError – If at least one commands is not successful in at least one host.

get_orchestrator(self, ip: str, private_ip: str = None, use_public: bool = True)

Get an orchestrator instance

get_orch_home_path(self)

Return the orchestrator home path

Returns: Return type:str

get_factory(self, ip: str, private_ip: str = None, use_public: bool = True)

Get an CPU factory instance

get_gpu_factory(self, ip: str, private_ip: str = None, use_public: bool = True)

Get an GPU factory instance

launch_ray_cluster(self)

Create a ray cluster.

The main node is going to be located in the orchestrator machine and all other nodes in the factories.

The main node is executed with –num-cpus=0 flag so that it doesn’t do any work and all work is done by the factories.

check_ray_cluster(self)

Check if ray cluster was build successfully.

Compares the name of workers available with the requested ones.

Returns:Whether the number of workers in the node matches the number of factories Return type:bool

shutdown_ray_cluster(self)

Shut down the ray cluster.

Shut down the main node running in the orchestrator.

existing_ray_cluster(self)

Return a list of the nodes in the Ray cluster.

Returns:The list of nodes Return type:List[Instance]

existing_flambe_execution(self)

Return a list of the hosts that are running flambe.

Returns:The list of nodes Return type:List[Instance]

shutdown_flambe_execution(self)

Shut down any flambe execution in the hosts.

existing_dir(self, _dir: str)

Determine if _dir exists in at least one host

is_ray_cluster_up(self)

Return if the ray cluster is running.

Returns: Return type:bool

rollback_env(self)

Rollback the enviornment.

When an error occures during the local stage of the remote runnable (i.e. creating the cluster, sending the data and submitting jobs), this method may be used to destroy the cluster that has been built.

parse(self)

Parse the cluster object.

Look for configurations mistakes that don’t allow the remote runnable to run. Each different cluster will have it’s own policies. For example, AWSCluster could check the instance types that are allowed. By default, checks nothing.

Raises:man_errors.ClusterConfigurationError – In case the Runnable is not able to run.

send_local_content(self, content: Dict[str, str], dest: str, all_hosts: bool = False)

Send local content to the cluster

Parameters:

• content (Dict[str, str]) – The dict of key -> name
• dest (str) – The orchestator’s destination folder
• all_hosts (bool) – If False, only send the content to the orchestrator. If True, send to all factories.

Returns:

The new dict of content with orchestrator’s paths.

Return type:

Dict[str, str]

rsync_orch(self, folder)

Rsync the orchestrator’s folder with all factories

Parameters:folder (str) – The folder to rsync. It should be a relative path. $HOME value will be automatically added.

send_secrets(self, whitelist: List[str] = None)

Send the secrets file to the orchestrator.

This file will be located in $HOME/secrets.ini The injected secrets file will be used.

Parameters:whitelist (List[str]) – A list of sections to filter. For example: [“AWS”, “GITHUB”]

execute(self, cluster_runnable, extensions: Dict[str, str], new_secrets: str, force: bool)

Execute a ClusterRunnable in the cluster.

It will first upload the runnable file + extensions to the orchestrator (under $HOME/flambe.yaml) and then it will execute it based on the provided secrets

Parameters:

• cluster_runnable (ClusterRunnable) – The ClusterRunnable to run in the cluster
• extensions (Dict[str, str]) – The extensions for the ClusterRunnable
• new_secrets (str) – The path (relative to the orchestrator) where the secrets are located. IMPORTANT: previous to calling this method, the secrets should have been uploaded to the orchestrator
• force (bool) – The force parameter provided when running flambe locally

remove_dir(self, _dir: str, content_only: bool = True, all_hosts: bool = True)

Remove a directory in the ClusterError

Parameters:

• _dir (str) – The directory to remove
• content_only (bool) – To remove the content only or the folder also. Defaults to True.
• all_hosts (bool) – To remove it in all hosts or only in the Orchestrator. Defaults to True (in all hosts).

cluster_has_key(self)

Whether the cluster already contains a valid common key.

The key must be in all hosts.

Returns:If the cluster has a key in all hosts. Return type:bool

distribute_keys(self)

Create a new key pair and distributes it to all hosts.

Ensure that the hosts have a safe communication. The name of the key is the cluster’s name

contains_gpu_factories(self)

Return if the factories contain GPU.

For now, all factories are same machine type, so as soon as a GPU is found, then this method returns.

get_max_resources(self)

Return the max common CPU/GPU devices in the factories

For example, if one factory contains 32 CPU + 1 GPU and the other factory contains 16 CPU + 2 GPU, this method will return {“cpu”: 16, “gpu”: 1} available

Returns:The devices, in {“cpu”: N, “gpu”: M} format Return type:Dict[str, int]

install_extensions_in_orchestrator(self, extensions: Dict[str, str])

Install local + pypi extensions in the orchestrator

Parameters:

extension (Dict[str, str]) – The extensions, as a dict from module_name to location

Raises:

• errors.RemoteCommandError – If could not install an extension.
• man_errors.ClusterError – If the orchestrator was not loaded.

install_extensions_in_factories(self, extensions: Dict[str, str])

Install local + pypi extensions in all the factories.

Parameters:extension (Dict[str, str]) – The extensions, as a dict from module_name to location Raises:errors.RemoteCommandError – If could not install an extension

get_remote_env(self)

Get the RemoteEnvironment for this cluster.

The IPs stored will be the private IPs

Returns:The RemoteEnvironment with information about this cluster. Return type:RemoteEnvironment

class flambe.cluster.AWSCluster(name: str, factories_num: int, factories_type: str, orchestrator_type: str, key_name: str, security_group: str, subnet_id: str, creator: str, key: str, volume_type: str = 'gp2', region_name: Optional[str] = None, username: str = 'ubuntu', tags: Dict[str, str] = None, orchestrator_ami: str = None, factory_ami: str = None, dedicated: bool = False, orchestrator_timeout: int = -1, factories_timeout: int = 1, volume_size: int = 100, setup_cmds: Optional[List[str]] = None)

Bases: flambe.cluster.cluster.Cluster

This Cluster implementation uses AWS EC2 as the cloud provider.

This cluster works with AWS Instances that are defined in: flambe.remote.instance.aws

Parameters:

• name (str) – The unique name for the cluster
• factories_num (int) – The amount of factories to use. This is not the amount of workers, as each factories can contain multiple GPUs and therefore, multiple workers.
• factories_type (str) – The type of instance to use for the Factory Instances. GPU instances are required for AWS the AWSCluster. “p2” and “p3” instances are recommended.
• factory_ami (str) – The AMI to be used for the Factory instances. Custom Flambe AMI are provided based on Ubuntu 18.04 distribution.
• orchestrator_type (str) – The type of instance to use for the Orchestrator Instances. This may not be a GPU instances. At least a “t2.small” instance is recommended.
• key_name (str) – The key name that will be used to connect into the instance.
• creator (str) – The creator should be a user identifier for the instances. This information will create a tag called ‘creator’ and it will also be used to retrieve existing hosts owned by the user.
• key (str) – The path to the ssh key used to communicate to all instances. IMPORTANT: all instances must be accessible with the same key.
• volume_type (str) – The type of volume in AWS to use. Only ‘gp2’ and ‘io1’ are currently available. If ‘io1’ is used, then IOPS will be fixed to 5000. IMPORTANT: ‘io1’ volumes are significantly more expensive than ‘gp2’ volumes. Defaults to ‘gp2’.
• region_name (Optional[str]) – The region name to use. If not specified, it uses the locally configured region name or ‘us-east-1’ in case it’s not configured.
• username (str) – The username of the instances the cluster will handle. Defaults to ‘ubuntu’. IMPORTANT: for now all instances need to have the same username.
• tags (Dict[str, str]) – A dictionary with tags that will be added to all created hosts.
• security_group (str) – The security group to use to create the instances.
• subnet_id (str) – The subnet ID to use.
• orchestrator_ami (str) – The AMI to be used for the Factory instances. Custom Flambe AMI are provided based on Ubuntu 18.04 distribution.
• dedicated (bool) – Wether all created instances are dedicated instances or shared.
• orchestrator_timeout (int) – Number of consecutive hours before terminating the orchestrator once the experiment is over (either success of failure). Specify -1 to disable automatic shutdown (the orchestrator will stay on until manually terminated) and 0 to shutdown when the experiment is over. For example, if specifying 24, then the orchestrator will be shut down one day after the experiment is over. ATTENTION: This also applies when the experiment ends with an error. Default is -1.
• factories_timeout (int) – Number of consecutive hours to automatically terminate factories once the experiment is over (either success or failure). Specify -1 to disable automatic shutdown (the factories will stay on until manually terminated) and 0 to shutdown when the experiment is over. For example, if specifying 10, then the factories will be shut down 10 hours after the experiment is over. ATTENTION: This also applies when the experiment ends with an error. Default is 1.
• volume_size (int) – The disk size in GB that all hosts will contain. Defaults to 100 GB.
• setup_cmds (Optional[List[str]]) – A list of commands to be run on all hosts for setup purposes. These commands can be used to mount volumes, install software, etc. Defaults to None. IMPORTANT: the commands need to be idempotent and they shouldn’t expect user input.

_get_boto_session(self, region_name: Optional[str])

Get the boto3 Session from which the resources and clients will be created.

This method is called by the contructor.

Parameters:region_name (Optional[str]) – The region to use. If None, boto3 will resolve to the locally configured region_name or ‘us-east-1’ if not configured. Returns:The boto3 Session to use Return type:boto3.Session

load_all_instances(self)

Launch all instances for the experiment.

This method launches both the orchestrator and the factories.

_existing_cluster(self)

Whether there is an existing cluster that matches name.

The cluster should also match all other tags, including Creator)

Returns:Returns the (boto_orchestrator, [boto_factories]) that match the experiment’s name. Return type:Tuple[Any, List[Any]]

_get_existing_tags(self, boto_instance: boto3.resources.factory.ec2.Instance)

Gets the tags of a EC2 instances

Parameters:boto_instance (BotoIns) – The EC2 instance to access the tags. Returns:Key, Value for the specified tags. Return type:Dict[str, str]

flambe_own_running_instances(self)

Get running instances with matching tags.

Yields:Tuple[‘boto3.resources.factory.ec2.Instance’, str] – A tuple with the instance and the name of the EC2 instance.

name_hosts(self)

Name the orchestrator and factories.

_get_all_tags(self)

Get user tags + default tags to add to the instances and volumes.

update_tags(self)

Update user provided tags to all hosts.

In case there is an existing cluster that do not contain all the tags, by executing this all hosts will have the user specified tags.

This won’t remove existing tags in the hosts.

_update_tags(self, boto_instance: boto3.resources.factory.ec2.Instance, tags: Dict[str, str])

Create/Overwrite tags on an EC2 instance and its volumes.

Parameters:

• boto_instance ('boto3.resources.factory.ec2.Instance') – The EC2 instance
• tags (Dict[str, str]) – The tags to create/overwrite

name_instance(self, boto_instance: boto3.resources.factory.ec2.Instance, name: str)

Renames a EC2 instance

Parameters:

• boto_instance ('boto3.resources.factory.ec2.Instance') – The EC2 instance
• name (str) – The new name

_create_orchestrator(self)

Create a new EC2 instance to be the Orchestrator instance.

This new machine receives all tags defined in the *.ini file.

Returns:The new orchestrator instance. Return type:instance.AWSOrchestratorInstance

_create_factories(self, number: int = 1)

Creates new AWS EC2 instances to be the Factory instances.

These new machines receive all tags defined in the *.ini file. Factory instances will be named using the factory basename plus an index. For example, “seq2seq_factory_0”, “seq2seq_factory_1”.

Parameters:number (int) – The number of factories to be created. Returns:The new factory instances. Return type:List[instance.AWSGPUFactoryInstance]

_generic_launch_instances(self, instance_class: Type[T], number: int, instance_type: str, instance_ami: str, role: str)

Generic method to launch instances in AWS EC2 using boto3.

This method should not be used outside this module.

Parameters:

• instance_class (Type[T]) – The instance class. It can be AWSOrchestratorInstance or AWSGPUFactoryInstance.
• number (int) – The amount of instances to create
• instance_type (str) – The instance type
• instance_ami (str) – The AMI to be used. Should be an Ubuntu 18.04 based AMI.
• role (str) – Wether is ‘Orchestrator’ or ‘Factory’

Returns:

The new Instances.

Return type:

List[Union[AWSOrchestratorInstance, AWSGPUFactoryInstance]]

terminate_instances(self)

Terminates all instances.

rollback_env(self)

Rollback the environment.

This occurs when an error is caucht during the local stage of the remote experiment (i.e. creating the cluster, sending the data and submitting jobs), this method handles cleanup stages.

parse(self)

Checks if the AWSCluster configuration is valid.

This checks that the factories are never terminated after the orchestrator is. Avoids the scenario where the cluster has only factories and no orchestrator, which is useless.

Raises:errors.ClusterConfigurationError – If configuration is not valid.

_get_boto_instance_by_host(self, public_host: str)

Returns the instance id given the public host

Parameters:public_host (str) – The host in IP format of DNS format Returns:The id if found else None Return type:Optional[boto3.resources.factory.ec2.Instance]

_get_instance_id_by_host(self, public_host: str)

Returns the instance id given the public host

Parameters:public_host (str) – The host in IP format of DNS format Returns:The id if found else None Return type:Optional[str]

_get_alarm_name(self, instance_id: str)

Get the alarm name to be used for the given instance.

Parameters:instance_id (str) – The id of the instance Returns:The name of the corresponding alarm Return type:str

has_alarm(self, instance_id: str)

Whether the instance has an alarm set.

Parameters:instance_id (str) – The id of the instance Returns:True if an alarm is set. False otherwise. Return type:bool

remove_existing_events(self)

Remove the current alarm.

In case the orchestrator or factories had an alarm, we remove it to reset the new policies.

create_cloudwatch_events(self)

Creates cloudwatch events for orchestrator and factories.

_delete_cloudwatch_event(self, instance_id: str)

Deletes the alarm related to the instance.

_put_fake_cloudwatch_data(self, instance_id: str, value: int = 100, points: int = 10)

Put fake CPU Usage metric in an instance.

This method is useful to avoid triggering alarms when they are created. For example, is an instance was idle for 10 hours and an termination alarm is set for 5 hours, it will be triggered immediately. Adding a fake point will allow the alarms to start the timer from the current moment.

Parameters:

• instance_id (str) – The ID of the EC2 instance
• value (int) – The CPU percent value to use. Defaults to 100
• points (int) – The amount of past minutes from the current time to generate metric points. For example, if points is 10, then 10 data metrics will be generated for the past 10 minutes, one per minute.

_create_cloudwatch_event(self, instance_id: str, mins: int = 60, cpu_thresh: float = 0.1)

Create CloudWatch alarm.

The alrm is used to terminate an instance based on CPU usage.

Parameters:

• instance_id (str) – The ID of the EC2 instance
• mins (int) – Number of minutes to trigger the termination event. The evaluation preriod will be always one minute.
• cpu_thresh (float) – Percentage specifying upper bound for triggering event. If mins is 60 and cpu_thresh is 0.1, then this instance will be deleted after 1 hour of average CPU below 0.1.

_get_images(self)

Get the official AWS public AMIs created by Flambe.

ATTENTION: why not just search the tags? We need to make sure the AMIs we pick were created by the Flambe team. Because of tags values not being unique, anyone can create a public AMI with ‘Creator: flambe@asapp.com’ as a tag. If we pick that AMI, then we could potentially be Creating instances with unknown AMIs, causing potential security issues. By filtering by our acount id (which can be public), then we can make sure that all AMIs that are being scanned were created by Flambe team.

Returns:The boto3 API response Return type:Dict

_get_ami(self, _type: str, version: str)

Given a type and a version, get the correct Flambe AMI.

IMPORTANT: we keep the version logic in case we add versioned AMIs in the future.

Parameters:

• _type (str) – It can be either ‘factory’ or ‘orchestrator’. Note that the type is lowercase in the AMI tag.
• version (str) – For example, “0.2.1” or “2.0”.

Returns:

Return type:

The ImageId if it’s found. None if not.

_find_default_ami(self, _type: str)

Returns an AMI with version 0.0.0, which is the default. This means that doesn’t contain flambe itself but it has some heavy dependencies already installed (like pytorch).

Parameters:_type (str) – Wether is “orchestrator” or “factory” Returns:The ImageId or None if not found. Return type:Optional[str]

class flambe.cluster.SSHCluster(name: str, orchestrator_ip: Union[str, List[str]], factories_ips: Union[List[str], List[List[str]]], key: str, username: str, remote_context=None, use_public: bool = True, setup_cmds: Optional[List[str]] = None)

Bases: flambe.cluster.cluster.Cluster

The SSH Manager needs to be used when having running instances.

For example when having on-prem hardware or just a couple of AWS EC2 instances running.

When using this cluster, the user needs to specify the IPs of the machines to use, both the public one and private one.

load_all_instances(self, exp_name: str = None, force: bool = False)

This manager assumed that instances are running.

This method loads the Python objects to the manager’s variables.

Parameters:

• exp_name (str) – The name of the experiment
• force (bool) – Whether to override the current experiment of the same name

rollback_env(self)

rsync_hosts(self)

Rsyncs the host’s result folders.

First, it rsyncs all worker folders to the orchestrator main folder. After that, so that every worker gets the last changes, the orchestrator rsync with all of them.

flambe.compile

Submodules

flambe.compile.component

Module Contents

flambe.compile.component._EMPTY

flambe.compile.component.A

flambe.compile.component.C

flambe.compile.component.YAML_TYPES

flambe.compile.component.logger

exception flambe.compile.component.CompilationError

Bases: Exception

exception flambe.compile.component.LoadError

Bases: Exception

class flambe.compile.component.Schema(component_subclass: Type[C], _flambe_custom_factory_name: Optional[str] = None, **keywords: Any)

Bases: MutableMapping[str, Any]

Holds and recursively initializes Component’s with kwargs

Holds a Component subclass and keyword arguments to that class’s compile method. When an instance is called it will perform the recursive compilation process, turning the nested structure of Schema’s into initialized Component objects

Implements MutableMapping methods to facilitate inspection and updates to the keyword args. Implements dot-notation access to the keyword args as well.

Parameters:

• component_subclass (Type[Component]) – Subclass of Component that will be compiled
• **keywords (Any) – kwargs passed into the Schema’s compile method

Examples

Create a Schema from a Component subclass

>>> class Test(Component):
...     def __init__(self, x=2):
...         self.x = x
...
>>> tp = Schema(Test)
>>> t1 = tp()
>>> t2 = tp()
>>> assert t1 is t2  # the same Schema always gives you same obj
>>> tp = Schema(Test) # create a new Schema
>>> tp['x'] = 3
>>> t3 = tp()
>>> assert t1.x == 3  # dot notation works as well

component_subclass

Subclass of Schema that will be compiled

Type:Type[Component]

keywords

kwargs passed into the Schema’s compile method

Type:Dict[str, Any]

__call__(self, stash: Optional[Dict[str, Any]] = None, **keywords: Any)

add_extensions_metadata(self, extensions: Dict[str, str])

Add extensions used when loading this schema and children

Uses component_subclass.__module__ to filter for only the single relevant extension for this object; extensions relevant for children are saved only on those children schemas directly. Use aggregate_extensions_metadata to generate a dictionary of all extensions used in the object hierarchy.

aggregate_extensions_metadata(self)

Aggregate extensions used in object hierarchy

__setitem__(self, key: str, value: Any)

__getitem__(self, key: str)

__delitem__(self, key: str)

__iter__(self)

__len__(self)

__getattr__(self, item: str)

__setattr__(self, key: str, value: Any)

__repr__(self)

Identical to super (schema), but sorts keywords

classmethod to_yaml(cls, representer: Any, node: Any, tag: str = '')

static serialize(obj: Any)

Return dictionary representation of schema

Includes yaml as a string, and extensions

Parameters:obj (Any) – Should be schema or dict of schemas Returns:dictionary containing yaml and extensions dictionary Return type:Dict[str, Any]

static deserialize(data: Dict[str, Any])

Construct Schema from dict returned by Schema.serialize

Parameters:data (Dict[str, Any]) – dictionary returned by Schema.serialize Returns:Schema or dict of schemas (depending on yaml in data) Return type:Any

flambe.compile.component._link_root_obj

flambe.compile.component._link_prefix

flambe.compile.component._link_context_active = False

flambe.compile.component._link_obj_stash :Dict[str, Any]

class flambe.compile.component.contextualized_linking(root_obj: Any, prefix: str)

Context manager used to change the representation of links

Links are always defined in relation to some root object and an attribute path, so when representing some piece of a larger object all the links need to be redefined in relation to the target object

__enter__(self)

__exit__(self, exc_type: Any, exc_value: Any, traceback: Any)

class flambe.compile.component.PickledDataLink(obj_id: str)

Bases: flambe.compile.registrable.Registrable

__call__(self, stash: Dict[str, Any])

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

classmethod from_yaml(cls, constructor: Any, node: Any, factory_name: str)

class flambe.compile.component.Link(ref: str, obj: Optional[Any] = None, local: bool = True)

Bases: flambe.compile.registrable.Registrable

Represent a dependency in your object hierarchy

A Link delays the access of some property, or the calling of some method, until the Link is called. Links can be passed directly into a Component subclass compile, Component’s method called compile will automatically record the links and call them to access their values before running __new__ and __init__. The recorded links will show up in the config if yaml.dump() is called on your object hierarchy. This typically happens when logging individual configs during a grid search, and when serializing between multiple processes

Parameters:

• ref (str) – Period separated list of keywords starting with the block id and ending at the target attribute. For example, b1.model.encoder.hidden_size.
• obj (Optional[Any]) – Object named by ref’s first keyword
• local (bool) – if true, changes tune convert behavior to insert a dummy link; used for links to global variables (“resources” in config)

ref

Period separated list of keywords starting with the block id and ending at the target attribute.

Type:str

var_name

The name of the class of obj

Type:str

attr

Attribute of obj that will be accessed

Type:List[str]

obj

Object containing the attribute or method to link. If it is a Schema it will be compiled when the Link is called if necessary

Type:Any

local

if true, changes tune convert behavior to insert a dummy link; used for links to global variables (“resources” in config)

Type:bool

__repr__(self)

__call__(self)

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

Build contextualized link based on the root node

If the link refers to something inside of the current object hierarchy (as determined by the global prefix _link_prefix) then it will be represented as a link; if the link refers to something out-of-scope, i.e. not inside the current object hiearchy, then replace the link with the resolved value. If the value cannot be represented throw an exception.

Raises:RepresenterError – If the link is “out-of-scope” and the value cannot be represented in YAML

classmethod from_yaml(cls, constructor: Any, node: Any, factory_name: str)

convert(self)

class flambe.compile.component.FunctionCallLink

Bases: flambe.compile.component.Link

Calls the link attribute instead of just accessing it

__call__(self)

flambe.compile.component.activate_links(kwargs: Dict[str, Any]) → Dict[str, Any]

Iterate through items in dictionary and activate any `Link`s

Parameters:kwargs (Dict[str, Any]) – A dictionary of kwargs that may contain instances of Link Returns:Copy of the original dictionay with all Links activated Return type:Dict[str, Any]

Examples

Process a dictionary with Links

>>> class A(Component):
...     def __init__(self, x=2):
...         self.x = x
...
>>> a = A(x=1)
>>> kwargs = {'kw1': 0, 'kw2': Link("ref_for_a.x", obj=a)}
>>> activate_links(kwargs)
{'kw1': 0, 'kw2': 1}

flambe.compile.component.activate_stash_refs(kwargs: Dict[str, Any], stash: Dict[str, Any]) → Dict[str, Any]

Activate the pickled data links using the loaded stash

flambe.compile.component.fill_defaults(kwargs: Dict[str, Any], function: Callable[..., Any]) → Dict[str, Any]

Use function signature to add missing kwargs to a dictionary

flambe.compile.component.merge_kwargs(kwargs: Dict[str, Any], compiled_kwargs: Dict[str, Any]) → Dict[str, Any]

Replace non links in kwargs with corresponding compiled values

For every key in kwargs if the value is NOT a link and IS a Schema, replace with the corresponding value in compiled_kwargs

Parameters:

• kwargs (Dict[str, Any]) – Original kwargs containing Links and Schemas
• compiled_kwargs (Dict[str, Any]) – Processes kwargs containing no links and no Schemas

Returns:

kwargs with links, but with Schemas replaced by compiled objects

Return type:

Dict[str, Any]

class flambe.compile.component.Component(**kwargs)

Bases: flambe.compile.registrable.Registrable

Class which can be serialized to yaml and implements compile

IMPORTANT: ALWAYS inherit from Component BEFORE torch.nn.Module

Automatically registers subclasses via Registrable and facilitates immediate usage in YAML with tags. When loaded, subclasses’ initialization is delayed; kwargs are wrapped in a custom schema called Schema that can be easily initialized later.

_flambe_version = 0.0.0

_config_str

Represent object’s architecture as a YAML string

Includes the extensions relevant to the object as well; NOTE: currently this section may include a superset of the extensions actually needed, but this will be changed in a future release.

run(self)

Run a single computational step.

When used in an experiment, this computational step should be on the order of tens of seconds to about 10 minutes of work on your intended hardware; checkpoints will be performed in between calls to run, and resources or search algorithms will be updated. If you want to run everything all at once, make sure a single call to run does all the work and return False.

Returns:True if should continue running later i.e. more work to do Return type:bool

metric(self)

Override this method to enable scheduling and searching.

Returns:The metric to compare different variants of your Component Return type:float

register_attrs(self, *names: str)

Set attributes that should be included in state_dict

Equivalent to overriding obj._state and obj._load_state to save and load these attributes. Recommended usage: call inside __init__ at the end: self.register_attrs(attr1, attr2, …) Should ONLY be called on existing attributes.

Parameters:*names (str) – The names of the attributes to register Raises:AttributeError – If self does not have existing attribute with that name

static _state_dict_hook(self, state_dict: State, prefix: str, local_metadata: Dict[str, Any])

Add metadata and recurse on Component children

This hook is used to integrate with the PyTorch state_dict mechanism; as either nn.Module.state_dict or Component.get_state recurse, this hook is responsible for adding Flambe specific metadata and recursing further on any Component children of self that are not also nn.Modules, as PyTorch will handle recursing to the latter.

Flambe specific metadata includes the class version specified in the Component._flambe_version class property, the name of the class, the source code, and the fact that this class is a Component and should correspond to a directory in our hiearchical save format

Finally, this hook calls a helper _state that users can implement to add custom state to a given class

Parameters:

• state_dict (State) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• prefix (str) – The current prefix for new compound keys that reflects the location of this instance in the object hierarchy being represented
• local_metadata (Dict[str, Any]) – A subset of the metadata relevant just to this object and its children

Returns:

The modified state_dict

Return type:

type

Raises:

ExceptionName – Why the exception is raised.

_add_registered_attrs(self, state_dict: State, prefix: str)

_state(self, state_dict: State, prefix: str, local_metadata: Dict[str, Any])

Add custom state to state_dict

Parameters:

• state_dict (State) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• prefix (str) – The current prefix for new compound keys that reflects the location of this instance in the object hierarchy being represented
• local_metadata (Dict[str, Any]) – A subset of the metadata relevant just to this object and its children

Returns:

The modified state_dict

Return type:

State

get_state(self, destination: Optional[State] = None, prefix: str = '', keep_vars: bool = False)

Extract PyTorch compatible state_dict

Adds Flambe specific properties to the state_dict, including special metadata (the class version, source code, and class name). By default, only includes state that PyTorch nn.Module includes (Parameters, Buffers, child Modules). Custom state can be added via the _state helper method which subclasses should override.

The metadata _flambe_directories indicates which objects are Components and should be a subdirectory in our hierarchical save format. This object will recurse on Component and nn.Module children, but NOT torch.optim.Optimizer subclasses, torch.optim.lr_scheduler._LRScheduler subclasses, or any other arbitrary python objects.

Parameters:

• destination (Optional[State]) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• prefix (str) – The current prefix for new compound keys that reflects the location of this instance in the object hierarchy being represented
• keep_vars (bool) – Whether or not to keep Variables (only used by PyTorch) (the default is False).

Returns:

The state_dict object

Return type:

State

Raises:

ExceptionName – Why the exception is raised.

_load_state_dict_hook(self, state_dict: State, prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[Any], unexpected_keys: List[Any], error_msgs: List[Any])

Load flambe-specific state

Parameters:

• state_dict (State) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• prefix (str) – The current prefix for new compound keys that reflects the location of this instance in the object hierarchy being represented
• local_metadata (Dict[str, Any]) – A subset of the metadata relevant just to this object and its children
• strict (bool) – Whether missing or unexpected keys should be allowed; should always be False in Flambe
• missing_keys (List[Any]) – Missing keys so far
• unexpected_keys (List[Any]) – Unexpected keys so far
• error_msgs (List[Any]) – Any error messages so far

Raises:

LoadError – If the state for some object does not have a matching major version number

_load_registered_attrs(self, state_dict: State, prefix: str)

_load_state(self, state_dict: State, prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[Any], unexpected_keys: List[Any], error_msgs: List[Any])

Load custom state (that was included via _state)

Subclasses should override this function to add custom state that isn’t normally included by PyTorch nn.Module

Parameters:

• state_dict (State) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• prefix (str) – The current prefix for new compound keys that reflects the location of this instance in the object hierarchy being represented
• local_metadata (Dict[str, Any]) – A subset of the metadata relevant just to this object and its children
• strict (bool) – Whether missing or unexpected keys should be allowed; should always be False in Flambe
• missing_keys (List[Any]) – Missing keys so far
• unexpected_keys (List[Any]) – Unexpected keys so far
• error_msgs (List[Any]) – Any error messages so far

load_state(self, state_dict: State, strict: bool = False)

Load state_dict into self

Loads state produced by get_state into the current object, recursing on child Component and nn.Module objects

Parameters:

• state_dict (State) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• strict (bool) – Whether missing or unexpected keys should be allowed; should ALWAYS be False in Flambe (the default is False).

Raises:

LoadError – If the state for some object does not have a matching major version number

classmethod load_from_path(cls, path: str, use_saved_config_defaults: bool = True, **kwargs: Any)

save(self, path: str, **kwargs: Any)

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

classmethod from_yaml(cls: Type[C], constructor: Any, node: Any, factory_name: str)

classmethod setup_dependencies(cls: Type[C], kwargs: Dict[str, Any])

Add default links to kwargs for cls; hook called in compile

For example, you may want to connect model parameters to the optimizer by default, without requiring users to specify this link in the config explicitly

Parameters:

• cls (Type[C]) – Class on which method is called
• kwargs (Dict[str, Any]) – Current kwargs that should be mutated directly to include links

classmethod precompile(cls: Type[C], **kwargs: Any)

Change kwargs before compilation occurs.

This hook is called after links have been activated, but before calling the recursive initialization process on all other objects in kwargs. This is useful in a number of cases, for example, in Trainer, we compile several objects ahead of time and move them to the GPU before compiling the optimizer, because it needs to be initialized with the model parameters after they have been moved to GPU.

Parameters:

• cls (Type[C]) – Class on which method is called
• **kwargs (Any) – Current kwargs that will be compiled and used to initialize an instance of cls after this hook is called

aggregate_extensions_metadata(self)

Aggregate extensions used in object hierarchy

TODO: remove or combine with schema implementation in refactor

classmethod compile(cls: Type[C], _flambe_custom_factory_name: Optional[str] = None, _flambe_extensions: Optional[Dict[str, str]] = None, _flambe_stash: Optional[Dict[str, Any]] = None, **kwargs: Any)

Create instance of cls after recursively compiling kwargs

Similar to normal initialization, but recursively initializes any arguments that should be compiled and allows overriding arbitrarily deep kwargs before initializing if needed. Also activates any Link instances passed in as kwargs, and saves the original kwargs for dumping to yaml later.

Parameters:**kwargs (Any) – Keyword args that should be forwarded to the initialization function (a specified factory, or the normal __new__ and __init__ methods) Returns:An instance of the class cls Return type:C

flambe.compile.component.dynamic_component(class_: Type[A], tag: str, tag_namespace: Optional[str] = None) → Type[Component]

Decorate given class, creating a dynamic Component

Creates a dynamic subclass of class_ that inherits from Component so it will be registered with the yaml loader and receive the appropriate functionality (from_yaml, to_yaml and compile). class_ should not implement any of the aforementioned functions.

Parameters:

• class (Type[A]) – Class to register with yaml and the compilation system
• tag (str) – Tag that will be used with yaml
• tag_namespace (str) – Namespace aka the prefix, used. e.g. for !torch.Adam torch is the namespace

Returns:

New subclass of _class and Component

Return type:

Type[Component]

flambe.compile.const

Module Contents

flambe.compile.const.STATE_DICT_DELIMETER = .

flambe.compile.const.FLAMBE_SOURCE_KEY = _flambe_source

flambe.compile.const.FLAMBE_CLASS_KEY = _flambe_class

flambe.compile.const.FLAMBE_CONFIG_KEY = _flambe_config

flambe.compile.const.FLAMBE_DIRECTORIES_KEY = _flambe_directories

flambe.compile.const.FLAMBE_STASH_KEY = _flambe_stash

flambe.compile.const.KEEP_VARS_KEY = keep_vars

flambe.compile.const.VERSION_KEY = _flambe_version

flambe.compile.const.HIGHEST_SERIALIZATION_PROTOCOL_VERSION = 1

flambe.compile.const.DEFAULT_SERIALIZATION_PROTOCOL_VERSION = 1

flambe.compile.const.DEFAULT_PROTOCOL = 2

flambe.compile.const.STATE_FILE_NAME = state.pt

flambe.compile.const.VERSION_FILE_NAME = version.txt

flambe.compile.const.SOURCE_FILE_NAME = source.py

flambe.compile.const.CONFIG_FILE_NAME = config.yaml

flambe.compile.const.STASH_FILE_NAME = stash.pkl

flambe.compile.const.PROTOCOL_VERSION_FILE_NAME = protocol_version.txt

flambe.compile.downloader

Module Contents

flambe.compile.downloader.logger

flambe.compile.downloader.s3_exists(url: ParseResult) → bool

Return is an S3 resource exists.

Parameters:url (ParseResult) – The parsed URL. Returns:True if it exists. False otherwise. Return type:bool

flambe.compile.downloader.s3_remote_file(url: ParseResult) → bool

Check if an S3 hosted artifact is a file or a folder.

Parameters:url (ParseResult) – The parsed URL. Returns:True if it’s a file, False if it’s a folder. Return type:bool

flambe.compile.downloader.download_s3_file(url: str, destination: str) → None

Download an S3 file.

Parameters:

• url (str) – The S3 URL. Should follow the format: ‘s3://<bucket-name>[/path/to/file]’
• destination (str) – The output file where to copy the content

flambe.compile.downloader.http_exists(url: str) → bool

Check if an HTTP/HTTPS file exists.

Parameters:url (str) – The HTTP/HTTPS URL. Returns:True if the HTTP file exists Return type:bool

flambe.compile.downloader.download_http_file(url: str, destination: str) → None

Download an HTTP/HTTPS file.

Parameters:

• url (str) – The HTTP/HTTPS URL.
• destination (str) – The output file where to copy the content. Needs to support binary writing.

flambe.compile.downloader.download_s3_folder(url: str, destination: str) → None

Download an S3 folder.

Parameters:

• url (str) – The S3 URL. Should follow the format: ‘s3://<bucket-name>[/path/to/folder]’
• destination (str) – The output folder where to copy the content

flambe.compile.downloader.download_manager(path: str)

Manager for downloading remote URLs

Parameters:path (str) – The remote URL to download. Currently, only S3 and http/https URLs are supported. In case it’s already a local path, it yields the same path.

Examples

>>> with download_manager("https://host.com/my/file.zip") as path:
>>>     os.path.exists(path)
>>> True

Yields:str – The new local path

flambe.compile.extensions

This module provides methods to orchestrate all extensions

Module Contents

flambe.compile.extensions.logger

flambe.compile.extensions.download_extensions(extensions: Dict[str, str], container_folder: str) → Dict[str, str]

Iterate through the extensions and download the remote urls.

Parameters:

• extensions (Dict[str, str]) – The extensions that may contain both local or remote locations.
• container_folder (str) – The auxiliary folder where to download the remote repo

Returns:

A new extensions dict with the local paths instead of remote urls. The local paths contain the downloaded remote resources.

Return type:

Dict[str, str]

flambe.compile.extensions._download_remote_extension(extension_url: str, location: str) → str

Download a remote hosted extension.

It fully supports github urls only (for now).

Parameters:

• extension_url (str) – The github url pointing to an extension. For example: https://github.com/user/folder/tree/branch/path/to/ext
• location (str) – The location to download the repo

Returns:

The location of the installed package (which it could not match the location passed as parameter)

Return type:

str

flambe.compile.extensions._has_svn() → bool

Return if the host has svn installed

flambe.compile.extensions._download_svn(svn_url: str, location: str, username: Optional[str] = None, password: Optional[str] = None) → None

Use svn to download a specific folder inside a git repo.

This works only with remote Github repositories.

Parameters:

• svn_url (str) – The github URL adapted to use the SVN protocol
• location (str) – The location to download the folder
• username (str) – The username
• password (str) – The password

flambe.compile.extensions.install_extensions(extensions: Dict[str, str], user_flag: bool = False) → None

Install extensions.

At this point, all extensions must be either local paths or valid pypi packages.

Remote extensions hosted in Github must have been download first.

Parameters:

• extensions (Dict[str, str]) – Dictionary of extensions
• user_flag (bool) – Use –user flag when running pip install

flambe.compile.extensions.is_installed_module(module_name: str) → bool

Whether the module is installed.

Parameters:module_name (str) – The name of the module to check for Returns:True if the module is installed locally, False otherwise. Return type:bool

flambe.compile.extensions.import_modules(modules: Iterable[str]) → None

Dinamically import modules

Parameters:modules (Iterable[str]) – An iterable of strings containing the modules to import

flambe.compile.extensions.setup_default_modules()

flambe.compile.registrable

Module Contents

flambe.compile.registrable.logger

flambe.compile.registrable.yaml

flambe.compile.registrable._reg_prefix :Optional[str]

flambe.compile.registrable.R

flambe.compile.registrable.A

flambe.compile.registrable.RT

exception flambe.compile.registrable.RegistrationError

Bases: Exception

Error thrown when acessing yaml tag on a non-registered class

Thrown when trying to access the default yaml tag for a class typically occurs when called on an abstract class

flambe.compile.registrable.make_from_yaml_with_metadata(from_yaml_fn: Callable[..., Any], tag: str, factory_name: Optional[str] = None) → Callable[..., Any]

flambe.compile.registrable.make_to_yaml_with_metadata(to_yaml_fn: Callable[..., Any]) → Callable[..., Any]

class flambe.compile.registrable.registration_context(namespace: str)

__enter__(self)

__exit__(self, *args: Any)

__call__(self, func: Callable[..., Any])

class flambe.compile.registrable.Registrable

Bases: abc.ABC

Subclasses automatically registered as yaml tags

Automatically registers subclasses with the yaml loader by adding a constructor and representer which can be overridden

_yaml_tags :Dict[str, List[str]]

_yaml_tag_namespace :Dict[str, str]

_yaml_registered_factories :Set[str]

classmethod __init_subclass__(cls: Type[R], should_register: Optional[bool] = True, tag_override: Optional[str] = None, tag_namespace: Optional[str] = None, **kwargs: Mapping[str, Any])

static register_tag(class_: RT, tag: str, tag_namespace: Optional[str] = None)

static get_default_tag(class_: RT, factory_name: Optional[str] = None)

Retrieve default yaml tag for class cls

Retrieve the default tag (aka the last one, which will be the only one, or the alias if it exists) for use in yaml representation

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

Use representer to create yaml representation of node

See Component class, and experiment/options for examples

classmethod from_yaml(cls, constructor: Any, node: Any, factory_name: str)

Use constructor to create an instance of cls

See Component class, and experiment/options for examples

flambe.compile.registrable.alias(tag: str, tag_namespace: Optional[str] = None) → Callable[[RT], RT]

Decorate a Registrable subclass with a new tag

Can be added multiple times to give a class multiple aliases, however the top most alias tag will be the default tag which means it will be used when representing the class in YAML

flambe.compile.registrable.register(cls: Type[A], tag: str) → Type[A]

Safely register a new tag for a class

Similar to alias, but it’s intended to be used on classes that are not already subclasses of Registrable, and it is NOT a decorator

class flambe.compile.registrable.registrable_factory(fn: Any)

Decorate Registrable factory method for use in the config

This Descriptor class will set properties that allow the factory method to be specified directly in the config as a suffix to the tag; for example:

class MyModel(Component):

    @registrable_factory
    def from_file(cls, path):
        # load instance from path
        ...
        return instance

defines the factory, which can then be used in yaml:

model: !MyModel.from_file
    path: some/path/to/file.pt

__set_name__(self, owner: type, name: str)

class flambe.compile.registrable.MappedRegistrable

Bases: flambe.compile.registrable.Registrable

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

Use representer to create yaml representation of node

classmethod from_yaml(cls, constructor: Any, node: Any, factory_name: str)

Use constructor to create an instance of cls

flambe.compile.serialization

Module Contents

flambe.compile.serialization.logger

exception flambe.compile.serialization.LoadError

Bases: Exception

Error thrown because of fatal error when loading

class flambe.compile.serialization.SaveTreeNode

Bases: typing.NamedTuple

Tree representation corresponding to the directory save format

state :Dict[str, Any]

version :str

class_name :str

source_code :str

config :str

object_stash :Dict[str, Any]

children :Dict[str, Any]

class flambe.compile.serialization.State

Bases: collections.OrderedDict

A state object for Flambe.

_metadata :Dict[str, Any]

flambe.compile.serialization._convert_to_tree(metadata: Dict[str, Any]) → SaveTreeNode

flambe.compile.serialization._update_save_tree(save_tree: SaveTreeNode, key: Sequence[str], value: Any) → None

flambe.compile.serialization._traverse_all_nodes(save_tree: SaveTreeNode, path: Optional[List[str]] = None) → Iterable[Tuple[List[str], SaveTreeNode]]

flambe.compile.serialization._extract_prefix(root, directory)

flambe.compile.serialization._prefix_keys(state, prefix)

flambe.compile.serialization.traverse(nested: Mapping[str, Any], path: Optional[List[str]] = None) → Iterable[Any]

Iterate over a nested mapping returning the path and key, value.

Parameters:

• nested (Mapping[str, Any]) – Mapping where some values are also mappings that should be traversed
• path (List[str]) – List of keys that were used to reach the current mapping

Returns:

Iterable of path, key, value triples

Return type:

Iterable[Any]

flambe.compile.serialization._update_link_refs(schema: Mapping) → None

Resolve links in schemas at block_id.

Parameters:schema (Dict[str, Schema[Any]]) – Map from block_id to Schema object

flambe.compile.serialization.save_state_to_file(state: State, path: str, compress: bool = False, pickle_only: bool = False, pickle_module=dill, pickle_protocol=DEFAULT_PROTOCOL) → None

Save state to given path

By default the state will be saved in directory structure that mirrors the object hierarchy, so that you can later inspect the save file and load individual components more easily. If you would like to compress this directory structure using tar + gz, set compress to True. You can also use pickle to write a single output file, more similar to how PyTorch’s save function operates.

Parameters:

• state (State) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• path (str) – Location to save the file / save directory to
• compress (bool) – Whether to compress the save file / directory via tar + gz
• pickle_only (bool) – Use given pickle_module instead of the hiearchical save format (the default is False).
• pickle_module (type) – Pickle module that has load and dump methods; dump should accpet a pickle_protocol parameter (the default is dill).
• pickle_protocol (type) – Pickle protocol to use; see pickle for more details (the default is 2).

flambe.compile.serialization.save(obj: Any, path: str, compress: bool = False, pickle_only: bool = False, pickle_module=dill, pickle_protocol=DEFAULT_PROTOCOL) → None

Save Component object to given path

See save_state_to_file for a more detailed explanation

Parameters:

• obj (Component) – The component to save.
• path (str) – Location to save the file / save directory to
• compress (bool) – Whether to compress the save file / directory via tar + gz
• pickle_only (bool) – Use given pickle_module instead of the hiearchical save format (the default is False).
• pickle_module (type) – Pickle module that has load and dump methods; dump should accept a pickle_protocol parameter (the default is dill).
• pickle_protocol (type) – Pickle protocol to use; see pickle for more details (the default is 2).

flambe.compile.serialization.load_state_from_file(path: str, map_location=None, pickle_module=dill, **pickle_load_args) → State

Load state from the given path

Loads a flambe save directory, pickled save object, or a compressed version of one of these two formats (using tar + gz). Will automatically infer the type of save format and if the directory structure is used, the serialization protocol version as well.

Parameters:

• path (str) – Path to the save file or directory
• map_location (type) – Location (device) where items will be moved. ONLY used when the directory save format is used. See torch.load documentation for more details (the default is None).
• pickle_module (type) – Pickle module that has load and dump methods; dump should accept a pickle_protocol parameter (the default is dill).
• **pickle_load_args (type) – Additional args that pickle_module should use to load; see torch.load documentation for more details

Returns:

state_dict that can be loaded into a compatible Component

Return type:

State

flambe.compile.serialization.load(path: str, map_location=None, auto_install=False, pickle_module=dill, **pickle_load_args)

Load object with state from the given path

Loads a flambe object by using the saved config files, and then loads the saved state into said object. See load_state_from_file for details regarding how the state is loaded from the save file or directory.

Parameters:

• path (str) – Path to the save file or directory
• map_location (type) – Location (device) where items will be moved. ONLY used when the directory save format is used. See torch.load documentation for more details (the default is None).
• auto_install (bool) – If True, automatically installs extensions as needed.
• pickle_module (type) – Pickle module that has load and dump methods; dump should accept a pickle_protocol parameter (the default is dill).
• **pickle_load_args (type) – Additional args that pickle_module should use to load; see torch.load documentation for more details

Returns:

object with both the architecture (config) and state that was saved to path

Return type:

Component

Raises:

LoadError – If a Component object is not loadable from the given path because extensions are not installed, or the config is empty, nonexistent, or otherwise invalid.

flambe.compile.utils

Module Contents

flambe.compile.utils.all_subclasses(class_: Type[Any]) → Set[Type[Any]]

Return a set of all subclasses for a given class object

Recursively collects all subclasses of class_ down the object hierarchy into one set.

Parameters:class (Type[Any]) – Class to retrieve all subclasses for Returns:All subclasses of class_ Return type:Set[Type[Any]]

flambe.compile.utils.make_component(class_: type, tag_namespace: str, only_module: Optional[str] = None) → None

Make class and all its children a Component

For example a call to make_component(torch.optim.Adam, “torch”) will make the tag !torch.Adam accessible in any yaml configs. This does NOT monkey patch (aka swizzle) torch, but instead creates a dynamic subclass which will be used by the yaml constructor i.e. only classes loaded from the config will be affected, anything imported and used in code.

Parameters:

• class (type) – To be registered with yaml as a component, along with all its children
• tag_prefix (str) – Added to beginning of all the tags
• only_module (str) – Module prefix used to limit the scope of what gets registered

Returns:

Return type:

None

flambe.compile.utils._is_url(resource: str) → bool

Whether a given resource is a remote URL.

Resolve by searching for a scheme.

Parameters:resource (str) – The given resource Returns:If the resource is a remote URL. Return type:bool

Package Contents

exception flambe.compile.RegistrationError

Bases: Exception

Error thrown when acessing yaml tag on a non-registered class

Thrown when trying to access the default yaml tag for a class typically occurs when called on an abstract class

class flambe.compile.Registrable

Bases: abc.ABC

Subclasses automatically registered as yaml tags

Automatically registers subclasses with the yaml loader by adding a constructor and representer which can be overridden

_yaml_tags :Dict[str, List[str]]

_yaml_tag_namespace :Dict[str, str]

_yaml_registered_factories :Set[str]

classmethod __init_subclass__(cls: Type[R], should_register: Optional[bool] = True, tag_override: Optional[str] = None, tag_namespace: Optional[str] = None, **kwargs: Mapping[str, Any])

static register_tag(class_: RT, tag: str, tag_namespace: Optional[str] = None)

static get_default_tag(class_: RT, factory_name: Optional[str] = None)

Retrieve default yaml tag for class cls

Retrieve the default tag (aka the last one, which will be the only one, or the alias if it exists) for use in yaml representation

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

Use representer to create yaml representation of node

See Component class, and experiment/options for examples

classmethod from_yaml(cls, constructor: Any, node: Any, factory_name: str)

Use constructor to create an instance of cls

See Component class, and experiment/options for examples

flambe.compile.alias(tag: str, tag_namespace: Optional[str] = None) → Callable[[RT], RT]

Decorate a Registrable subclass with a new tag

Can be added multiple times to give a class multiple aliases, however the top most alias tag will be the default tag which means it will be used when representing the class in YAML

flambe.compile.yaml

flambe.compile.register(cls: Type[A], tag: str) → Type[A]

Safely register a new tag for a class

Similar to alias, but it’s intended to be used on classes that are not already subclasses of Registrable, and it is NOT a decorator

class flambe.compile.registrable_factory(fn: Any)

Decorate Registrable factory method for use in the config

This Descriptor class will set properties that allow the factory method to be specified directly in the config as a suffix to the tag; for example:

class MyModel(Component):

    @registrable_factory
    def from_file(cls, path):
        # load instance from path
        ...
        return instance

defines the factory, which can then be used in yaml:

model: !MyModel.from_file
    path: some/path/to/file.pt

__set_name__(self, owner: type, name: str)

class flambe.compile.registration_context(namespace: str)

__enter__(self)

__exit__(self, *args: Any)

__call__(self, func: Callable[..., Any])

class flambe.compile.MappedRegistrable

Bases: flambe.compile.registrable.Registrable

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

Use representer to create yaml representation of node

classmethod from_yaml(cls, constructor: Any, node: Any, factory_name: str)

Use constructor to create an instance of cls

class flambe.compile.Schema(component_subclass: Type[C], _flambe_custom_factory_name: Optional[str] = None, **keywords: Any)

Bases: MutableMapping[str, Any]

Holds and recursively initializes Component’s with kwargs

Holds a Component subclass and keyword arguments to that class’s compile method. When an instance is called it will perform the recursive compilation process, turning the nested structure of Schema’s into initialized Component objects

Implements MutableMapping methods to facilitate inspection and updates to the keyword args. Implements dot-notation access to the keyword args as well.

Parameters:

• component_subclass (Type[Component]) – Subclass of Component that will be compiled
• **keywords (Any) – kwargs passed into the Schema’s compile method

Examples

Create a Schema from a Component subclass

>>> class Test(Component):
...     def __init__(self, x=2):
...         self.x = x
...
>>> tp = Schema(Test)
>>> t1 = tp()
>>> t2 = tp()
>>> assert t1 is t2  # the same Schema always gives you same obj
>>> tp = Schema(Test) # create a new Schema
>>> tp['x'] = 3
>>> t3 = tp()
>>> assert t1.x == 3  # dot notation works as well

component_subclass

Subclass of Schema that will be compiled

Type:Type[Component]

keywords

kwargs passed into the Schema’s compile method

Type:Dict[str, Any]

__call__(self, stash: Optional[Dict[str, Any]] = None, **keywords: Any)

add_extensions_metadata(self, extensions: Dict[str, str])

Add extensions used when loading this schema and children

Uses component_subclass.__module__ to filter for only the single relevant extension for this object; extensions relevant for children are saved only on those children schemas directly. Use aggregate_extensions_metadata to generate a dictionary of all extensions used in the object hierarchy.

aggregate_extensions_metadata(self)

Aggregate extensions used in object hierarchy

__setitem__(self, key: str, value: Any)

__getitem__(self, key: str)

__delitem__(self, key: str)

__iter__(self)

__len__(self)

__getattr__(self, item: str)

__setattr__(self, key: str, value: Any)

__repr__(self)

Identical to super (schema), but sorts keywords

classmethod to_yaml(cls, representer: Any, node: Any, tag: str = '')

static serialize(obj: Any)

Return dictionary representation of schema

Includes yaml as a string, and extensions

Parameters:obj (Any) – Should be schema or dict of schemas Returns:dictionary containing yaml and extensions dictionary Return type:Dict[str, Any]

static deserialize(data: Dict[str, Any])

Construct Schema from dict returned by Schema.serialize

Parameters:data (Dict[str, Any]) – dictionary returned by Schema.serialize Returns:Schema or dict of schemas (depending on yaml in data) Return type:Any

class flambe.compile.Component(**kwargs)

Bases: flambe.compile.registrable.Registrable

Class which can be serialized to yaml and implements compile

IMPORTANT: ALWAYS inherit from Component BEFORE torch.nn.Module

Automatically registers subclasses via Registrable and facilitates immediate usage in YAML with tags. When loaded, subclasses’ initialization is delayed; kwargs are wrapped in a custom schema called Schema that can be easily initialized later.

_flambe_version = 0.0.0

_config_str

Represent object’s architecture as a YAML string

Includes the extensions relevant to the object as well; NOTE: currently this section may include a superset of the extensions actually needed, but this will be changed in a future release.

run(self)

Run a single computational step.

When used in an experiment, this computational step should be on the order of tens of seconds to about 10 minutes of work on your intended hardware; checkpoints will be performed in between calls to run, and resources or search algorithms will be updated. If you want to run everything all at once, make sure a single call to run does all the work and return False.

Returns:True if should continue running later i.e. more work to do Return type:bool

metric(self)

Override this method to enable scheduling and searching.

Returns:The metric to compare different variants of your Component Return type:float

register_attrs(self, *names: str)

Set attributes that should be included in state_dict

Equivalent to overriding obj._state and obj._load_state to save and load these attributes. Recommended usage: call inside __init__ at the end: self.register_attrs(attr1, attr2, …) Should ONLY be called on existing attributes.

Parameters:*names (str) – The names of the attributes to register Raises:AttributeError – If self does not have existing attribute with that name

static _state_dict_hook(self, state_dict: State, prefix: str, local_metadata: Dict[str, Any])

Add metadata and recurse on Component children

This hook is used to integrate with the PyTorch state_dict mechanism; as either nn.Module.state_dict or Component.get_state recurse, this hook is responsible for adding Flambe specific metadata and recursing further on any Component children of self that are not also nn.Modules, as PyTorch will handle recursing to the latter.

Flambe specific metadata includes the class version specified in the Component._flambe_version class property, the name of the class, the source code, and the fact that this class is a Component and should correspond to a directory in our hiearchical save format

Finally, this hook calls a helper _state that users can implement to add custom state to a given class

Parameters:

• state_dict (State) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• prefix (str) – The current prefix for new compound keys that reflects the location of this instance in the object hierarchy being represented
• local_metadata (Dict[str, Any]) – A subset of the metadata relevant just to this object and its children

Returns:

The modified state_dict

Return type:

type

Raises:

ExceptionName – Why the exception is raised.

_add_registered_attrs(self, state_dict: State, prefix: str)

_state(self, state_dict: State, prefix: str, local_metadata: Dict[str, Any])

Add custom state to state_dict

Parameters:

• state_dict (State) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• prefix (str) – The current prefix for new compound keys that reflects the location of this instance in the object hierarchy being represented
• local_metadata (Dict[str, Any]) – A subset of the metadata relevant just to this object and its children

Returns:

The modified state_dict

Return type:

State

get_state(self, destination: Optional[State] = None, prefix: str = '', keep_vars: bool = False)

Extract PyTorch compatible state_dict

Adds Flambe specific properties to the state_dict, including special metadata (the class version, source code, and class name). By default, only includes state that PyTorch nn.Module includes (Parameters, Buffers, child Modules). Custom state can be added via the _state helper method which subclasses should override.

The metadata _flambe_directories indicates which objects are Components and should be a subdirectory in our hierarchical save format. This object will recurse on Component and nn.Module children, but NOT torch.optim.Optimizer subclasses, torch.optim.lr_scheduler._LRScheduler subclasses, or any other arbitrary python objects.

Parameters:

• destination (Optional[State]) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• prefix (str) – The current prefix for new compound keys that reflects the location of this instance in the object hierarchy being represented
• keep_vars (bool) – Whether or not to keep Variables (only used by PyTorch) (the default is False).

Returns:

The state_dict object

Return type:

State

Raises:

ExceptionName – Why the exception is raised.

_load_state_dict_hook(self, state_dict: State, prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[Any], unexpected_keys: List[Any], error_msgs: List[Any])

Load flambe-specific state

Parameters:

• state_dict (State) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• prefix (str) – The current prefix for new compound keys that reflects the location of this instance in the object hierarchy being represented
• local_metadata (Dict[str, Any]) – A subset of the metadata relevant just to this object and its children
• strict (bool) – Whether missing or unexpected keys should be allowed; should always be False in Flambe
• missing_keys (List[Any]) – Missing keys so far
• unexpected_keys (List[Any]) – Unexpected keys so far
• error_msgs (List[Any]) – Any error messages so far

Raises:

LoadError – If the state for some object does not have a matching major version number

_load_registered_attrs(self, state_dict: State, prefix: str)

_load_state(self, state_dict: State, prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[Any], unexpected_keys: List[Any], error_msgs: List[Any])

Load custom state (that was included via _state)

Subclasses should override this function to add custom state that isn’t normally included by PyTorch nn.Module

Parameters:

• state_dict (State) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• prefix (str) – The current prefix for new compound keys that reflects the location of this instance in the object hierarchy being represented
• local_metadata (Dict[str, Any]) – A subset of the metadata relevant just to this object and its children
• strict (bool) – Whether missing or unexpected keys should be allowed; should always be False in Flambe
• missing_keys (List[Any]) – Missing keys so far
• unexpected_keys (List[Any]) – Unexpected keys so far
• error_msgs (List[Any]) – Any error messages so far

load_state(self, state_dict: State, strict: bool = False)

Load state_dict into self

Loads state produced by get_state into the current object, recursing on child Component and nn.Module objects

Parameters:

• state_dict (State) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• strict (bool) – Whether missing or unexpected keys should be allowed; should ALWAYS be False in Flambe (the default is False).

Raises:

LoadError – If the state for some object does not have a matching major version number

classmethod load_from_path(cls, path: str, use_saved_config_defaults: bool = True, **kwargs: Any)

save(self, path: str, **kwargs: Any)

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

classmethod from_yaml(cls: Type[C], constructor: Any, node: Any, factory_name: str)

classmethod setup_dependencies(cls: Type[C], kwargs: Dict[str, Any])

Add default links to kwargs for cls; hook called in compile

For example, you may want to connect model parameters to the optimizer by default, without requiring users to specify this link in the config explicitly

Parameters:

• cls (Type[C]) – Class on which method is called
• kwargs (Dict[str, Any]) – Current kwargs that should be mutated directly to include links

classmethod precompile(cls: Type[C], **kwargs: Any)

Change kwargs before compilation occurs.

This hook is called after links have been activated, but before calling the recursive initialization process on all other objects in kwargs. This is useful in a number of cases, for example, in Trainer, we compile several objects ahead of time and move them to the GPU before compiling the optimizer, because it needs to be initialized with the model parameters after they have been moved to GPU.

Parameters:

• cls (Type[C]) – Class on which method is called
• **kwargs (Any) – Current kwargs that will be compiled and used to initialize an instance of cls after this hook is called

aggregate_extensions_metadata(self)

Aggregate extensions used in object hierarchy

TODO: remove or combine with schema implementation in refactor

classmethod compile(cls: Type[C], _flambe_custom_factory_name: Optional[str] = None, _flambe_extensions: Optional[Dict[str, str]] = None, _flambe_stash: Optional[Dict[str, Any]] = None, **kwargs: Any)

Create instance of cls after recursively compiling kwargs

Similar to normal initialization, but recursively initializes any arguments that should be compiled and allows overriding arbitrarily deep kwargs before initializing if needed. Also activates any Link instances passed in as kwargs, and saves the original kwargs for dumping to yaml later.

Parameters:**kwargs (Any) – Keyword args that should be forwarded to the initialization function (a specified factory, or the normal __new__ and __init__ methods) Returns:An instance of the class cls Return type:C

class flambe.compile.Link(ref: str, obj: Optional[Any] = None, local: bool = True)

Bases: flambe.compile.registrable.Registrable

Represent a dependency in your object hierarchy

A Link delays the access of some property, or the calling of some method, until the Link is called. Links can be passed directly into a Component subclass compile, Component’s method called compile will automatically record the links and call them to access their values before running __new__ and __init__. The recorded links will show up in the config if yaml.dump() is called on your object hierarchy. This typically happens when logging individual configs during a grid search, and when serializing between multiple processes

Parameters:

• ref (str) – Period separated list of keywords starting with the block id and ending at the target attribute. For example, b1.model.encoder.hidden_size.
• obj (Optional[Any]) – Object named by ref’s first keyword
• local (bool) – if true, changes tune convert behavior to insert a dummy link; used for links to global variables (“resources” in config)

ref

Period separated list of keywords starting with the block id and ending at the target attribute.

Type:str

var_name

The name of the class of obj

Type:str

attr

Attribute of obj that will be accessed

Type:List[str]

obj

Object containing the attribute or method to link. If it is a Schema it will be compiled when the Link is called if necessary

Type:Any

local

if true, changes tune convert behavior to insert a dummy link; used for links to global variables (“resources” in config)

Type:bool

__repr__(self)

__call__(self)

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

Build contextualized link based on the root node

If the link refers to something inside of the current object hierarchy (as determined by the global prefix _link_prefix) then it will be represented as a link; if the link refers to something out-of-scope, i.e. not inside the current object hiearchy, then replace the link with the resolved value. If the value cannot be represented throw an exception.

Raises:RepresenterError – If the link is “out-of-scope” and the value cannot be represented in YAML

classmethod from_yaml(cls, constructor: Any, node: Any, factory_name: str)

convert(self)

flambe.compile.dynamic_component(class_: Type[A], tag: str, tag_namespace: Optional[str] = None) → Type[Component]

Decorate given class, creating a dynamic Component

Creates a dynamic subclass of class_ that inherits from Component so it will be registered with the yaml loader and receive the appropriate functionality (from_yaml, to_yaml and compile). class_ should not implement any of the aforementioned functions.

Parameters:

• class (Type[A]) – Class to register with yaml and the compilation system
• tag (str) – Tag that will be used with yaml
• tag_namespace (str) – Namespace aka the prefix, used. e.g. for !torch.Adam torch is the namespace

Returns:

New subclass of _class and Component

Return type:

Type[Component]

flambe.compile.make_component(class_: type, tag_namespace: str, only_module: Optional[str] = None) → None

Make class and all its children a Component

For example a call to make_component(torch.optim.Adam, “torch”) will make the tag !torch.Adam accessible in any yaml configs. This does NOT monkey patch (aka swizzle) torch, but instead creates a dynamic subclass which will be used by the yaml constructor i.e. only classes loaded from the config will be affected, anything imported and used in code.

Parameters:

• class (type) – To be registered with yaml as a component, along with all its children
• tag_prefix (str) – Added to beginning of all the tags
• only_module (str) – Module prefix used to limit the scope of what gets registered

Returns:

Return type:

None

flambe.compile.all_subclasses(class_: Type[Any]) → Set[Type[Any]]

Return a set of all subclasses for a given class object

Recursively collects all subclasses of class_ down the object hierarchy into one set.

Parameters:class (Type[Any]) – Class to retrieve all subclasses for Returns:All subclasses of class_ Return type:Set[Type[Any]]

flambe.compile.save(obj: Any, path: str, compress: bool = False, pickle_only: bool = False, pickle_module=dill, pickle_protocol=DEFAULT_PROTOCOL) → None

Save Component object to given path

See save_state_to_file for a more detailed explanation

Parameters:

• obj (Component) – The component to save.
• path (str) – Location to save the file / save directory to
• compress (bool) – Whether to compress the save file / directory via tar + gz
• pickle_only (bool) – Use given pickle_module instead of the hiearchical save format (the default is False).
• pickle_module (type) – Pickle module that has load and dump methods; dump should accept a pickle_protocol parameter (the default is dill).
• pickle_protocol (type) – Pickle protocol to use; see pickle for more details (the default is 2).

flambe.compile.load(path: str, map_location=None, auto_install=False, pickle_module=dill, **pickle_load_args)

Load object with state from the given path

Loads a flambe object by using the saved config files, and then loads the saved state into said object. See load_state_from_file for details regarding how the state is loaded from the save file or directory.

Parameters:

• path (str) – Path to the save file or directory
• map_location (type) – Location (device) where items will be moved. ONLY used when the directory save format is used. See torch.load documentation for more details (the default is None).
• auto_install (bool) – If True, automatically installs extensions as needed.
• pickle_module (type) – Pickle module that has load and dump methods; dump should accept a pickle_protocol parameter (the default is dill).
• **pickle_load_args (type) – Additional args that pickle_module should use to load; see torch.load documentation for more details

Returns:

object with both the architecture (config) and state that was saved to path

Return type:

Component

Raises:

LoadError – If a Component object is not loadable from the given path because extensions are not installed, or the config is empty, nonexistent, or otherwise invalid.

flambe.compile.save_state_to_file(state: State, path: str, compress: bool = False, pickle_only: bool = False, pickle_module=dill, pickle_protocol=DEFAULT_PROTOCOL) → None

Save state to given path

By default the state will be saved in directory structure that mirrors the object hierarchy, so that you can later inspect the save file and load individual components more easily. If you would like to compress this directory structure using tar + gz, set compress to True. You can also use pickle to write a single output file, more similar to how PyTorch’s save function operates.

Parameters:

• state (State) – The state_dict as defined by PyTorch; a flat dictionary with compound keys separated by ‘.’
• path (str) – Location to save the file / save directory to
• compress (bool) – Whether to compress the save file / directory via tar + gz
• pickle_only (bool) – Use given pickle_module instead of the hiearchical save format (the default is False).
• pickle_module (type) – Pickle module that has load and dump methods; dump should accpet a pickle_protocol parameter (the default is dill).
• pickle_protocol (type) – Pickle protocol to use; see pickle for more details (the default is 2).

flambe.compile.load_state_from_file(path: str, map_location=None, pickle_module=dill, **pickle_load_args) → State

Load state from the given path

Loads a flambe save directory, pickled save object, or a compressed version of one of these two formats (using tar + gz). Will automatically infer the type of save format and if the directory structure is used, the serialization protocol version as well.

Parameters:

• path (str) – Path to the save file or directory
• map_location (type) – Location (device) where items will be moved. ONLY used when the directory save format is used. See torch.load documentation for more details (the default is None).
• pickle_module (type) – Pickle module that has load and dump methods; dump should accept a pickle_protocol parameter (the default is dill).
• **pickle_load_args (type) – Additional args that pickle_module should use to load; see torch.load documentation for more details

Returns:

state_dict that can be loaded into a compatible Component

Return type:

State

class flambe.compile.State

Bases: collections.OrderedDict

A state object for Flambe.

_metadata :Dict[str, Any]

flambe.experiment

Subpackages

flambe.experiment.webapp

Submodules

flambe.experiment.webapp.app

Report site using Flambe

The report site is the main control centre for the remote experiment. It’s responsible of showing the current status of the experiment and to provide the resources that the experiment is producing (metrics, trained models, etc.)

The website consumes the files that flambe outputs, it doesn’t require extra running resource (like for example, Redis DB).

The website is implemented using Flask.

Important: no typing is provided for now.

Module Contents

flambe.experiment.webapp.app.app

flambe.experiment.webapp.app.load_state()

Get status about the blocks (runned, running, remaining)

flambe.experiment.webapp.app.analyze_download_params(block, variant)

flambe.experiment.webapp.app.download(block=None, variant=None)

Downloads models + logs stored in the filesystem of the Orchestrator

flambe.experiment.webapp.app.download_logs(block=None, variant=None)

Downloads all artifacts but the models stored in the filesystem of the Orchestrator machine

flambe.experiment.webapp.app.stream()

flambe.experiment.webapp.app.state()

flambe.experiment.webapp.app.console_log()

View console with the logs

flambe.experiment.webapp.app.index()

Main endpoint. Works with the index.html template.

Submodules

flambe.experiment.experiment

Module Contents

flambe.experiment.experiment.logger

flambe.experiment.experiment.OptionalSearchAlgorithms

flambe.experiment.experiment.OptionalTrialSchedulers

class flambe.experiment.experiment.Experiment(name: str, pipeline: Dict[str, Schema], resume: Optional[Union[str, Sequence[str]]] = None, debug: bool = False, devices: Dict[str, int] = None, save_path: Optional[str] = None, resources: Optional[Dict[str, Dict[str, Any]]] = None, search: OptionalSearchAlgorithms = None, schedulers: OptionalTrialSchedulers = None, reduce: Optional[Dict[str, int]] = None, env: RemoteEnvironment = None, max_failures: int = 2, merge_plot: bool = True)

Bases: flambe.runnable.ClusterRunnable

A Experiment object.

The Experiment object is the top level module in the Flambé workflow. The object is responsible for starting workers, assiging the orchestrator machine, as well as converting the input blocks into Ray Tune Experiment objects.

Parameters:

• name (str) – A name for the experiment
• pipeline (OrderedDict[str, Schema[Component]]) – Ordered mapping from block id to a schema of the block
• force (bool) – When running a local experiment this flag will make flambe override existing results from previous experiments. When running remote experiments this flag will reuse an existing cluster (in case of any) that is running an experiment with the same name in the same cloud service. The use of this flag is discouraged as you may lose useful data.
• resume (Union[str, List[str]]) – If a string is given, resume all blocks up until the given block_id. If a list is given, resume all blocks in that list.
• debug (bool) – If debug is True, then a debugger will be available at the beginning of each Component block of the pipeline. Defaults to False. ATTENTION: debug only works when running locally.
• save_path (Optional[str]) – A directory where to save the experiment.
• devices (Dict[str, int]) – Tune’s resources per trial. For example: {“cpu”: 12, “gpu”: 2}.
• resources (Optional[Dict[str, Dict[str, Any]]]) – Variables to use in the pipeline section with !@ notation. This section is splitted into 2 sections: local and remote.
• search (Mapping[str, SearchAlgorithm], optional) – Map from block id to hyperparameter search space generator. May have Schemas of SearchAlgorithm as well.
• schedulers (Mapping[str, TrialScheduler], optional) – Map from block id to search scheduler. May have Schemas of TrialScheduler as well.
• reduce (Mapping[str, int], optional) – Map from block to number of trials to reduce to.
• env (RemoteEnvironment) – Contains remote information about the cluster. This object will be received in case this Experiment is running remotely.
• max_failures (int) – number of times to retry running the pipeline if it hits some type of failure, defaults to retrying twice
• merge_plot (bool) – Display all tensorboard logs in the same plot (per block type). Defaults to True.

run(self, force: bool = False, verbose: bool = False, **kwargs)

Run an Experiment

setup(self, cluster: Cluster, extensions: Dict[str, str], force: bool, **kwargs)

Prepare the cluster for the Experiment remote execution.

This involves:

1. [Optional] Kill previous flambe execution
2. [Optional] Remove existing results
3. Create supporting dirs (exp/synced_results, exp/resources)
4. Install extensions in all factories
5. Launch ray cluster
6. Send resources
7. Launch Tensorboard + Report site

Parameters:

• cluster (Cluster) – The cluster where this Runnable will be running
• extensions (Dict[str, str]) – The ClusterRunnable extensions
• force (bool) – The force value provided to Flambe

parse(self)

Parse the experiment.

Parse the Experiment in search of errors that won’t allow the experiment to run successfully. If it finds any error, then it raises an ParsingExperimentError.

Raises:ParsingExperimentError – In case a parsing error is found.

flambe.experiment.options

Module Contents

flambe.experiment.options.Number

class flambe.experiment.options.Options

Bases: flambe.compile.Registrable, abc.ABC

classmethod from_sequence(cls, options: Sequence[Any])

Construct an options class from a sequence of values

Parameters:options (Sequence[Any]) – Discrete sequence that defines what values to search over Returns:Returns a subclass of DiscreteOptions Return type:T

convert(self)

Convert the options to Ray Tune representation.

Returns:The Ray Tune conversion Return type:Dict

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

classmethod from_yaml(cls, constructor: Any, node: Any, factory_name: str)

class flambe.experiment.options.GridSearchOptions(elements: Sequence[Any])

Bases: Sequence[Any], flambe.experiment.options.Options

Discrete set of values used for grid search

Defines a finite, discrete set of values to be substituted at the location where the set currently resides in the config

classmethod from_sequence(cls, options: Sequence[Any])

convert(self)

__getitem__(self, key: Any)

__len__(self)

__repr__(self)

class flambe.experiment.options.SampledUniformSearchOptions(low: Number, high: Number, k: int, decimals: int = 10)

Bases: Sequence[Number], flambe.experiment.options.Options

Yields k values from the range (low, high)

Randomly yields k values from the range (low, high) to be substituted at the location where the class currently resides in the config

classmethod from_sequence(cls, options: Sequence[Any])

convert(self)

__getitem__(self, key: Any)

__len__(self)

__repr__(self)

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

flambe.experiment.progress

Module Contents

class flambe.experiment.progress.ProgressState(name, save_path, dependency_dag, factories_num=0)

checkpoint_start(self, block_id)

refresh(self)

checkpoint_end(self, block_id, checkpoints, block_success)

finish(self)

_save(self)

toJSON(self)

flambe.experiment.tune_adapter

Module Contents

class flambe.experiment.tune_adapter.TuneAdapter

Bases: ray.tune.Trainable

Adapter to the tune.Trainable inteface.

_setup(self, config: Dict)

Subclasses should override this for custom initialization.

save(self, checkpoint_dir: Optional[str] = None)

Override to replace checkpoint.

_train(self)

Subclasses should override this to implement train().

_save(self, checkpoint_dir: str)

Subclasses should override this to implement save().

_restore(self, checkpoint: str)

Subclasses should override this to implement restore().

_stop(self)

Subclasses should override this for any cleanup on stop.

flambe.experiment.utils

Module Contents

flambe.experiment.utils.check_links(blocks: Dict[str, Schema], global_vars: Optional[Dict[str, Any]] = None) → None

Check validity of links between blocks.

Ensures dependency order, and that only Comparable blocks are being reduced through a LinkBest object.

Parameters:

blocks (OrderedDict[str, Schema[Component]]) – The blocks to check, in order

Raises:

• LinkError – On undeclared blocks (i.e not the right config order)
• ProtocolError – Attempt to reduce a non-comparable block

flambe.experiment.utils.check_search(blocks: Dict[str, Schema], search: Mapping[str, SearchAlgorithm], schedulers: Mapping[str, TrialScheduler])

Check validity of links between blocks.

Ensures dependency order, and that only Comparable blocks are being reduced through a LinkBest object.

Parameters:

• blocks (OrderedDict[str, Schema[Component]]) – Ordered mapping from block id to a schema of the block
• search (Mapping[str, SearchAlgorithm], optional) – Map from block id to hyperparameter search space generator
• schedulers (Mapping[str, TrialScheduler], optional) – Map from block id to search scheduler

Raises:

• ProtocolError – Non computable block assigned a search or scheduler.
• ProtocolError – Non comparable block assigned a non default search or scheduler

flambe.experiment.utils.convert_tune(blocks: Mapping[str, Schema])

Convert the options and links in the block.

Convert Option objects to tune.grid_search or tune.sample_from functions, depending on the type.

Parameters:

• blocks (OrderedDict[str, Schema[Component]]) – The input blocks
• block_id (The block id of the current block) –

flambe.experiment.utils.traverse(nested: Mapping[str, Any], path: Optional[List[str]] = None) → Iterable[Any]

Iterate over a nested mapping returning the path and key, value.

Parameters:

• nested (Mapping[str, Any]) – Mapping where some values are also mappings that should be traversed
• path (List[str]) – List of keys that were used to reach the current mapping

Returns:

Iterable of path, key, value triples

Return type:

Iterable[Any]

flambe.experiment.utils.traverse_spec(nested: Mapping[str, Any], path: Optional[List[str]] = None) → Iterable[Any]

Iterate over a nested mapping returning the path and key, value.

Parameters:

• nested (Mapping[str, Any]) – Mapping where some values are also mappings that should be traversed
• path (List[str]) – List of keys that were used to reach the current mapping

Returns:

Iterable of path, key, value triples

Return type:

Iterable[Any]

flambe.experiment.utils.update_nested(nested: MutableMapping[str, Any], prefix: Iterable[str], key: str, new_value: Any) → None

Multi-level set operation for nested mapping.

Parameters:

• nested (Mapping[str, Any]) – Nested dictionary where keys are all strings
• prefix (Iterable[str]) – List of keys specifying path to value to be updated
• key (str) – Final key corresponding to value to be updated
• new_value (Any) – New value to set for [p1]…[key] in nested

flambe.experiment.utils.get_nested(nested: Mapping[str, Any], prefix: Iterable[str], key: str) → Any

Get nested value in standard Mapping.

Parameters:

• nested (Mapping[str, Any]) – The mapping to index in
• prefix (Iterable[str]) – The path to the final key in the nested input
• key (str) – The key to query

Returns:

The value at the given path and key

Return type:

Any

flambe.experiment.utils.update_schema_with_params(schema: Schema, params: Dict[str, Any]) → Schema

Replace options in the schema recursivly.

Parameters:

• schema (Schema[Any]) – The schema object to update
• params (Dict[str, Any]) – The corresponding nested diciontary with values

Returns:

The update schema (same object as the input, not a copy)

Return type:

Schema[Any]

flambe.experiment.utils.has_schemas_or_options(x: Any) → bool

Check if object contains Schemas or Options.

Recurses for Mappings and Sequences

Parameters:x (Any) – Input object to check for Schemas and Options Returns:True iff contains any Options or Schemas. Return type:bool

flambe.experiment.utils.divide_nested_grid_search_options(config: MutableMapping[str, Any]) → Iterable[Mapping[str, Any]]

Divide config into a config Iterable to remove nested Options.

For every GridSearchOptions or SampledUniformSearchOptions, if any values contain more Options or Schemas, create copies with a single value selected in place of the option. Resulting configs will have no nested options.

Parameters:config (MutableMapping[str, Any]) – MutableMapping (or Schema) containing Options and Schemas Returns:Each Mapping contains variants from original config without nested options Return type:Iterable[Mapping[str, Any]]

flambe.experiment.utils.extract_dict(config: Mapping[str, Any]) → Dict[str, Any]

Turn the schema into a dictionary, ignoring types.

NOTE: We recurse if any value is itself a Schema, a Sequence of Schema`s, or a `Mapping of `Schema`s. Other unconvential collections will not be inspected.

Parameters:schema (Schema) – The object to be converted into a dictionary Returns:The output dictionary representation. Return type:Dict

flambe.experiment.utils.extract_needed_blocks(schemas: Dict[str, Schema], block_id: str, global_vars: Optional[Dict[str, Any]] = None) → Set[str]

Returns the set of all blocks that the input block links to.

Parameters:

• schemas (Dict[str, Schema[Any]]) – Map from block_id to Schema object
• block_id (str) – The block containing links

Returns:

The list of ancestor block ids

Return type:

List[str]

flambe.experiment.utils.update_link_refs(schemas: Dict[str, Schema], block_id: str, global_vars: Dict[str, Any]) → None

Resolve links in schemas at block_id.

Parameters:

• schemas (Dict[str, Schema[Any]]) – Map from block_id to Schema object
• block_id (str) – The block where links should be activated
• global_vars (Dict[str, Any]) – The environment links (ex: resources)

flambe.experiment.utils.get_best_trials(trials: List[Trial], topk: int, metric='episode_reward_mean') → List[Trial]

Get the trials with the best result.

Parameters:

• trials (List[ray.tune.Trial]) – The list of trials to examine
• topk (int) – The number of trials to reduce to
• metric (str, optional) – The metric used in comparaison (higher is better)

Returns:

The list of best trials

Return type:

List[ray.tune.Trial]

flambe.experiment.utils.get_non_remote_config(experiment)

Returns a copy of the original config file without the remote configuration

Parameters:experiment (Experiment) – The experiment object

flambe.experiment.utils.local_has_gpu() → bool

Returns is local process has GPU

Returns: Return type:bool

flambe.experiment.utils.rel_to_abs_paths(d: Dict[str, str]) → Dict[str, str]

Convert relative paths to absolute paths.

Parameters:d (Dict[str, str]) – A dict from name -> path. Returns:The same dict received as parameter with relative paths replaced with absolute. Return type:Dict[str, str]

flambe.experiment.utils.shutdown_ray_node() → int

Call ‘ray stop’ locally to terminate the ray node.

flambe.experiment.utils.shutdown_remote_ray_node(host: str, user: str, key: str) → int

Execute ‘ray stop’ on a remote machine through ssh to terminate the ray node.

IMPORTANT: this method is intended to be run in the cluster.

Parameters:

• host (str) – The Orchestrator’s IP that is visible by the factories (usually the private IP)
• user (str) – The username for that machine.
• key (str) – The key that communicate with the machine.

flambe.experiment.wording

Module Contents

flambe.experiment.wording.logger

flambe.experiment.wording.print_useful_local_info(full_save_path) → None

Information to display before experiment is running.

flambe.experiment.wording.print_useful_remote_info(manager, experiment_name) → None

Once the local process of the remote run is over, this information is shown to the user.

flambe.experiment.wording.print_useful_metrics_only_info() → None

Printable warning when debug flag is active

flambe.experiment.wording.print_extensions_cache_size_warning(location, limit) → None

Print message when the extensions cache folder is getting big.

Package Contents

class flambe.experiment.Experiment(name: str, pipeline: Dict[str, Schema], resume: Optional[Union[str, Sequence[str]]] = None, debug: bool = False, devices: Dict[str, int] = None, save_path: Optional[str] = None, resources: Optional[Dict[str, Dict[str, Any]]] = None, search: OptionalSearchAlgorithms = None, schedulers: OptionalTrialSchedulers = None, reduce: Optional[Dict[str, int]] = None, env: RemoteEnvironment = None, max_failures: int = 2, merge_plot: bool = True)

Bases: flambe.runnable.ClusterRunnable

A Experiment object.

The Experiment object is the top level module in the Flambé workflow. The object is responsible for starting workers, assiging the orchestrator machine, as well as converting the input blocks into Ray Tune Experiment objects.

Parameters:

• name (str) – A name for the experiment
• pipeline (OrderedDict[str, Schema[Component]]) – Ordered mapping from block id to a schema of the block
• force (bool) – When running a local experiment this flag will make flambe override existing results from previous experiments. When running remote experiments this flag will reuse an existing cluster (in case of any) that is running an experiment with the same name in the same cloud service. The use of this flag is discouraged as you may lose useful data.
• resume (Union[str, List[str]]) – If a string is given, resume all blocks up until the given block_id. If a list is given, resume all blocks in that list.
• debug (bool) – If debug is True, then a debugger will be available at the beginning of each Component block of the pipeline. Defaults to False. ATTENTION: debug only works when running locally.
• save_path (Optional[str]) – A directory where to save the experiment.
• devices (Dict[str, int]) – Tune’s resources per trial. For example: {“cpu”: 12, “gpu”: 2}.
• resources (Optional[Dict[str, Dict[str, Any]]]) – Variables to use in the pipeline section with !@ notation. This section is splitted into 2 sections: local and remote.
• search (Mapping[str, SearchAlgorithm], optional) – Map from block id to hyperparameter search space generator. May have Schemas of SearchAlgorithm as well.
• schedulers (Mapping[str, TrialScheduler], optional) – Map from block id to search scheduler. May have Schemas of TrialScheduler as well.
• reduce (Mapping[str, int], optional) – Map from block to number of trials to reduce to.
• env (RemoteEnvironment) – Contains remote information about the cluster. This object will be received in case this Experiment is running remotely.
• max_failures (int) – number of times to retry running the pipeline if it hits some type of failure, defaults to retrying twice
• merge_plot (bool) – Display all tensorboard logs in the same plot (per block type). Defaults to True.

run(self, force: bool = False, verbose: bool = False, **kwargs)

Run an Experiment

setup(self, cluster: Cluster, extensions: Dict[str, str], force: bool, **kwargs)

Prepare the cluster for the Experiment remote execution.

This involves:

1. [Optional] Kill previous flambe execution
2. [Optional] Remove existing results
3. Create supporting dirs (exp/synced_results, exp/resources)
4. Install extensions in all factories
5. Launch ray cluster
6. Send resources
7. Launch Tensorboard + Report site

Parameters:

• cluster (Cluster) – The cluster where this Runnable will be running
• extensions (Dict[str, str]) – The ClusterRunnable extensions
• force (bool) – The force value provided to Flambe

parse(self)

Parse the experiment.

Parse the Experiment in search of errors that won’t allow the experiment to run successfully. If it finds any error, then it raises an ParsingExperimentError.

Raises:ParsingExperimentError – In case a parsing error is found.

class flambe.experiment.ProgressState(name, save_path, dependency_dag, factories_num=0)

checkpoint_start(self, block_id)

refresh(self)

checkpoint_end(self, block_id, checkpoints, block_success)

finish(self)

_save(self)

toJSON(self)

class flambe.experiment.TuneAdapter

Bases: ray.tune.Trainable

Adapter to the tune.Trainable inteface.

_setup(self, config: Dict)

Subclasses should override this for custom initialization.

save(self, checkpoint_dir: Optional[str] = None)

Override to replace checkpoint.

_train(self)

Subclasses should override this to implement train().

_save(self, checkpoint_dir: str)

Subclasses should override this to implement save().

_restore(self, checkpoint: str)

Subclasses should override this to implement restore().

_stop(self)

Subclasses should override this for any cleanup on stop.

class flambe.experiment.GridSearchOptions(elements: Sequence[Any])

Bases: Sequence[Any], flambe.experiment.options.Options

Discrete set of values used for grid search

Defines a finite, discrete set of values to be substituted at the location where the set currently resides in the config

classmethod from_sequence(cls, options: Sequence[Any])

convert(self)

__getitem__(self, key: Any)

__len__(self)

__repr__(self)

class flambe.experiment.SampledUniformSearchOptions(low: Number, high: Number, k: int, decimals: int = 10)

Bases: Sequence[Number], flambe.experiment.options.Options

Yields k values from the range (low, high)

Randomly yields k values from the range (low, high) to be substituted at the location where the class currently resides in the config

classmethod from_sequence(cls, options: Sequence[Any])

convert(self)

__getitem__(self, key: Any)

__len__(self)

__repr__(self)

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

flambe.export

Submodules

flambe.export.builder

Module Contents

flambe.export.builder.logger

class flambe.export.builder.Builder(component: Schema, destination: str, storage: str = 'local', compress: bool = False, pickle_only: bool = False, pickle_module=dill, pickle_protocol=DEFAULT_PROTOCOL)

Bases: flambe.runnable.Runnable

Implement a Builder.

A builder is a simple object that can be used to create any Component post-experiment, and export it to a local or remote location.

Currently supports local, and S3 locations.

config

The secrets that the user provides. For example, ‘config[“AWS”][“ACCESS_KEY”]’

Type:configparser.ConfigParser

run(self, force: bool = False, **kwargs)

Run the Builder.

save_local(self, force)

Save an object locally.

Parameters:force (bool) – Wheter to use a non-empty folder or not

get_boto_session(self)

Get a boto Session

save_s3(self, force)

Save an object to s3 using awscli

Parameters:force (bool) – Wheter to use a non-empty bucket folder or not

flambe.export.exporter

Module Contents

class flambe.export.exporter.Exporter(**kwargs: Dict[str, Any])

Bases: flambe.Component

Implement an Exporter computable.

This object can be viewed as a dummy computable. It is useful to group objects into a block when those get save, to more easily refer to them later on, for instance in an object builder.

run(self)

Run the exporter.

Returns:False, as this is a single step Component. Return type:bool

Package Contents

class flambe.export.Builder(component: Schema, destination: str, storage: str = 'local', compress: bool = False, pickle_only: bool = False, pickle_module=dill, pickle_protocol=DEFAULT_PROTOCOL)

Bases: flambe.runnable.Runnable

Implement a Builder.

A builder is a simple object that can be used to create any Component post-experiment, and export it to a local or remote location.

Currently supports local, and S3 locations.

config

The secrets that the user provides. For example, ‘config[“AWS”][“ACCESS_KEY”]’

Type:configparser.ConfigParser

run(self, force: bool = False, **kwargs)

Run the Builder.

save_local(self, force)

Save an object locally.

Parameters:force (bool) – Wheter to use a non-empty folder or not

get_boto_session(self)

Get a boto Session

save_s3(self, force)

Save an object to s3 using awscli

Parameters:force (bool) – Wheter to use a non-empty bucket folder or not

class flambe.export.Exporter(**kwargs: Dict[str, Any])

Bases: flambe.Component

Implement an Exporter computable.

This object can be viewed as a dummy computable. It is useful to group objects into a block when those get save, to more easily refer to them later on, for instance in an object builder.

run(self)

Run the exporter.

Returns:False, as this is a single step Component. Return type:bool

flambe.field

Submodules

flambe.field.bow

Module Contents

class flambe.field.bow.BoWField(tokenizer: Optional[Tokenizer] = None, lower: bool = False, unk_token: str = '<unk>', min_freq: int = 5, normalize: bool = False, scale_factor: float = None)

Bases: flambe.field.Field

Featurize raw text inputs using bag of words (BoW)

This class performs tokenization and numericalization.

The pad, unk, when given, are assigned the first indices in the vocabulary, in that order. This means, that whenever a pad token is specified, it will always use the 0 index.

Examples

>>> f = BoWField(min_freq=2, normalize=True)
>>> f.setup(['thank you', 'thank you very much', 'thanks a lot'])
>>> f._vocab.keys()
['thank', you']

Note that ‘thank’ and ‘you’ are the only ones that appear twice.

>>> f.process("thank you really. You help was awesome")
tensor([1, 2])

vocab_size :int

Get the vocabulary length.

Returns:The length of the vocabulary Return type:int

process(self, example)

setup(self, *data)

flambe.field.field

Module Contents

class flambe.field.field.Field

Bases: flambe.Component

Base Field interface.

A field processes raw examples and produces Tensors.

setup(self, *data: np.ndarray)

Setup the field.

This method will be called with all the data in the dataset and it can be used to compute aggregated information (for example, vocabulary in Fields that process text).

ATTENTION: this method could be called multiple times in case the same field is used in different datasets. Take this into account and build a stateful implementation.

Parameters:*data (np.ndarray) – Multiple 2d arrays (ex: train_data, dev_data, test_data). First dimension is for the examples, second dimension for the columns specified for this specific field.

process(self, *example: Any)

Process an example into a Tensor or tuple of Tensor.

This method allows N to M mappings from example columns (N) to tensors (M).

Parameters:*example (Any) – Column values of the example Returns:The processed example, as a tensor or tuple of tensors Return type:Union[torch.Tensor, Tuple[torch.Tensor, ..]]

flambe.field.label

Module Contents

class flambe.field.label.LabelField(one_hot: bool = False, multilabel_sep: Optional[str] = None, labels: Optional[Sequence[str]] = None)

Bases: flambe.field.field.Field

Featurizes input labels.

The class also handles multilabel inputs and one hot encoding.

vocab_size :int

Get the vocabulary length.

Returns:The length of the vocabulary Return type:int

label_count :torch.Tensor

Get the label count.

Returns:Tensor containing the count for each label, indexed by the id of the label in the vocabulary. Return type:torch.Tensor

label_freq :torch.Tensor

Get the frequency of each label.

Returns:Tensor containing the frequency of each label, indexed by the id of the label in the vocabulary. Return type:torch.Tensor

label_inv_freq :torch.Tensor

Get the inverse frequency for each label.

Returns:Tensor containing the inverse frequency of each label, indexed by the id of the label in the vocabulary. Return type:torch.Tensor

setup(self, *data: np.ndarray)

Build the vocabulary.

Parameters:data (Iterable[str]) – List of input strings.

process(self, example)

Featurize a single example.

Parameters:example (str) – The input label Returns:A list of integer tokens Return type:torch.Tensor

flambe.field.text

Module Contents

class flambe.field.text.TextField(tokenizer: Optional[Tokenizer] = None, lower: bool = False, pad_token: Optional[str] = '<pad>', unk_token: Optional[str] = '<unk>', sos_token: Optional[str] = None, eos_token: Optional[str] = None, embeddings: Optional[str] = None, embeddings_format: str = 'glove', embeddings_binary: bool = False, unk_init_all: bool = False)

Bases: flambe.field.Field

Featurize raw text inputs

This class performs tokenization and numericalization, as well as decorating the input sequences with optional start and end tokens.

When a vocabulary is passed during initialiazation, it is used to map the the words to indices. However, the vocabulary can also be generated from input data, through the setup method. Once a vocabulary has been built, this object can also be used to load external pretrained embeddings.

The pad, unk, sos and eos tokens, when given, are assigned the first indices in the vocabulary, in that order. This means, that whenever a pad token is specified, it will always use the 0 index.

vocab_size :int

Get the vocabulary length.

Returns:The length of the vocabulary Return type:int

setup(self, *data: np.ndarray)

Build the vocabulary and sets embeddings.

Parameters:data (Iterable[str]) – List of input strings.

process(self, example: str)

Process an example, and create a Tensor.

Parameters:example (str) – The example to process, as a single string Returns:The processed example, tokenized and numericalized Return type:torch.Tensor

Package Contents

class flambe.field.Field

Bases: flambe.Component

Base Field interface.

A field processes raw examples and produces Tensors.

setup(self, *data: np.ndarray)

Setup the field.

This method will be called with all the data in the dataset and it can be used to compute aggregated information (for example, vocabulary in Fields that process text).

ATTENTION: this method could be called multiple times in case the same field is used in different datasets. Take this into account and build a stateful implementation.

Parameters:*data (np.ndarray) – Multiple 2d arrays (ex: train_data, dev_data, test_data). First dimension is for the examples, second dimension for the columns specified for this specific field.

process(self, *example: Any)

Process an example into a Tensor or tuple of Tensor.

This method allows N to M mappings from example columns (N) to tensors (M).

Parameters:*example (Any) – Column values of the example Returns:The processed example, as a tensor or tuple of tensors Return type:Union[torch.Tensor, Tuple[torch.Tensor, ..]]

class flambe.field.TextField(tokenizer: Optional[Tokenizer] = None, lower: bool = False, pad_token: Optional[str] = '<pad>', unk_token: Optional[str] = '<unk>', sos_token: Optional[str] = None, eos_token: Optional[str] = None, embeddings: Optional[str] = None, embeddings_format: str = 'glove', embeddings_binary: bool = False, unk_init_all: bool = False)

Bases: flambe.field.Field

Featurize raw text inputs

This class performs tokenization and numericalization, as well as decorating the input sequences with optional start and end tokens.

When a vocabulary is passed during initialiazation, it is used to map the the words to indices. However, the vocabulary can also be generated from input data, through the setup method. Once a vocabulary has been built, this object can also be used to load external pretrained embeddings.

The pad, unk, sos and eos tokens, when given, are assigned the first indices in the vocabulary, in that order. This means, that whenever a pad token is specified, it will always use the 0 index.

vocab_size :int

Get the vocabulary length.

Returns:The length of the vocabulary Return type:int

setup(self, *data: np.ndarray)

Build the vocabulary and sets embeddings.

Parameters:data (Iterable[str]) – List of input strings.

process(self, example: str)

Process an example, and create a Tensor.

Parameters:example (str) – The example to process, as a single string Returns:The processed example, tokenized and numericalized Return type:torch.Tensor

class flambe.field.BoWField(tokenizer: Optional[Tokenizer] = None, lower: bool = False, unk_token: str = '<unk>', min_freq: int = 5, normalize: bool = False, scale_factor: float = None)

Bases: flambe.field.Field

Featurize raw text inputs using bag of words (BoW)

This class performs tokenization and numericalization.

The pad, unk, when given, are assigned the first indices in the vocabulary, in that order. This means, that whenever a pad token is specified, it will always use the 0 index.

Examples

>>> f = BoWField(min_freq=2, normalize=True)
>>> f.setup(['thank you', 'thank you very much', 'thanks a lot'])
>>> f._vocab.keys()
['thank', you']

Note that ‘thank’ and ‘you’ are the only ones that appear twice.

>>> f.process("thank you really. You help was awesome")
tensor([1, 2])

vocab_size :int

Get the vocabulary length.

Returns:The length of the vocabulary Return type:int

process(self, example)

setup(self, *data)

class flambe.field.LabelField(one_hot: bool = False, multilabel_sep: Optional[str] = None, labels: Optional[Sequence[str]] = None)

Bases: flambe.field.field.Field

Featurizes input labels.

The class also handles multilabel inputs and one hot encoding.

vocab_size :int

Get the vocabulary length.

Returns:The length of the vocabulary Return type:int

label_count :torch.Tensor

Get the label count.

Returns:Tensor containing the count for each label, indexed by the id of the label in the vocabulary. Return type:torch.Tensor

label_freq :torch.Tensor

Get the frequency of each label.

Returns:Tensor containing the frequency of each label, indexed by the id of the label in the vocabulary. Return type:torch.Tensor

label_inv_freq :torch.Tensor

Get the inverse frequency for each label.

Returns:Tensor containing the inverse frequency of each label, indexed by the id of the label in the vocabulary. Return type:torch.Tensor

setup(self, *data: np.ndarray)

Build the vocabulary.

Parameters:data (Iterable[str]) – List of input strings.

process(self, example)

Featurize a single example.

Parameters:example (str) – The input label Returns:A list of integer tokens Return type:torch.Tensor

flambe.learn

Submodules

flambe.learn.distillation

Module Contents

class flambe.learn.distillation.DistillationTrainer(dataset: Dataset, train_sampler: Sampler, dev_sampler: Sampler, teacher_model: Module, student_model: Module, loss_fn: Metric, metric_fn: Metric, optimizer: Optimizer, scheduler: Optional[_LRScheduler] = None, device: Optional[str] = None, max_steps: int = 10, epoch_per_step: float = 1.0, iter_per_step: Optional[int] = None, batches_per_iter: int = 1, lower_is_better: bool = False, max_grad_norm: Optional[float] = None, max_grad_abs_val: Optional[float] = None, extra_validation_metrics: Optional[List[Metric]] = None, teacher_columns: Optional[Tuple[int, ...]] = None, student_columns: Optional[Tuple[int, ...]] = None, alpha_kl: float = 0.5, temperature: int = 1, unlabel_dataset: Optional[Dataset] = None, unlabel_sampler: Optional[Sampler] = None)

Bases: flambe.learn.Trainer

Implement a Distillation Trainer.

Perform knowledge distillation between a teacher and a student model. Note that the model outputs are expected to be raw logits. Make sure that you are not applying a softmax after the decoder. You can replace the traditional Decoder with a MLPEncoder.

_compute_loss(self, batch: Tuple[torch.Tensor, ...])

Compute the loss for a single batch

Important: the student and teacher output predictions must be the raw logits, so ensure that your decoder object is step with take_log=False.

Parameters:batch (Tuple[torch.Tensor, ..]) – The batch to train on Returns:The computed loss Return type:torch.Tensor

_aggregate_preds(self, data_iterator)

Aggregate the predicitons and targets for the dataset.

Parameters:data_iterator (Iterator) – Batches of data Returns:The predictions, and targets Return type:Tuple[torch.tensor, torch.tensor]

flambe.learn.eval

Module Contents

class flambe.learn.eval.Evaluator(dataset: Dataset, eval_sampler: Sampler, model: Module, metric_fn: Metric, eval_data: str = 'test', device: Optional[str] = None)

Bases: flambe.compile.Component

Implement an Evaluator block.

An Evaluator takes as input data, and a model and executes the evaluation. This is a single step Component object.

Parameters:

• dataset (Dataset) – The dataset to run evaluation on
• eval_sampler (Sampler) – The sampler to use over validation examples
• model (Module) – The model to train
• metric_fn (Metric) – The metric to use for evaluation
• eval_data (str) – The data split to evaluate on: one of train, val or test
• device (str, optional) – The device to use in the computation.

run(self, block_name: str = None)

Run the evaluation.

Returns:Whether the computable has completed. Return type:bool

metric(self)

Override this method to enable scheduling.

Returns:The metric to compare computable varients Return type:float

flambe.learn.script

Module Contents

class flambe.learn.script.Script(script: str, args: Dict[str, Any])

Bases: flambe.compile.Component

Implement a Script computable.

The obejct can be used to turn any script into a Flambé computable. This is useful when you want to rapidly integrate code. Note however that this computable does not enable checkpointing or linking to internal components as it does not have any attributes.

To use this object, your script needs to be in a pip installable, containing all dependencies. The script is run with the following command:

python -m script.py --arg1 value1 --arg2 value2

Parameters:

• path (str) – The script module
• args (Dict[str, Any]) – Argument dictionary

run(self)

Run the evaluation.

Returns:Report dictionary to use for logging Return type:Dict[str, float]

flambe.learn.train

Module Contents

class flambe.learn.train.Trainer(dataset: Dataset, train_sampler: Sampler, val_sampler: Sampler, model: Module, loss_fn: Metric, metric_fn: Metric, optimizer: Optimizer, scheduler: Optional[_LRScheduler] = None, device: Optional[str] = None, max_steps: int = 10, epoch_per_step: float = 1.0, iter_per_step: Optional[int] = None, batches_per_iter: int = 1, lower_is_better: bool = False, max_grad_norm: Optional[float] = None, max_grad_abs_val: Optional[float] = None, extra_validation_metrics: Optional[List[Metric]] = None)

Bases: flambe.compile.Component

Implement a Trainer block.

A Trainer takes as input data, model and optimizer, and executes training incrementally in run.

Note that it is important that a trainer run be long enough to not increase overhead, so at least a few seconds, and ideally multiple minutes.

_batch_to_device(self, batch: Tuple[torch.Tensor, ...])

Move the current batch on the correct device.

Can be overriden if a batch doesn’t follow the expected structure. For example if the batch is a dictionary.

Parameters:batch (Tuple[torch.Tensor, ..]) – The batch to train on.

_compute_loss(self, batch: Tuple[torch.Tensor, ...])

Compute the loss given a single batch

Parameters:batch (Tuple[torch.Tensor, ..]) – The batch to train on.

_train_step(self)

Run a training step over the training data.

_aggregate_preds(self, data_iterator: Iterator)

Aggregate the predicitons and targets for the dataset.

Parameters:data_iterator (Iterator) – Batches of data. Returns:The predictions and targets. Return type:Tuple[torch.tensor, torch.tensor]

_eval_step(self)

Run an evaluation step over the validation data.

run(self)

Train until the next checkpoint, and evaluate.

Returns:Whether the computable is not yet complete. Return type:bool

metric(self)

Override this method to enable scheduling.

Returns:The metric to compare computable variants. Return type:float

_state(self, state_dict: State, prefix: str, local_metadata: Dict[str, Any])

_load_state(self, state_dict: State, prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[Any], unexpected_keys: List[Any], error_msgs: List[Any])

classmethod precompile(cls, **kwargs)

Override initialization.

Ensure that the model is compiled and pushed to the right device before its parameters are passed to the optimizer.

Package Contents

class flambe.learn.Trainer(dataset: Dataset, train_sampler: Sampler, val_sampler: Sampler, model: Module, loss_fn: Metric, metric_fn: Metric, optimizer: Optimizer, scheduler: Optional[_LRScheduler] = None, device: Optional[str] = None, max_steps: int = 10, epoch_per_step: float = 1.0, iter_per_step: Optional[int] = None, batches_per_iter: int = 1, lower_is_better: bool = False, max_grad_norm: Optional[float] = None, max_grad_abs_val: Optional[float] = None, extra_validation_metrics: Optional[List[Metric]] = None)

Bases: flambe.compile.Component

Implement a Trainer block.

A Trainer takes as input data, model and optimizer, and executes training incrementally in run.

Note that it is important that a trainer run be long enough to not increase overhead, so at least a few seconds, and ideally multiple minutes.

_batch_to_device(self, batch: Tuple[torch.Tensor, ...])

Move the current batch on the correct device.

Can be overriden if a batch doesn’t follow the expected structure. For example if the batch is a dictionary.

Parameters:batch (Tuple[torch.Tensor, ..]) – The batch to train on.

_compute_loss(self, batch: Tuple[torch.Tensor, ...])

Compute the loss given a single batch

Parameters:batch (Tuple[torch.Tensor, ..]) – The batch to train on.

_train_step(self)

Run a training step over the training data.

_aggregate_preds(self, data_iterator: Iterator)

Aggregate the predicitons and targets for the dataset.

Parameters:data_iterator (Iterator) – Batches of data. Returns:The predictions and targets. Return type:Tuple[torch.tensor, torch.tensor]

_eval_step(self)

Run an evaluation step over the validation data.

run(self)

Train until the next checkpoint, and evaluate.

Returns:Whether the computable is not yet complete. Return type:bool

metric(self)

Override this method to enable scheduling.

Returns:The metric to compare computable variants. Return type:float

_state(self, state_dict: State, prefix: str, local_metadata: Dict[str, Any])

_load_state(self, state_dict: State, prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[Any], unexpected_keys: List[Any], error_msgs: List[Any])

classmethod precompile(cls, **kwargs)

Override initialization.

Ensure that the model is compiled and pushed to the right device before its parameters are passed to the optimizer.

class flambe.learn.Evaluator(dataset: Dataset, eval_sampler: Sampler, model: Module, metric_fn: Metric, eval_data: str = 'test', device: Optional[str] = None)

Bases: flambe.compile.Component

Implement an Evaluator block.

An Evaluator takes as input data, and a model and executes the evaluation. This is a single step Component object.

Parameters:

• dataset (Dataset) – The dataset to run evaluation on
• eval_sampler (Sampler) – The sampler to use over validation examples
• model (Module) – The model to train
• metric_fn (Metric) – The metric to use for evaluation
• eval_data (str) – The data split to evaluate on: one of train, val or test
• device (str, optional) – The device to use in the computation.

run(self, block_name: str = None)

Run the evaluation.

Returns:Whether the computable has completed. Return type:bool

metric(self)

Override this method to enable scheduling.

Returns:The metric to compare computable varients Return type:float

class flambe.learn.Script(script: str, args: Dict[str, Any])

Bases: flambe.compile.Component

Implement a Script computable.

The obejct can be used to turn any script into a Flambé computable. This is useful when you want to rapidly integrate code. Note however that this computable does not enable checkpointing or linking to internal components as it does not have any attributes.

To use this object, your script needs to be in a pip installable, containing all dependencies. The script is run with the following command:

python -m script.py --arg1 value1 --arg2 value2

Parameters:

• path (str) – The script module
• args (Dict[str, Any]) – Argument dictionary

run(self)

Run the evaluation.

Returns:Report dictionary to use for logging Return type:Dict[str, float]

flambe.logging

Subpackages

flambe.logging.handler

Submodules

flambe.logging.handler.contextual_file

Module Contents

class flambe.logging.handler.contextual_file.ContextualFileHandler(canonical_name: str, mode: str = 'a', encoding=None)

Bases: logging.FileHandler

Uses the record current_log_dir value to customize file path

Uses the LogRecord object’s current_log_dir value to dynamically determine a path for the output file name. Functions the same as parent logging.FileHandler but always writes to the file given by current_log_dir + canonical_name.

Parameters:

• canonical_name (str) – Common name for each file
• mode (str) – See built-in open description of mode
• encoding (type) – See built-in open description of encoding

current_log_dir

Most recently used prefix in an incoming LogRecord

Type:str

canonical_name

Common name for each file

Type:str

mode

See built-in open description of mode

Type:str

delay

If true will delay opening of file until first use

Type:type

stream

Currently open file stream for writing logs - should match the file indicated by base_path + current_prefix + canonical_name

Type:type

encoding

See built-in open description of encoding

baseFilename :str

Output filename; Override parent property to use prefix

emit(self, record: logging.LogRecord)

Emit a record

If the stream is invalidated by a new record prefix value it will be closed and set to None before calling the super emit which will handle opening a new stream to baseFilename

Parameters:record (logging.LogRecord) – Record to be saved at _console_log_dir Returns: Return type:None

flambe.logging.handler.tensorboard

Module Contents

class flambe.logging.handler.tensorboard.TensorboardXHandler(*args: Any, **kwargs: Any)

Bases: logging.Handler

Implements Tensorboard message logging via TensorboardX

Parameters:

• writer (SummaryWriter) – Initialized TensorboardX Writer
• *args (Any) – Other positional args for logging.Handler
• **kwargs (Any) – Other kwargs for logging.Handler

writer

Initialized TensorboardX Writer

Type:SummaryWriter

emit(self, record: logging.LogRecord)

Save to tensorboard logging directory

Overrides logging.Handler.emit

Parameters:record (logging.LogRecord) – LogRecord with data relevant to Tensorboard Returns: Return type:None

close(self)

Teardown writers and teardown super

Returns: Return type:None

flush(self)

Call flush on the Tensorboard writer

Returns: Return type:None

Submodules

flambe.logging.datatypes

Module Contents

class flambe.logging.datatypes.ScalarT

Bases: typing.NamedTuple

A single scalar value

Supported by TensorboardX

Parameters:

• tag (str) – Data identifier
• scalar_value (float) – The scalar value
• global_step (int) – Iteration associated with this value
• walltime (float = time.time()) – Wall clock time associated with this value

tag :str

scalar_value :float

global_step :int

walltime :float

__repr__(self)

class flambe.logging.datatypes.ScalarsT

Bases: typing.NamedTuple

A dictionary mapping tag keys to scalar values

Supported by TensorboardX

Parameters:

• main_tag (str) – Parent name for all the children tags
• tag_scalar_dict (Dict[str, float]) – Mapping from scalar tags to their values
• global_step (int) – Iteration associated with this value
• walltime (float = time.time()) – Wall clock time associated with this value

main_tag :str

tag_scalar_dict :Dict[str, float]

global_step :int

walltime :float

__repr__(self)

class flambe.logging.datatypes.HistogramT

Bases: typing.NamedTuple

A histogram with an array of values

Supported by TensorboardX

Parameters:

• tag (str) – Data identifier
• values (Union[torch.Tensor, numpy.array]) – Values to build histogram
• global_step (int) – Iteration associated with this value
• bins (str) – Determines how bins are made
• walltime (float = time.time()) – Wall clock time associated with this value

tag :str

values :Union[torch.Tensor, numpy.array]

global_step :int

bins :str

walltime :float

__repr__(self)

class flambe.logging.datatypes.ImageT

Bases: typing.NamedTuple

Image message

Supported by TensorboardX

Parameters:

• tag (str) – Data identifier
• img_tensor (Union) – Image tensor to record
• global_step (int) – Iteration associated with this value
• walltime (float) – Wall clock time associated with this value

tag :str

img_tensor :Union[torch.Tensor, numpy.array]

global_step :int

walltime :float

__repr__(self)

class flambe.logging.datatypes.TextT

Bases: typing.NamedTuple

Text message

Supported by TensorboardX

Parameters:

• tag (str) – Data identifier
• text_string (str) – String to record
• global_step (int) – Iteration associated with this value
• walltime (float) – Wall clock time associated with this value

tag :str

text_string :str

global_step :int

walltime :float

__repr__(self)

class flambe.logging.datatypes.PRCurveT

Bases: typing.NamedTuple

PRCurve message

Supported by TensorboardX

Parameters:

• tag (str) – Data identifier
• labels (Union[torch.Tensor, numpy.array]) – Containing 0, 1 values
• predictions (Union[torch.Tensor, numpy.array]) – Containing 0<=x<=1 values. Needs to match labels size
• num_thresholds (int = 127) – The number of thresholds to evaluate. Max value allowed 127.
• weights (Optional[float] = None) – No description provided.
• global_step (int) – Iteration associated with this value
• walltime (float) – Wall clock time associated with this value

tag :str

labels :Union[torch.Tensor, numpy.array]

predictions :Union[torch.Tensor, numpy.array]

global_step :int

num_thresholds :int = 127

weights :Optional[float]

walltime :float

__repr__(self)

class flambe.logging.datatypes.EmbeddingT

Bases: typing.NamedTuple

Embedding data, including array of vaues and metadata

Supported by TensorboardX

Parameters:

• mat (\((N, D)\)) – A matrix where each row is the feature vector of a data point
• metadata (Sequence[str]) – A list of labels; each element will be converted to string
• label_img (\((N, C, H, W)\)) – Images corresponding to each data point
• global_step (int) – Iteration associated with this value
• tag (str) – Data identifier
• metadata_header (Sequence[str]) –
• Shape –
• ----- –
• mat – where N is number of data and D is feature dimension
• label_img –

mat :Union[torch.Tensor, numpy.array]

metadata :Sequence[str]

label_img :torch.Tensor

global_step :int

tag :str

metadata_header :Sequence[str]

__repr__(self)

class flambe.logging.datatypes.GraphT

Bases: typing.NamedTuple

PyTorch Model with input and other keyword args

Supported by ModelSave NOT YET Supported by TensorboardX

model

PyTorch Model (should have forward and state_dict methods)

Type:torch.nn.Module

input_to_model

Input to the model forward call

Type:torch.autograd.Variable

verbose

Include extra detail

Type:bool = False

kwargs

Other kwargs for model recording

Type:Dict[str, Any] = {}

model :torch.nn.Module

input_to_model :torch.autograd.Variable

verbose :bool = False

kwargs :Dict[str, Any]

flambe.logging.datatypes.DATA_TYPES

class flambe.logging.datatypes.DataLoggingFilter(default: bool = True, level: int = logging.NOTSET, dont_include: Optional[Tuple[type, ...]] = None, **kwargs: Any)

Bases: logging.Filter

Filters on DATA_TYPES otherwise returns default

filter returns self.default if record is not a DATA_TYPES type; True if message is a DATA_TYPES type not in dont_include and high enough level; otherwise False

Parameters:

• default (bool) – Returned when record is not one DATA_TYPES
• level (int) – Minimum level of records that are DATA_TYPES to be accepted
• dont_include (Sequence[Type[Any]]) – Types from DATA_TYPES to be excluded
• **kwargs (Any) – Additional kwargs to pass to logging.Filter

default

Returned when record is not one DATA_TYPES

Type:bool

level

Minimum level of records that are DATA_TYPES to be accepted

Type:int

dont_include

Types from DATA_TYPES to be excluded

Type:Tuple[Type[Any]]

filter(self, record: logging.LogRecord)

Return True iff record should be accepted

Parameters:record (logging.LogRecord) – logging record to be filtered Returns:True iff record should be accepted. self.default if record is not a DATA_TYPES type; True if message is a DATA_TYPES type not in dont_include and high enough level; otherwise False Return type:bool

flambe.logging.logging

Module Contents

flambe.logging.logging.MB

flambe.logging.logging.setup_global_logging(console_log_level: int = logging.NOTSET) → None

Set up flambe logging with a Stream handler and a Rotating File handler.

This method should be set before consuming any logger as it sets the basic configuration for all future logs.

After executing this method, all loggers will have the following handlers: * Stream handler: prints to std output all logs that above The console_log_level * Rotating File hanlder: 10MB log file located in Flambe global folder. Configured to store all logs (min level DEBUG)

Parameters:console_log_level (int) – The minimum log level for the Stream handler

class flambe.logging.logging.FlambeFilter

Bases: logging.Filter

Filter all log records that don’t come from flambe or main.

filter(self, record: logging.LogRecord)

class flambe.logging.logging.TrialLogging(log_dir: str, verbose: bool = False, root_log_level: Optional[int] = None, capture_warnings: bool = True, console_prefix: Optional[str] = None, hyper_params: Optional[Dict] = None)

__enter__(self)

__exit__(self, exc_type: Any, exc_value: Any, traceback: Any)

Close the listener and restore original logging config

class flambe.logging.logging.ContextInjection(**attrs)

Add specified attributes to all log records

Parameters:**attrs (Any) – Attributes that should be added to all log records, for use in downstream handlers

filter(self, record: logging.LogRecord)

__call__(self, record: logging.LogRecord)

class flambe.logging.logging.TqdmFileWrapper(file: Any)

Dummy file-like that will write to tqdm

Based on canoncial tqdm example

write(self, x: AnyStr)

flush(self)

flambe.logging.logging.colorize_exceptions() → None

Colorizes the system stderr ouput using pygments if installed

flambe.logging.utils

Module Contents

flambe.logging.utils.ValueT

flambe.logging.utils.d :Dict[str, Callable]

flambe.logging.utils.coloredlogs

flambe.logging.utils._get_context_logger() → logging.Logger

Return the appropriate logger related to the module that logs.

flambe.logging.utils.log(tag: str, data: ValueT, global_step: int, walltime: Optional[float] = None) → None

Log data to tensorboard and console (convenience function)

Inspects type of data and uses the appropriate wrapper for tensorboard to consume the data. Supports floats (scalar), dictionary mapping tags to gloats (scalars), and strings (text).

Parameters:

• tag (str) – Name of data, used as the tensorboard tag
• data (ValueT) – The scalar or text to log
• global_step (int) – Iteration number associated with data
• walltime (Optional[float]) – Walltime for data (the default is None).

Examples

Normally you would have to do the following to log a scalar >>> import logging; from flambe.logging import ScalarT >>> logger = logging.getLogger(__name__) >>> logger.info(ScalarT(tag, data, step, walltime)) But this method allows you to write a more concise statement with a common interface >>> from flambe.logging import log >>> log(tag, data, step)

flambe.logging.utils.log_scalar(tag: str, data: float, global_step: int, walltime: Optional[float] = None, logger: Optional[logging.Logger] = None) → None

Log tensorboard compatible scalar value with common interface

Parameters:

• tag (str) – Tensorboard tag associated with scalar data
• data (float) – Scalar float value
• global_step (int) – The global step or iteration number
• walltime (Optional[float]) – Current walltime, for example from time.time()
• logger (Optional[logging.Logger]) – logger to use for logging the scalar

flambe.logging.utils.log_scalars(tag: str, data: Dict[str, float], global_step: int, walltime: Optional[float] = None, logger: Optional[logging.Logger] = None) → None

Log tensorboard compatible scalar values with common interface

Parameters:

• tag (str) – Main tensorboard tag associated with all data
• data (Dict[str, float]) – Scalar float value
• global_step (int) – The global step or iteration number
• walltime (Optional[float]) – Current walltime, for example from time.time()
• logger (Optional[logging.Logger]) – logger to use for logging the scalar

flambe.logging.utils.log_text(tag: str, data: str, global_step: int, walltime: Optional[float] = None, logger: Optional[logging.Logger] = None) → None

Log tensorboard compatible text value with common interface

Parameters:

• tag (str) – Tensorboard tag associated with data
• data (str) – Scalar float value
• global_step (int) – The global step or iteration number
• walltime (Optional[float]) – Current walltime, for example from time.time()
• logger (Optional[logging.Logger]) – logger to use for logging the scalar

flambe.logging.utils.log_image(tag: str, data: str, global_step: int, walltime: Optional[float] = None, logger: Optional[logging.Logger] = None) → None

Log tensorboard compatible image value with common interface

Parameters:

• tag (str) – Tensorboard tag associated with data
• data (str) – Scalar float value
• global_step (int) – The global step or iteration number
• walltime (Optional[float]) – Current walltime, for example from time.time()
• logger (Optional[logging.Logger]) – logger to use for logging the scalar

flambe.logging.utils.log_pr_curve(tag: str, labels: Union[torch.Tensor, numpy.array], predictions: Union[torch.Tensor, numpy.array], global_step: int, num_thresholds: int = 127, walltime: Optional[float] = None, logger: Optional[logging.Logger] = None) → None

Log tensorboard compatible image value with common interface

Parameters:

• tag (str) – Data identifier
• labels (Union[torch.Tensor, numpy.array]) – Containing 0, 1 values
• predictions (Union[torch.Tensor, numpy.array]) – Containing 0<=x<=1 values. Needs to match labels size
• num_thresholds (int = 127) – The number of thresholds to evaluate. Max value allowed 127.
• weights (Optional[float] = None) – No description provided.
• global_step (int) – Iteration associated with this value
• walltime (float) – Wall clock time associated with this value
• logger (Optional[logging.Logger]) – logger to use for logging the scalar

flambe.logging.utils.log_histogram(tag: str, data: str, global_step: int, bins: str = 'auto', walltime: Optional[float] = None, logger: Optional[logging.Logger] = None) → None

Log tensorboard compatible image value with common interface

Parameters:

• tag (str) – Tensorboard tag associated with data
• data (str) – Scalar float value
• global_step (int) – The global step or iteration number
• walltime (Optional[float]) – Current walltime, for example from time.time()
• logger (Optional[logging.Logger]) – logger to use for logging the scalar

Package Contents

class flambe.logging.TrialLogging(log_dir: str, verbose: bool = False, root_log_level: Optional[int] = None, capture_warnings: bool = True, console_prefix: Optional[str] = None, hyper_params: Optional[Dict] = None)

__enter__(self)

__exit__(self, exc_type: Any, exc_value: Any, traceback: Any)

Close the listener and restore original logging config

flambe.logging.setup_global_logging(console_log_level: int = logging.NOTSET) → None

Set up flambe logging with a Stream handler and a Rotating File handler.

This method should be set before consuming any logger as it sets the basic configuration for all future logs.

After executing this method, all loggers will have the following handlers: * Stream handler: prints to std output all logs that above The console_log_level * Rotating File hanlder: 10MB log file located in Flambe global folder. Configured to store all logs (min level DEBUG)

Parameters:console_log_level (int) – The minimum log level for the Stream handler

class flambe.logging.ScalarT

Bases: typing.NamedTuple

A single scalar value

Supported by TensorboardX

Parameters:

• tag (str) – Data identifier
• scalar_value (float) – The scalar value
• global_step (int) – Iteration associated with this value
• walltime (float = time.time()) – Wall clock time associated with this value

tag :str

scalar_value :float

global_step :int

walltime :float

__repr__(self)

class flambe.logging.ScalarsT

Bases: typing.NamedTuple

A dictionary mapping tag keys to scalar values

Supported by TensorboardX

Parameters:

• main_tag (str) – Parent name for all the children tags
• tag_scalar_dict (Dict[str, float]) – Mapping from scalar tags to their values
• global_step (int) – Iteration associated with this value
• walltime (float = time.time()) – Wall clock time associated with this value

main_tag :str

tag_scalar_dict :Dict[str, float]

global_step :int

walltime :float

__repr__(self)

class flambe.logging.HistogramT

Bases: typing.NamedTuple

A histogram with an array of values

Supported by TensorboardX

Parameters:

• tag (str) – Data identifier
• values (Union[torch.Tensor, numpy.array]) – Values to build histogram
• global_step (int) – Iteration associated with this value
• bins (str) – Determines how bins are made
• walltime (float = time.time()) – Wall clock time associated with this value

tag :str

values :Union[torch.Tensor, numpy.array]

global_step :int

bins :str

walltime :float

__repr__(self)

class flambe.logging.TextT

Bases: typing.NamedTuple

Text message

Supported by TensorboardX

Parameters:

• tag (str) – Data identifier
• text_string (str) – String to record
• global_step (int) – Iteration associated with this value
• walltime (float) – Wall clock time associated with this value

tag :str

text_string :str

global_step :int

walltime :float

__repr__(self)

class flambe.logging.ImageT

Bases: typing.NamedTuple

Image message

Supported by TensorboardX

Parameters:

• tag (str) – Data identifier
• img_tensor (Union) – Image tensor to record
• global_step (int) – Iteration associated with this value
• walltime (float) – Wall clock time associated with this value

tag :str

img_tensor :Union[torch.Tensor, numpy.array]

global_step :int

walltime :float

__repr__(self)

class flambe.logging.PRCurveT

Bases: typing.NamedTuple

PRCurve message

Supported by TensorboardX

Parameters:

• tag (str) – Data identifier
• labels (Union[torch.Tensor, numpy.array]) – Containing 0, 1 values
• predictions (Union[torch.Tensor, numpy.array]) – Containing 0<=x<=1 values. Needs to match labels size
• num_thresholds (int = 127) – The number of thresholds to evaluate. Max value allowed 127.
• weights (Optional[float] = None) – No description provided.
• global_step (int) – Iteration associated with this value
• walltime (float) – Wall clock time associated with this value

tag :str

labels :Union[torch.Tensor, numpy.array]

predictions :Union[torch.Tensor, numpy.array]

global_step :int

num_thresholds :int = 127

weights :Optional[float]

walltime :float

__repr__(self)

class flambe.logging.EmbeddingT

Bases: typing.NamedTuple

Embedding data, including array of vaues and metadata

Supported by TensorboardX

Parameters:

• mat (\((N, D)\)) – A matrix where each row is the feature vector of a data point
• metadata (Sequence[str]) – A list of labels; each element will be converted to string
• label_img (\((N, C, H, W)\)) – Images corresponding to each data point
• global_step (int) – Iteration associated with this value
• tag (str) – Data identifier
• metadata_header (Sequence[str]) –
• Shape –
• ----- –
• mat – where N is number of data and D is feature dimension
• label_img –

mat :Union[torch.Tensor, numpy.array]

metadata :Sequence[str]

label_img :torch.Tensor

global_step :int

tag :str

metadata_header :Sequence[str]

__repr__(self)

class flambe.logging.GraphT

Bases: typing.NamedTuple

PyTorch Model with input and other keyword args

Supported by ModelSave NOT YET Supported by TensorboardX

model

PyTorch Model (should have forward and state_dict methods)

Type:torch.nn.Module

input_to_model

Input to the model forward call

Type:torch.autograd.Variable

verbose

Include extra detail

Type:bool = False

kwargs

Other kwargs for model recording

Type:Dict[str, Any] = {}

model :torch.nn.Module

input_to_model :torch.autograd.Variable

verbose :bool = False

kwargs :Dict[str, Any]

flambe.logging.log(tag: str, data: ValueT, global_step: int, walltime: Optional[float] = None) → None

Log data to tensorboard and console (convenience function)

Inspects type of data and uses the appropriate wrapper for tensorboard to consume the data. Supports floats (scalar), dictionary mapping tags to gloats (scalars), and strings (text).

Parameters:

• tag (str) – Name of data, used as the tensorboard tag
• data (ValueT) – The scalar or text to log
• global_step (int) – Iteration number associated with data
• walltime (Optional[float]) – Walltime for data (the default is None).

Examples

Normally you would have to do the following to log a scalar >>> import logging; from flambe.logging import ScalarT >>> logger = logging.getLogger(__name__) >>> logger.info(ScalarT(tag, data, step, walltime)) But this method allows you to write a more concise statement with a common interface >>> from flambe.logging import log >>> log(tag, data, step)

flambe.logging.coloredlogs

flambe.logging.log_scalar(tag: str, data: float, global_step: int, walltime: Optional[float] = None, logger: Optional[logging.Logger] = None) → None

Log tensorboard compatible scalar value with common interface

Parameters:

• tag (str) – Tensorboard tag associated with scalar data
• data (float) – Scalar float value
• global_step (int) – The global step or iteration number
• walltime (Optional[float]) – Current walltime, for example from time.time()
• logger (Optional[logging.Logger]) – logger to use for logging the scalar

flambe.logging.log_scalars(tag: str, data: Dict[str, float], global_step: int, walltime: Optional[float] = None, logger: Optional[logging.Logger] = None) → None

Log tensorboard compatible scalar values with common interface

Parameters:

• tag (str) – Main tensorboard tag associated with all data
• data (Dict[str, float]) – Scalar float value
• global_step (int) – The global step or iteration number
• walltime (Optional[float]) – Current walltime, for example from time.time()
• logger (Optional[logging.Logger]) – logger to use for logging the scalar

flambe.logging.log_text(tag: str, data: str, global_step: int, walltime: Optional[float] = None, logger: Optional[logging.Logger] = None) → None

Log tensorboard compatible text value with common interface

Parameters:

• tag (str) – Tensorboard tag associated with data
• data (str) – Scalar float value
• global_step (int) – The global step or iteration number
• walltime (Optional[float]) – Current walltime, for example from time.time()
• logger (Optional[logging.Logger]) – logger to use for logging the scalar

flambe.logging.log_image(tag: str, data: str, global_step: int, walltime: Optional[float] = None, logger: Optional[logging.Logger] = None) → None

Log tensorboard compatible image value with common interface

Parameters:

• tag (str) – Tensorboard tag associated with data
• data (str) – Scalar float value
• global_step (int) – The global step or iteration number
• walltime (Optional[float]) – Current walltime, for example from time.time()
• logger (Optional[logging.Logger]) – logger to use for logging the scalar

flambe.logging.log_histogram(tag: str, data: str, global_step: int, bins: str = 'auto', walltime: Optional[float] = None, logger: Optional[logging.Logger] = None) → None

Log tensorboard compatible image value with common interface

Parameters:

• tag (str) – Tensorboard tag associated with data
• data (str) – Scalar float value
• global_step (int) – The global step or iteration number
• walltime (Optional[float]) – Current walltime, for example from time.time()
• logger (Optional[logging.Logger]) – logger to use for logging the scalar

flambe.logging.log_pr_curve(tag: str, labels: Union[torch.Tensor, numpy.array], predictions: Union[torch.Tensor, numpy.array], global_step: int, num_thresholds: int = 127, walltime: Optional[float] = None, logger: Optional[logging.Logger] = None) → None

Log tensorboard compatible image value with common interface

Parameters:

• tag (str) – Data identifier
• labels (Union[torch.Tensor, numpy.array]) – Containing 0, 1 values
• predictions (Union[torch.Tensor, numpy.array]) – Containing 0<=x<=1 values. Needs to match labels size
• num_thresholds (int = 127) – The number of thresholds to evaluate. Max value allowed 127.
• weights (Optional[float] = None) – No description provided.
• global_step (int) – Iteration associated with this value
• walltime (float) – Wall clock time associated with this value
• logger (Optional[logging.Logger]) – logger to use for logging the scalar

flambe.metric

Subpackages

flambe.metric.dev

Submodules

flambe.metric.dev.accuracy

Module Contents

class flambe.metric.dev.accuracy.Accuracy

Bases: flambe.metric.metric.Metric

compute(self, pred: torch.Tensor, target: torch.Tensor)

Computes the loss.

Parameters:

• pred (Tensor) – input logits of shape (B x N)
• target (LontTensor) – target tensor of shape (B) or (B x N)

Returns:

accuracy – single label accuracy, of shape (B)

Return type:

torch.Tensor

flambe.metric.dev.auc

Module Contents

class flambe.metric.dev.auc.AUC(max_fpr=1.0)

Bases: flambe.metric.metric.Metric

compute(self, pred: torch.Tensor, target: torch.Tensor)

Compute AUC at the given max false positive rate.

Parameters:

• pred (torch.Tensor) – The model predictions
• target (torch.Tensor) – The binary targets

Returns:

The computed AUC

Return type:

torch.Tensor

flambe.metric.dev.binary

Module Contents

class flambe.metric.dev.binary.BinaryMetric(threshold: float = 0.5)

Bases: flambe.metric.metric.Metric

compute(self, pred: torch.Tensor, target: torch.Tensor)

Compute the metric given predictions and targets

Parameters:

• pred (Tensor) – The model predictions
• target (Tensor) – The binary targets

Returns:

The computed binary metric

Return type:

float

compute_binary(self, pred: torch.Tensor, target: torch.Tensor)

Compute a binary-input metric.

Parameters:

• pred (torch.Tensor) – Predictions made by the model. It should be a probability 0 <= p <= 1 for each sample, 1 being the positive class.
• target (torch.Tensor) – Ground truth. Each label should be either 0 or 1.

Returns:

The computed binary metric

Return type:

torch.float

class flambe.metric.dev.binary.BinaryAccuracy

Bases: flambe.metric.dev.binary.BinaryMetric

Compute binary accuracy.

` |True positives + True negatives| / N `

compute_binary(self, pred: torch.Tensor, target: torch.Tensor)

Compute binary accuracy.

Parameters:

• pred (torch.Tensor) – Predictions made by the model. It should be a probability 0 <= p <= 1 for each sample, 1 being the positive class.
• target (torch.Tensor) – Ground truth. Each label should be either 0 or 1.

Returns:

The computed binary metric

Return type:

torch.float

class flambe.metric.dev.binary.BinaryPrecision(threshold: float = 0.5, positive_label: int = 1)

Bases: flambe.metric.dev.binary.BinaryMetric

Compute Binary Precision.

An example is considered negative when its score is below the specified threshold. Binary precition is computed as follows:

` |True positives| / |True Positives| + |False Positives| `

compute_binary(self, pred: torch.Tensor, target: torch.Tensor)

Compute binary precision.

Parameters:

• pred (torch.Tensor) – Predictions made by the model. It should be a probability 0 <= p <= 1 for each sample, 1 being the positive class.
• target (torch.Tensor) – Ground truth. Each label should be either 0 or 1.

Returns:

The computed binary metric

Return type:

torch.float

__str__(self)

Return the name of the Metric (for use in logging).

class flambe.metric.dev.binary.BinaryRecall(threshold: float = 0.5, positive_label: int = 1)

Bases: flambe.metric.dev.binary.BinaryMetric

Compute binary recall.

An example is considered negative when its score is below the specified threshold. Binary precition is computed as follows:

` |True positives| / |True Positives| + |False Negatives| `

compute_binary(self, pred: torch.Tensor, target: torch.Tensor)

Compute binary recall.

Parameters:

• pred (torch.Tensor) – Predictions made by the model. It should be a probability 0 <= p <= 1 for each sample, 1 being the positive class.
• target (torch.Tensor) – Ground truth. Each label should be either 0 or 1.

Returns:

The computed binary metric

Return type:

torch.float

__str__(self)

Return the name of the Metric (for use in logging).

flambe.metric.dev.perplexity

Module Contents

class flambe.metric.dev.perplexity.Perplexity

Bases: flambe.metric.Metric

compute(self, pred: torch.Tensor, target: torch.Tensor)

Compute the preplexity given the input and target.

Parameters:

• pred (torch.Tensor) – input logits of shape (B x N)
• target (torch.LontTensor) – target tensor of shape (B x N)

Returns:

Output perplexity

Return type:

torch.float

flambe.metric.loss

Submodules

flambe.metric.loss.cross_entropy

Module Contents

class flambe.metric.loss.cross_entropy.MultiLabelCrossEntropy(weight: Optional[torch.Tensor] = None, ignore_index: Optional[int] = None, reduction: str = 'mean')

Bases: flambe.metric.metric.Metric

compute(self, pred: torch.Tensor, target: torch.Tensor)

Computes the multilabel cross entropy loss.

Parameters:

• pred (torch.Tensor) – input logits of shape (B x N)
• target (torch.LontTensor) – target tensor of shape (B x N)

Returns:

loss – Multi label cross-entropy loss, of shape (B)

Return type:

torch.Tensor

flambe.metric.loss.nll_loss

Module Contents

class flambe.metric.loss.nll_loss.MultiLabelNLLLoss(weight: Optional[torch.Tensor] = None, ignore_index: Optional[int] = None, reduction: str = 'mean')

Bases: flambe.metric.metric.Metric

compute(self, pred: torch.Tensor, target: torch.Tensor)

Computes the Negative log likelihood loss for multilabel.

Parameters:

• pred (torch.Tensor) – input logits of shape (B x N)
• target (torch.LontTensor) – target tensor of shape (B x N)

Returns:

loss – Multi label negative log likelihood loss, of shape (B)

Return type:

torch.float

Submodules

flambe.metric.metric

Module Contents

class flambe.metric.metric.Metric

Bases: flambe.compile.Component

Base Metric interface.

Objects implementing this interface should take in a sequence of examples and provide as output a processd list of the same size.

compute(self, pred: torch.Tensor, target: torch.Tensor)

Computes the metric over the given prediction and target.

Parameters:

• pred (torch.Tensor) – The model predictions
• target (torch.Tensor) – The ground truth targets

Returns:

The computed metric

Return type:

torch.Tensor

__call__(self, *args, **kwargs)

Makes Featurizer a callable.

__str__(self)

Return the name of the Metric (for use in logging).

Package Contents

class flambe.metric.Metric

Bases: flambe.compile.Component

Base Metric interface.

Objects implementing this interface should take in a sequence of examples and provide as output a processd list of the same size.

compute(self, pred: torch.Tensor, target: torch.Tensor)

Computes the metric over the given prediction and target.

Parameters:

• pred (torch.Tensor) – The model predictions
• target (torch.Tensor) – The ground truth targets

Returns:

The computed metric

Return type:

torch.Tensor

__call__(self, *args, **kwargs)

Makes Featurizer a callable.

__str__(self)

Return the name of the Metric (for use in logging).

class flambe.metric.MultiLabelCrossEntropy(weight: Optional[torch.Tensor] = None, ignore_index: Optional[int] = None, reduction: str = 'mean')

Bases: flambe.metric.metric.Metric

compute(self, pred: torch.Tensor, target: torch.Tensor)

Computes the multilabel cross entropy loss.

Parameters:

• pred (torch.Tensor) – input logits of shape (B x N)
• target (torch.LontTensor) – target tensor of shape (B x N)

Returns:

loss – Multi label cross-entropy loss, of shape (B)

Return type:

torch.Tensor

class flambe.metric.MultiLabelNLLLoss(weight: Optional[torch.Tensor] = None, ignore_index: Optional[int] = None, reduction: str = 'mean')

Bases: flambe.metric.metric.Metric

compute(self, pred: torch.Tensor, target: torch.Tensor)

Computes the Negative log likelihood loss for multilabel.

Parameters:

• pred (torch.Tensor) – input logits of shape (B x N)
• target (torch.LontTensor) – target tensor of shape (B x N)

Returns:

loss – Multi label negative log likelihood loss, of shape (B)

Return type:

torch.float

class flambe.metric.Accuracy

Bases: flambe.metric.metric.Metric

compute(self, pred: torch.Tensor, target: torch.Tensor)

Computes the loss.

Parameters:

• pred (Tensor) – input logits of shape (B x N)
• target (LontTensor) – target tensor of shape (B) or (B x N)

Returns:

accuracy – single label accuracy, of shape (B)

Return type:

torch.Tensor

class flambe.metric.Perplexity

Bases: flambe.metric.Metric

compute(self, pred: torch.Tensor, target: torch.Tensor)

Compute the preplexity given the input and target.

Parameters:

• pred (torch.Tensor) – input logits of shape (B x N)
• target (torch.LontTensor) – target tensor of shape (B x N)

Returns:

Output perplexity

Return type:

torch.float

class flambe.metric.AUC(max_fpr=1.0)

Bases: flambe.metric.metric.Metric

compute(self, pred: torch.Tensor, target: torch.Tensor)

Compute AUC at the given max false positive rate.

Parameters:

• pred (torch.Tensor) – The model predictions
• target (torch.Tensor) – The binary targets

Returns:

The computed AUC

Return type:

torch.Tensor

class flambe.metric.BinaryPrecision(threshold: float = 0.5, positive_label: int = 1)

Bases: flambe.metric.dev.binary.BinaryMetric

Compute Binary Precision.

An example is considered negative when its score is below the specified threshold. Binary precition is computed as follows:

` |True positives| / |True Positives| + |False Positives| `

compute_binary(self, pred: torch.Tensor, target: torch.Tensor)

Compute binary precision.

Parameters:

• pred (torch.Tensor) – Predictions made by the model. It should be a probability 0 <= p <= 1 for each sample, 1 being the positive class.
• target (torch.Tensor) – Ground truth. Each label should be either 0 or 1.

Returns:

The computed binary metric

Return type:

torch.float

__str__(self)

Return the name of the Metric (for use in logging).

class flambe.metric.BinaryRecall(threshold: float = 0.5, positive_label: int = 1)

Bases: flambe.metric.dev.binary.BinaryMetric

Compute binary recall.

An example is considered negative when its score is below the specified threshold. Binary precition is computed as follows:

` |True positives| / |True Positives| + |False Negatives| `

compute_binary(self, pred: torch.Tensor, target: torch.Tensor)

Compute binary recall.

Parameters:

• pred (torch.Tensor) – Predictions made by the model. It should be a probability 0 <= p <= 1 for each sample, 1 being the positive class.
• target (torch.Tensor) – Ground truth. Each label should be either 0 or 1.

Returns:

The computed binary metric

Return type:

torch.float

__str__(self)

Return the name of the Metric (for use in logging).

class flambe.metric.BinaryAccuracy

Bases: flambe.metric.dev.binary.BinaryMetric

Compute binary accuracy.

` |True positives + True negatives| / N `

compute_binary(self, pred: torch.Tensor, target: torch.Tensor)

Compute binary accuracy.

Parameters:

• pred (torch.Tensor) – Predictions made by the model. It should be a probability 0 <= p <= 1 for each sample, 1 being the positive class.
• target (torch.Tensor) – Ground truth. Each label should be either 0 or 1.

Returns:

The computed binary metric

Return type:

torch.float

flambe.model

Submodules

flambe.model.logistic_regression

Module Contents

class flambe.model.logistic_regression.LogisticRegression(input_size: int)

Bases: flambe.nn.module.Module

Logistic regression model given an input vector v the forward calculation is sigmoid(Wv+b), where W is a weight vector and b a bias term. The result is then passed to a sigmoid function, which maps it as a real number in [0,1]. This is typically interpreted in classification settings as the probability of belonging to a given class.

input_size

Dimension (number of features) of the input vector.

Type:int

forward(self, data: Tensor, target: Optional[Tensor] = None)

Forward pass that encodes data :param data: input data to encode :type data: Tensor :param target: target value, will be casted to a float tensor. :type target: Optional[Tensor]

Package Contents

class flambe.model.LogisticRegression(input_size: int)

Bases: flambe.nn.module.Module

Logistic regression model given an input vector v the forward calculation is sigmoid(Wv+b), where W is a weight vector and b a bias term. The result is then passed to a sigmoid function, which maps it as a real number in [0,1]. This is typically interpreted in classification settings as the probability of belonging to a given class.

input_size

Dimension (number of features) of the input vector.

Type:int

forward(self, data: Tensor, target: Optional[Tensor] = None)

Forward pass that encodes data :param data: input data to encode :type data: Tensor :param target: target value, will be casted to a float tensor. :type target: Optional[Tensor]

flambe.nlp

Subpackages

flambe.nlp.classification

Submodules

flambe.nlp.classification.datasets

Module Contents

class flambe.nlp.classification.datasets.SSTDataset(binary: bool = True, phrases: bool = False, cache: bool = True, transform: Dict[str, Union[Field, Dict]] = None)

Bases: flambe.dataset.TabularDataset

The official SST-1 dataset.

URL = https://raw.githubusercontent.com/harvardnlp/sent-conv-torch/master/data/

classmethod _load_file(cls, path: str, sep: Optional[str] = 't', header: Optional[str] = None, columns: Optional[Union[List[str], List[int]]] = None, encoding: Optional[str] = 'utf-8')

Load data from the given path.

class flambe.nlp.classification.datasets.TRECDataset(cache: bool = True, transform: Dict[str, Union[Field, Dict]] = None)

Bases: flambe.dataset.TabularDataset

The official TREC dataset.

URL = https://raw.githubusercontent.com/harvardnlp/sent-conv-torch/master/data/

classmethod _load_file(cls, path: str, sep: Optional[str] = 't', header: Optional[str] = None, columns: Optional[Union[List[str], List[int]]] = None, encoding: Optional[str] = 'latin-1')

Load data from the given path.

class flambe.nlp.classification.datasets.NewsGroupDataset(cache: bool = False, transform: Dict[str, Union[Field, Dict]] = None)

Bases: flambe.dataset.TabularDataset

The official 20 news group dataset.

flambe.nlp.classification.model

Module Contents

class flambe.nlp.classification.model.TextClassifier(embedder: Embedder, output_layer: Module, dropout: float = 0)

Bases: flambe.nn.Module

Implements a standard classifier.

The classifier is composed of an encoder module, followed by a fully connected output layer, with a dropout layer in between.

embedder

The embedder layer

Type:Embedder

output_layer

The output layer, yields a probability distribution over targets

Type:Module

drop

the dropout layer

Type:nn.Dropout

loss

the loss function to optimize the model with

Type:Metric

metric

the dev metric to evaluate the model on

Type:Metric

forward(self, data: Tensor, target: Optional[Tensor] = None)

Run a forward pass through the network.

Parameters:

• data (Tensor) – The input data
• target (Tensor, optional) – The input targets, optional

Returns:

The output predictions, and optionally the targets

Return type:

Union[Tensor, Tuple[Tensor, Tensor]

Package Contents

class flambe.nlp.classification.SSTDataset(binary: bool = True, phrases: bool = False, cache: bool = True, transform: Dict[str, Union[Field, Dict]] = None)

Bases: flambe.dataset.TabularDataset

The official SST-1 dataset.

URL = https://raw.githubusercontent.com/harvardnlp/sent-conv-torch/master/data/

classmethod _load_file(cls, path: str, sep: Optional[str] = 't', header: Optional[str] = None, columns: Optional[Union[List[str], List[int]]] = None, encoding: Optional[str] = 'utf-8')

Load data from the given path.

class flambe.nlp.classification.TRECDataset(cache: bool = True, transform: Dict[str, Union[Field, Dict]] = None)

Bases: flambe.dataset.TabularDataset

The official TREC dataset.

URL = https://raw.githubusercontent.com/harvardnlp/sent-conv-torch/master/data/

classmethod _load_file(cls, path: str, sep: Optional[str] = 't', header: Optional[str] = None, columns: Optional[Union[List[str], List[int]]] = None, encoding: Optional[str] = 'latin-1')

Load data from the given path.

class flambe.nlp.classification.NewsGroupDataset(cache: bool = False, transform: Dict[str, Union[Field, Dict]] = None)

Bases: flambe.dataset.TabularDataset

The official 20 news group dataset.

class flambe.nlp.classification.TextClassifier(embedder: Embedder, output_layer: Module, dropout: float = 0)

Bases: flambe.nn.Module

Implements a standard classifier.

The classifier is composed of an encoder module, followed by a fully connected output layer, with a dropout layer in between.

embedder

The embedder layer

Type:Embedder

output_layer

The output layer, yields a probability distribution over targets

Type:Module

drop

the dropout layer

Type:nn.Dropout

loss

the loss function to optimize the model with

Type:Metric

metric

the dev metric to evaluate the model on

Type:Metric

forward(self, data: Tensor, target: Optional[Tensor] = None)

Run a forward pass through the network.

Parameters:

• data (Tensor) – The input data
• target (Tensor, optional) – The input targets, optional

Returns:

The output predictions, and optionally the targets

Return type:

Union[Tensor, Tuple[Tensor, Tensor]

flambe.nlp.fewshot

Submodules

flambe.nlp.fewshot.model

Module Contents

class flambe.nlp.fewshot.model.PrototypicalTextClassifier(embedder: Embedder, distance: str = 'euclidean', detach_mean: bool = False)

Bases: flambe.nn.Module

Implements a standard classifier.

The classifier is composed of an encoder module, followed by a fully connected output layer, with a dropout layer in between.

encoder

the encoder object

Type:Module

decoder

the decoder layer

Type:Decoder

drop

the dropout layer

Type:nn.Dropout

loss

the loss function to optimize the model with

Type:Metric

metric

the dev metric to evaluate the model on

Type:Metric

compute_prototypes(self, support: Tensor, label: Tensor)

Set the current prototypes used for classification.

Parameters:

• data (torch.Tensor) – Input encodings
• label (torch.Tensor) – Corresponding labels

forward(self, query: Tensor, query_label: Optional[Tensor] = None, support: Optional[Tensor] = None, support_label: Optional[Tensor] = None)

Run a forward pass through the network.

Parameters:data (Tensor) – The input data Returns:The output predictions Return type:Union[Tensor, Tuple[Tensor, Tensor]]

Package Contents

class flambe.nlp.fewshot.PrototypicalTextClassifier(embedder: Embedder, distance: str = 'euclidean', detach_mean: bool = False)

Bases: flambe.nn.Module

Implements a standard classifier.

The classifier is composed of an encoder module, followed by a fully connected output layer, with a dropout layer in between.

encoder

the encoder object

Type:Module

decoder

the decoder layer

Type:Decoder

drop

the dropout layer

Type:nn.Dropout

loss

the loss function to optimize the model with

Type:Metric

metric

the dev metric to evaluate the model on

Type:Metric

compute_prototypes(self, support: Tensor, label: Tensor)

Set the current prototypes used for classification.

Parameters:

• data (torch.Tensor) – Input encodings
• label (torch.Tensor) – Corresponding labels

forward(self, query: Tensor, query_label: Optional[Tensor] = None, support: Optional[Tensor] = None, support_label: Optional[Tensor] = None)

Run a forward pass through the network.

Parameters:data (Tensor) – The input data Returns:The output predictions Return type:Union[Tensor, Tuple[Tensor, Tensor]]

flambe.nlp.language_modeling

Submodules

flambe.nlp.language_modeling.datasets

Module Contents

class flambe.nlp.language_modeling.datasets.PTBDataset(cache=False, transform: Dict[str, Union[Field, Dict]] = None)

Bases: flambe.dataset.TabularDataset

The official SST training dataset.

PTB_URL = https://raw.githubusercontent.com/yoonkim/lstm-char-cnn/master/data/ptb/

classmethod _load_file(cls, path: str, sep: Optional[str] = 't', header: Optional[str] = None, columns: Optional[Union[List[str], List[int]]] = None, encoding: Optional[str] = 'utf-8')

Load data from the given path.

flambe.nlp.language_modeling.fields

Module Contents

class flambe.nlp.language_modeling.fields.LMField(**kwargs)

Bases: flambe.field.TextField

Language Model field.

Generates the original tensor alongside its shifted version.

process(self, example: str)

Process an example and create 2 Tensors.

Parameters:example (str) – The example to process, as a single string Returns:The processed example, tokenized and numericalized Return type:Tuple[torch.Tensor, ..]

flambe.nlp.language_modeling.model

Module Contents

class flambe.nlp.language_modeling.model.LanguageModel(embedder: Embedder, output_layer: Module, dropout: float = 0, pad_index: int = 0, tie_weights: bool = False)

Bases: flambe.nn.Module

Implement an LanguageModel model for sequential classification.

This model can be used to language modeling, as well as other sequential classification tasks. The full sequence predictions are produced by the model, effectively making the number of examples the batch size multiplied by the sequence length.

forward(self, data: Tensor, target: Optional[Tensor] = None)

Run a forward pass through the network.

Parameters:data (Tensor) – The input data Returns:The output predictions of shape seq_len x batch_size x n_out Return type:Union[Tensor, Tuple[Tensor, Tensor]]

Package Contents

class flambe.nlp.language_modeling.PTBDataset(cache=False, transform: Dict[str, Union[Field, Dict]] = None)

Bases: flambe.dataset.TabularDataset

The official SST training dataset.

PTB_URL = https://raw.githubusercontent.com/yoonkim/lstm-char-cnn/master/data/ptb/

classmethod _load_file(cls, path: str, sep: Optional[str] = 't', header: Optional[str] = None, columns: Optional[Union[List[str], List[int]]] = None, encoding: Optional[str] = 'utf-8')

Load data from the given path.

class flambe.nlp.language_modeling.LMField(**kwargs)

Bases: flambe.field.TextField

Language Model field.

Generates the original tensor alongside its shifted version.

process(self, example: str)

Process an example and create 2 Tensors.

Parameters:example (str) – The example to process, as a single string Returns:The processed example, tokenized and numericalized Return type:Tuple[torch.Tensor, ..]

class flambe.nlp.language_modeling.LanguageModel(embedder: Embedder, output_layer: Module, dropout: float = 0, pad_index: int = 0, tie_weights: bool = False)

Bases: flambe.nn.Module

Implement an LanguageModel model for sequential classification.

This model can be used to language modeling, as well as other sequential classification tasks. The full sequence predictions are produced by the model, effectively making the number of examples the batch size multiplied by the sequence length.

forward(self, data: Tensor, target: Optional[Tensor] = None)

Run a forward pass through the network.

Parameters:data (Tensor) – The input data Returns:The output predictions of shape seq_len x batch_size x n_out Return type:Union[Tensor, Tuple[Tensor, Tensor]]

flambe.nlp.transformers

Submodules

flambe.nlp.transformers.bert

Intergation of the pytorch_transformers bert module

Module Contents

class flambe.nlp.transformers.bert.BERTTextField(vocab_file: str, sos_token: str = '[CLS]', eos_token: str = '[SEP]', do_lower_case: bool = False, max_len_truncate: int = 100, **kwargs)

Bases: flambe.field.TextField, pytorch_transformers.BertTokenizer

Perform WordPiece tokenization.

Inspired by: https://github.com/huggingface/pytorch-pretrained-BERT/ blob/master/pytorch_pretrained_bert/tokenization.py.

Note that this object requires a pretrained vocabulary.

classmethod from_alias(cls, path: str = 'bert-base-cased', cache_dir: Optional[str] = None, do_lower_case: bool = False, max_len_truncate: int = 100, **kwargs)

Initialize from a pretrained tokenizer.

Parameters:path (str) – Path to a pretrained model, or one of the following string aliases currently available: . bert-base-uncased . bert-large-uncased . bert-base-cased . bert-large-cased . bert-base-multilingual-uncased . bert-base-multilingual-cased . bert-base-chinese

process(self, example: str)

Process an example, and create a Tensor.

Parameters:example (str) – The example to process, as a single string Returns:The processed example, tokenized and numericalized Return type:torch.Tensor

class flambe.nlp.transformers.bert.BERTEmbeddings(input_size_or_config: Union[int, pt.BertConfig], embedding_size: int = 768, embedding_dropout: float = 0.1, embedding_freeze: bool = False, pad_index: int = 0, max_position_embeddings: int = 512, type_vocab_size: int = 2, **kwargs)

Bases: flambe.nn.Module, pytorch_transformers.modeling_bert.BertPreTrainedModel

Integrate the pytorch_pretrained_bert BERT word embedding model.

This module can be used as any normal encoder, or it can be loaded with the official pretrained BERT models. Simply used the from_pretrained class method when initializing the model.

Currently available: . bert-base-uncased . bert-large-uncased . bert-base-cased . bert-large-cased . bert-base-multilingual-uncased . bert-base-multilingual-cased . bert-base-chinese

classmethod from_alias(cls, path: str = 'bert-base-cased', cache_dir: Optional[str] = None, **kwargs)

Initialize from a pretrained model.

Parameters:path (str) – Path to a pretrained model, or one of the following string aliases currently available: . bert-base-uncased . bert-large-uncased . bert-base-cased . bert-large-cased . bert-base-multilingual-uncased . bert-base-multilingual-cased . bert-base-chinese

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor, batch first Returns:

• torch.Tensor – The encoded output, as a float tensor, batch_first
• torch.Tensor, optional – The padding mask if a pad index was given

class flambe.nlp.transformers.bert.BERTEncoder(input_size_or_config: Union[int, pt.modeling_bert.BertConfig], hidden_size: int = 768, num_hidden_layers: int = 12, num_attention_heads: int = 12, intermediate_size: int = 3072, hidden_act: str = 'gelu', hidden_dropout_prob: float = 0.1, attention_probs_dropout_prob: float = 0.1, max_position_embeddings: int = 512, type_vocab_size: int = 2, initializer_range: float = 0.02, pool_last: bool = False, **kwargs)

Bases: flambe.nn.Module, pytorch_transformers.modeling_bert.BertPreTrainedModel

Integrate the pytorch_pretrained_bert BERT encoder model.

This module can be used as any normal encoder, or it can be loaded with the official pretrained BERT models. Simply used the from_pretrained class method when initializing the model.

Currently available: . bert-base-uncased . bert-large-uncased . bert-base-cased . bert-large-cased . bert-base-multilingual-uncased . bert-base-multilingual-cased . bert-base-chinese

classmethod from_alias(cls, path: str = 'bert-base-cased', cache_dir: Optional[str] = None, pool_last: bool = False, **kwargs)

Initialize from a pretrained model.

Parameters:path (str) – Path to a pretrained model, or one of the following string aliases currently available: . bert-base-uncased . bert-large-uncased . bert-base-cased . bert-large-cased . bert-base-multilingual-uncased . bert-base-multilingual-cased . bert-base-chinese

forward(self, data: Tensor, mask: Optional[Tensor] = None)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a long tensor Returns:The encoded output, as a float tensor or the pooled output Return type:torch.Tensor

flambe.nlp.transformers.openai

Intergation of the pytorch_transformers openai module

Module Contents

class flambe.nlp.transformers.openai.OpenAIGPTTextField(vocab_file: str, merges_file: str, max_len: int = 100, lower: bool = False)

Bases: flambe.field.TextField, pytorch_transformers.OpenAIGPTTokenizer

Perform WordPiece tokenization.

Inspired by: https://github.com/huggingface/pytorch-pretrained-BERT/ blob/master/pytorch_pretrained_bert/tokenization_openai.py.

Note that this object requires a pretrained vocabulary.

classmethod from_alias(cls, path: str = 'openai-gpt', cache_dir: Optional[str] = None)

Initialize from a pretrained tokenizer.

process(self, example: str)

Process an example, and create a Tensor.

Parameters:example (str) – The example to process, as a single string Returns:The processed example, tokenized and numericalized Return type:torch.Tensor

class flambe.nlp.transformers.openai.OpenAIGPTEmbeddings(input_size_or_config: Union[int, pt.OpenAIGPTConfig] = 40478, embedding_size: int = 768, embedding_dropout: float = 0.1, embedding_freeze: bool = False, pad_index: int = 0, n_special: int = 0, n_positions: int = 512, initializer_range=0.02)

Bases: flambe.nn.Module, pytorch_transformers.modeling_openai.OpenAIGPTPreTrainedModel

Integrate the pytorch_pretrained_bert OpenAI embedding model.

This module can be used as any normal encoder, or it can be loaded with the official pretrained OpenAI models. Simply used the from_pretrained class method when initializing the model.

classmethod from_alias(cls, path: str = 'openai-gpt', cache_dir: Optional[str] = None)

Initialize from a pretrained model.

Parameters:path (str) – Path to a pretrained model, or one of the following string aliases currently available: . openai-gpt

set_num_special_tokens(self, num_special_tokens)

Update input embeddings with new embedding matrice if needed

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor, batch first Returns:

• torch.Tensor – The encoded output, as a float tensor, batch_first
• torch.Tensor, optional – The padding mask if a pad index was given

class flambe.nlp.transformers.openai.OpenAIGPTEncoder(input_size_or_config: Union[int, pt.OpenAIGPTConfig] = 768, n_ctx: int = 512, n_layer: int = 12, n_head: int = 12, afn: Union[str, nn.Module] = 'gelu', resid_pdrop: float = 0.1, embd_pdrop: float = 0.1, attn_pdrop: float = 0.1, layer_norm_epsilon: float = 1e-05, initializer_range=0.02)

Bases: flambe.nn.Module, pytorch_transformers.modeling_openai.OpenAIGPTPreTrainedModel

Integrate the pytorch_pretrained_bert OpenAIGPT encoder model.

This module can be used as any normal encoder, or it can be loaded with the official pretrained BERT models. Simply used the from_pretrained class method when initializing the model.

Currently available: . openai-gpt

classmethod from_alias(cls, path: str = 'openai-gpt', cache_dir: Optional[str] = None)

Initialize from a pretrained model.

Parameters:path (str) – Path to a pretrained model, or one of the following string aliases currently available: . openai-gpt

forward(self, data: Tensor, mask: Optional[Tensor] = None)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a long tensor Returns:The encoded output, as a float tensor or the pooled output Return type:torch.Tensor

flambe.nlp.transformers.optim

Intergation of the pytorch_transformers optimization module

Module Contents

class flambe.nlp.transformers.optim.ConstantLRSchedule

Bases: flambe.Component, pytorch_transformers.ConstantLRSchedule

class flambe.nlp.transformers.optim.WarmupConstantSchedule

Bases: flambe.Component, pytorch_transformers.WarmupConstantSchedule

class flambe.nlp.transformers.optim.WarmupLinearSchedule

Bases: flambe.Component, pytorch_transformers.WarmupLinearSchedule

class flambe.nlp.transformers.optim.AdamW

Bases: flambe.Component, pytorch_transformers.optimization.Optimizer

Package Contents

class flambe.nlp.transformers.BERTTextField(vocab_file: str, sos_token: str = '[CLS]', eos_token: str = '[SEP]', do_lower_case: bool = False, max_len_truncate: int = 100, **kwargs)

Bases: flambe.field.TextField, pytorch_transformers.BertTokenizer

Perform WordPiece tokenization.

Inspired by: https://github.com/huggingface/pytorch-pretrained-BERT/ blob/master/pytorch_pretrained_bert/tokenization.py.

Note that this object requires a pretrained vocabulary.

classmethod from_alias(cls, path: str = 'bert-base-cased', cache_dir: Optional[str] = None, do_lower_case: bool = False, max_len_truncate: int = 100, **kwargs)

Initialize from a pretrained tokenizer.

Parameters:path (str) – Path to a pretrained model, or one of the following string aliases currently available: . bert-base-uncased . bert-large-uncased . bert-base-cased . bert-large-cased . bert-base-multilingual-uncased . bert-base-multilingual-cased . bert-base-chinese

process(self, example: str)

Process an example, and create a Tensor.

Parameters:example (str) – The example to process, as a single string Returns:The processed example, tokenized and numericalized Return type:torch.Tensor

class flambe.nlp.transformers.BERTEmbeddings(input_size_or_config: Union[int, pt.BertConfig], embedding_size: int = 768, embedding_dropout: float = 0.1, embedding_freeze: bool = False, pad_index: int = 0, max_position_embeddings: int = 512, type_vocab_size: int = 2, **kwargs)

Bases: flambe.nn.Module, pytorch_transformers.modeling_bert.BertPreTrainedModel

Integrate the pytorch_pretrained_bert BERT word embedding model.

This module can be used as any normal encoder, or it can be loaded with the official pretrained BERT models. Simply used the from_pretrained class method when initializing the model.

Currently available: . bert-base-uncased . bert-large-uncased . bert-base-cased . bert-large-cased . bert-base-multilingual-uncased . bert-base-multilingual-cased . bert-base-chinese

classmethod from_alias(cls, path: str = 'bert-base-cased', cache_dir: Optional[str] = None, **kwargs)

Initialize from a pretrained model.

Parameters:path (str) – Path to a pretrained model, or one of the following string aliases currently available: . bert-base-uncased . bert-large-uncased . bert-base-cased . bert-large-cased . bert-base-multilingual-uncased . bert-base-multilingual-cased . bert-base-chinese

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor, batch first Returns:

• torch.Tensor – The encoded output, as a float tensor, batch_first
• torch.Tensor, optional – The padding mask if a pad index was given

class flambe.nlp.transformers.BERTEncoder(input_size_or_config: Union[int, pt.modeling_bert.BertConfig], hidden_size: int = 768, num_hidden_layers: int = 12, num_attention_heads: int = 12, intermediate_size: int = 3072, hidden_act: str = 'gelu', hidden_dropout_prob: float = 0.1, attention_probs_dropout_prob: float = 0.1, max_position_embeddings: int = 512, type_vocab_size: int = 2, initializer_range: float = 0.02, pool_last: bool = False, **kwargs)

Bases: flambe.nn.Module, pytorch_transformers.modeling_bert.BertPreTrainedModel

Integrate the pytorch_pretrained_bert BERT encoder model.

This module can be used as any normal encoder, or it can be loaded with the official pretrained BERT models. Simply used the from_pretrained class method when initializing the model.

Currently available: . bert-base-uncased . bert-large-uncased . bert-base-cased . bert-large-cased . bert-base-multilingual-uncased . bert-base-multilingual-cased . bert-base-chinese

classmethod from_alias(cls, path: str = 'bert-base-cased', cache_dir: Optional[str] = None, pool_last: bool = False, **kwargs)

Initialize from a pretrained model.

Parameters:path (str) – Path to a pretrained model, or one of the following string aliases currently available: . bert-base-uncased . bert-large-uncased . bert-base-cased . bert-large-cased . bert-base-multilingual-uncased . bert-base-multilingual-cased . bert-base-chinese

forward(self, data: Tensor, mask: Optional[Tensor] = None)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a long tensor Returns:The encoded output, as a float tensor or the pooled output Return type:torch.Tensor

class flambe.nlp.transformers.OpenAIGPTTextField(vocab_file: str, merges_file: str, max_len: int = 100, lower: bool = False)

Bases: flambe.field.TextField, pytorch_transformers.OpenAIGPTTokenizer

Perform WordPiece tokenization.

Inspired by: https://github.com/huggingface/pytorch-pretrained-BERT/ blob/master/pytorch_pretrained_bert/tokenization_openai.py.

Note that this object requires a pretrained vocabulary.

classmethod from_alias(cls, path: str = 'openai-gpt', cache_dir: Optional[str] = None)

Initialize from a pretrained tokenizer.

process(self, example: str)

Process an example, and create a Tensor.

Parameters:example (str) – The example to process, as a single string Returns:The processed example, tokenized and numericalized Return type:torch.Tensor

class flambe.nlp.transformers.OpenAIGPTEmbeddings(input_size_or_config: Union[int, pt.OpenAIGPTConfig] = 40478, embedding_size: int = 768, embedding_dropout: float = 0.1, embedding_freeze: bool = False, pad_index: int = 0, n_special: int = 0, n_positions: int = 512, initializer_range=0.02)

Bases: flambe.nn.Module, pytorch_transformers.modeling_openai.OpenAIGPTPreTrainedModel

Integrate the pytorch_pretrained_bert OpenAI embedding model.

This module can be used as any normal encoder, or it can be loaded with the official pretrained OpenAI models. Simply used the from_pretrained class method when initializing the model.

classmethod from_alias(cls, path: str = 'openai-gpt', cache_dir: Optional[str] = None)

Initialize from a pretrained model.

Parameters:path (str) – Path to a pretrained model, or one of the following string aliases currently available: . openai-gpt

set_num_special_tokens(self, num_special_tokens)

Update input embeddings with new embedding matrice if needed

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor, batch first Returns:

• torch.Tensor – The encoded output, as a float tensor, batch_first
• torch.Tensor, optional – The padding mask if a pad index was given

class flambe.nlp.transformers.OpenAIGPTEncoder(input_size_or_config: Union[int, pt.OpenAIGPTConfig] = 768, n_ctx: int = 512, n_layer: int = 12, n_head: int = 12, afn: Union[str, nn.Module] = 'gelu', resid_pdrop: float = 0.1, embd_pdrop: float = 0.1, attn_pdrop: float = 0.1, layer_norm_epsilon: float = 1e-05, initializer_range=0.02)

Bases: flambe.nn.Module, pytorch_transformers.modeling_openai.OpenAIGPTPreTrainedModel

Integrate the pytorch_pretrained_bert OpenAIGPT encoder model.

This module can be used as any normal encoder, or it can be loaded with the official pretrained BERT models. Simply used the from_pretrained class method when initializing the model.

Currently available: . openai-gpt

classmethod from_alias(cls, path: str = 'openai-gpt', cache_dir: Optional[str] = None)

Initialize from a pretrained model.

Parameters:path (str) – Path to a pretrained model, or one of the following string aliases currently available: . openai-gpt

forward(self, data: Tensor, mask: Optional[Tensor] = None)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a long tensor Returns:The encoded output, as a float tensor or the pooled output Return type:torch.Tensor

class flambe.nlp.transformers.AdamW

Bases: flambe.Component, pytorch_transformers.optimization.Optimizer

class flambe.nlp.transformers.ConstantLRSchedule

Bases: flambe.Component, pytorch_transformers.ConstantLRSchedule

class flambe.nlp.transformers.WarmupConstantSchedule

Bases: flambe.Component, pytorch_transformers.WarmupConstantSchedule

class flambe.nlp.transformers.WarmupLinearSchedule

Bases: flambe.Component, pytorch_transformers.WarmupLinearSchedule

flambe.nn

Subpackages

flambe.nn.distance

Submodules

flambe.nn.distance.cosine

Module Contents

class flambe.nn.distance.cosine.CosineDistance(eps: float = 1e-08)

Bases: flambe.nn.distance.DistanceModule

Implement a CosineDistance object.

forward(self, mat_1: Tensor, mat_2: Tensor)

Returns the cosine distance between each element in mat_1 and each element in mat_2.

Parameters:

• mat_1 (torch.Tensor) – matrix of shape (n_1, n_features)
• mat_2 (torch.Tensor) – matrix of shape (n_2, n_features)

Returns:

dist – distance matrix of shape (n_1, n_2)

Return type:

torch.Tensor

class flambe.nn.distance.cosine.CosineMean

Bases: flambe.nn.distance.MeanModule

Implement a CosineMean object.

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

flambe.nn.distance.distance

Module Contents

class flambe.nn.distance.distance.DistanceModule

Bases: flambe.nn.module.Module

Implement a DistanceModule object.

forward(self, mat_1: Tensor, mat_2: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

class flambe.nn.distance.distance.MeanModule(detach_mean: bool = False)

Bases: flambe.nn.module.Module

Implement a MeanModule object.

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

flambe.nn.distance.euclidean

Module Contents

class flambe.nn.distance.euclidean.EuclideanDistance

Bases: flambe.nn.distance.distance.DistanceModule

Implement a EuclideanDistance object.

forward(self, mat_1: Tensor, mat_2: Tensor)

Returns the squared euclidean distance between each element in mat_1 and each element in mat_2.

Parameters:

• mat_1 (torch.Tensor) – matrix of shape (n_1, n_features)
• mat_2 (torch.Tensor) – matrix of shape (n_2, n_features)

Returns:

dist – distance matrix of shape (n_1, n_2)

Return type:

torch.Tensor

class flambe.nn.distance.euclidean.EuclideanMean

Bases: flambe.nn.distance.distance.MeanModule

Implement a EuclideanMean object.

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

flambe.nn.distance.hyperbolic

Module Contents

flambe.nn.distance.hyperbolic.EPSILON = 1e-05

flambe.nn.distance.hyperbolic.arccosh(x)

Compute the arcosh, numerically stable.

flambe.nn.distance.hyperbolic.mdot(x, y)

Compute the inner product.

flambe.nn.distance.hyperbolic.dist(x, y)

Get the hyperbolic distance between x and y.

flambe.nn.distance.hyperbolic.project(x)

Project onto the hyeprboloid embedded in in n+1 dimensions.

flambe.nn.distance.hyperbolic.log_map(x, y)

Perform the log step.

flambe.nn.distance.hyperbolic.norm(x)

Compute the norm

flambe.nn.distance.hyperbolic.exp_map(x, y)

Perform the exp step.

flambe.nn.distance.hyperbolic.loss(x, y)

Get the loss for the optimizer.

class flambe.nn.distance.hyperbolic.HyperbolicDistance

Bases: flambe.nn.distance.distance.DistanceModule

Implement a HyperbolicDistance object.

forward(self, mat_1: Tensor, mat_2: Tensor)

Returns the squared euclidean distance between each element in mat_1 and each element in mat_2.

Parameters:

• mat_1 (torch.Tensor) – matrix of shape (n_1, n_features)
• mat_2 (torch.Tensor) – matrix of shape (n_2, n_features)

Returns:

dist – distance matrix of shape (n_1, n_2)

Return type:

torch.Tensor

class flambe.nn.distance.hyperbolic.HyperbolicMean

Bases: flambe.nn.distance.distance.MeanModule

Compute the mean point in the hyperboloid model.

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

Package Contents

class flambe.nn.distance.DistanceModule

Bases: flambe.nn.module.Module

Implement a DistanceModule object.

forward(self, mat_1: Tensor, mat_2: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

class flambe.nn.distance.MeanModule(detach_mean: bool = False)

Bases: flambe.nn.module.Module

Implement a MeanModule object.

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

class flambe.nn.distance.EuclideanDistance

Bases: flambe.nn.distance.distance.DistanceModule

Implement a EuclideanDistance object.

forward(self, mat_1: Tensor, mat_2: Tensor)

Returns the squared euclidean distance between each element in mat_1 and each element in mat_2.

Parameters:

• mat_1 (torch.Tensor) – matrix of shape (n_1, n_features)
• mat_2 (torch.Tensor) – matrix of shape (n_2, n_features)

Returns:

dist – distance matrix of shape (n_1, n_2)

Return type:

torch.Tensor

class flambe.nn.distance.EuclideanMean

Bases: flambe.nn.distance.distance.MeanModule

Implement a EuclideanMean object.

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

class flambe.nn.distance.CosineDistance(eps: float = 1e-08)

Bases: flambe.nn.distance.DistanceModule

Implement a CosineDistance object.

forward(self, mat_1: Tensor, mat_2: Tensor)

Returns the cosine distance between each element in mat_1 and each element in mat_2.

Parameters:

• mat_1 (torch.Tensor) – matrix of shape (n_1, n_features)
• mat_2 (torch.Tensor) – matrix of shape (n_2, n_features)

Returns:

dist – distance matrix of shape (n_1, n_2)

Return type:

torch.Tensor

class flambe.nn.distance.CosineMean

Bases: flambe.nn.distance.MeanModule

Implement a CosineMean object.

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

class flambe.nn.distance.HyperbolicDistance

Bases: flambe.nn.distance.distance.DistanceModule

Implement a HyperbolicDistance object.

forward(self, mat_1: Tensor, mat_2: Tensor)

Returns the squared euclidean distance between each element in mat_1 and each element in mat_2.

Parameters:

• mat_1 (torch.Tensor) – matrix of shape (n_1, n_features)
• mat_2 (torch.Tensor) – matrix of shape (n_2, n_features)

Returns:

dist – distance matrix of shape (n_1, n_2)

Return type:

torch.Tensor

class flambe.nn.distance.HyperbolicMean

Bases: flambe.nn.distance.distance.MeanModule

Compute the mean point in the hyperboloid model.

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

flambe.nn.distance.get_distance_module(metric: str) → DistanceModule

Get the distance module from a string alias.

Currently available: . euclidean . cosine . hyperbolic

Parameters:metric (str) – The distance metric to use Raises:ValueError – Unvalid distance string alias provided Returns:The instantiated distance module Return type:DistanceModule

flambe.nn.distance.get_mean_module(metric: str) → MeanModule

Get the mean module from a string alias.

Currently available: . euclidean . cosine . hyperbolic

Parameters:metric (str) – The distance metric to use Raises:ValueError – Unvalid distance string alias provided Returns:The instantiated distance module Return type:DistanceModule

Submodules

flambe.nn.cnn

Module Contents

flambe.nn.cnn.conv_block(conv_mod: nn.Module, activation: nn.Module, pooling: nn.Module, dropout: float, batch_norm: Optional[nn.Module] = None) → nn.Module

Return a convolutional block.

class flambe.nn.cnn.CNNEncoder(input_channels: int, channels: List[int], conv_dim: int = 2, kernel_size: Union[int, List[Union[Tuple[int, ...], int]]] = 3, activation: nn.Module = None, pooling: nn.Module = None, dropout: float = 0, batch_norm: bool = True, stride: int = 1, padding: int = 0)

Bases: flambe.nn.module.Module

Implements a multi-layer n-dimensional CNN.

This module can be used to create multi-layer CNN models.

cnn

The cnn submodule

Type:nn.Module

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:Union[Tensor, Tuple[Tensor, ..]]

flambe.nn.embedder

Module Contents

class flambe.nn.embedder.Embeddings

Bases: flambe.nn.module.Module, torch.nn.Embedding

Implement an Embedding module.

This object replicates the usage of nn.Embedding but registers the from_pretrained classmethod to be used inside a Flambé configuration, as this does not happen automatically during the registration of PyTorch objects.

classmethod from_pretrained(cls, embeddings: Tensor, freeze: bool = True, paddinx_idx: Optional[int] = None, max_norm: Optional[float] = None, norm_type: float = 2.0, scale_grad_by_freq: bool = False, sparse: bool = False)

Create Embedding instance from given 2-dimensional Tensor.

Parameters:

• embeddings (torch.Tensor) – FloatTensor containing weights for the Embedding. First dimension is being passed to Embedding as num_embeddings, second as embedding_dim.
• freeze (bool) – If True, the tensor does not get updated in the learning process. Default: True
• (int, optional) (padding_idx) – See module initialization documentation.
• max_norm (float, optional) – See module initialization documentation.
• norm_type (float, optional) – See module initialization documentation. Default 2.
• scale_grad_by_freq (bool, optional) – See module initialization documentation. Default False.
• (bool, optional) (sparse) – See module initialization documentation. Default False.

class flambe.nn.embedder.Embedder(embedding: nn.Embedding, encoder: Module, embedding_dropout: float = 0, pad_index: Optional[int] = 0)

Bases: flambe.nn.module.Module

Implements an Embedder module.

An Embedder takes as input a sequence of index tokens, and computes the corresponding embedded representations, and padding mask. The encoder may be initialized using a pretrained embedding matrix.

embeddings

The embedding layer

Type:Embedding

encoder

The sub-encoder that this object is wrapping

Type:Encoder

drop

The dropout layer

Type:nn.Dropout

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor, batch first Returns:The encoded output, as a float tensor. May return a state if the encoder is an RNN Return type:Union[Tensor, Tuple[Tensor, Tensor]]

flambe.nn.mlp

Module Contents

class flambe.nn.mlp.MLPEncoder(input_size: int, output_size: int, n_layers: int = 1, output_activation: Optional[nn.Module] = None, hidden_size: Optional[int] = None, hidden_activation: Optional[nn.Module] = None)

Bases: flambe.nn.module.Module

Implements a multi layer feed forward network.

This module can be used to create output layers, or more complex multi-layer feed forward networks.

seq

the sequence of layers and activations

Type:nn.Sequential

forward(self, data: torch.Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – input to the model of shape (batch_size, input_size) Returns:output – output of the model of shape (batch_size, output_size) Return type:torch.Tensor

flambe.nn.module

Module Contents

class flambe.nn.module.Module

Bases: flambe.compile.Component, torch.nn.Module

Base Flambé Module inteface.

Provides the exact same interface as Pytorch’s nn.Module, but extends it with a useful set of methods to access and clip parameters, as well as gradients.

This abstraction allows users to convert their modules with a single line change, by importing from Flambé instead. Just like every Pytorch module, a forward method should be implemented.

named_trainable_params :Iterator[Tuple[str, nn.Parameter]]

Get all the named parameters with requires_grad=True.

Returns:Iterator over the parameters and their name. Return type:Iterator[Tuple[str, nn.Parameter]]

trainable_params :Iterator[nn.Parameter]

Get all the parameters with requires_grad=True.

Returns:Iterator over the parameters Return type:Iterator[nn.Parameter]

gradient_norm :float

Compute the average gradient norm.

Returns:The current average gradient norm Return type:float

parameter_norm :float

Compute the average parameter norm.

Returns:The current average parameter norm Return type:float

num_parameters(self, trainable=False)

Gets the number of parameters in the model.

Returns:number of model params Return type:int

clip_params(self, threshold: float)

Clip the parameters to the given range.

Parameters:float – Values are clipped between -threshold, threshold

clip_gradient_norm(self, threshold: float)

Clip the norm of the gradient by the given value.

Parameters:float – Threshold to clip at

flambe.nn.mos

Module Contents

class flambe.nn.mos.MixtureOfSoftmax(input_size: int, output_size: int, k: int = 1, take_log: bool = True)

Bases: flambe.nn.module.Module

Implement the MixtureOfSoftmax output layer.

pi

softmax layer over the different softmax

Type:FullyConnected

layers

list of the k softmax layers

Type:[FullyConnected]

forward(self, data: Tensor)

Implement mixture of softmax for language modeling.

Parameters:data (torch.Tensor) – seq_len x batch_size x hidden_size Returns:out – output matrix of shape seq_len x batch_size x out_size Return type:Variable

flambe.nn.rnn

Module Contents

class flambe.nn.rnn.RNNEncoder(input_size: int, hidden_size: int, n_layers: int = 1, rnn_type: str = 'lstm', dropout: float = 0, bidirectional: bool = False, layer_norm: bool = False, highway_bias: float = 0, rescale: bool = True, enforce_sorted: bool = False)

Bases: flambe.nn.module.Module

Implements a multi-layer RNN.

This module can be used to create multi-layer RNN models, and provides a way to reduce to output of the RNN to a single hidden state by pooling the encoder states either by taking the maximum, average, or by taking the last hidden state before padding.

Padding is delt with by using torch’s PackedSequence.

rnn

The rnn submodule

Type:nn.Module

forward(self, data: Tensor, state: Optional[Tensor] = None, mask: Optional[Tensor] = None)

Performs a forward pass through the network.

Parameters:data (Tensor) – The input data, as a float tensor Returns:

• Tensor – The encoded output, as a float tensor
• Tensor – The encoded state, as a float tensor

class flambe.nn.rnn.PooledRNNEncoder(input_size: int, hidden_size: int, n_layers: int = 1, rnn_type: str = 'lstm', dropout: float = 0, bidirectional: bool = False, layer_norm: bool = False, highway_bias: float = 0, rescale: bool = True, pooling: str = 'last')

Bases: flambe.nn.module.Module

Implement an RNNEncoder with additional pooling.

This class can be used to obtan a single encoded output for an input sequence. It also ignores the state of the RNN.

forward(self, data: Tensor, state: Optional[Tensor] = None, mask: Optional[Tensor] = None)

Perform a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

flambe.nn.sequential

Module Contents

class flambe.nn.sequential.Sequential(**kwargs: Dict[str, Union[Module, torch.nn.Module]])

Bases: flambe.nn.Module

Implement a Sequential module.

This class can be used in the same way as torch’s nn.Sequential, with the difference that it accepts kwargs arguments.

forward(self, data: torch.Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – input to the model Returns:output – output of the model Return type:torch.Tensor

flambe.nn.softmax

Module Contents

class flambe.nn.softmax.SoftmaxLayer(input_size: int, output_size: int, take_log: bool = True)

Bases: flambe.nn.module.Module

Implement an SoftmaxLayer module.

Can be used to form a classifier out of any encoder.

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

Package Contents

class flambe.nn.Module

Bases: flambe.compile.Component, torch.nn.Module

Base Flambé Module inteface.

Provides the exact same interface as Pytorch’s nn.Module, but extends it with a useful set of methods to access and clip parameters, as well as gradients.

This abstraction allows users to convert their modules with a single line change, by importing from Flambé instead. Just like every Pytorch module, a forward method should be implemented.

named_trainable_params :Iterator[Tuple[str, nn.Parameter]]

Get all the named parameters with requires_grad=True.

Returns:Iterator over the parameters and their name. Return type:Iterator[Tuple[str, nn.Parameter]]

trainable_params :Iterator[nn.Parameter]

Get all the parameters with requires_grad=True.

Returns:Iterator over the parameters Return type:Iterator[nn.Parameter]

gradient_norm :float

Compute the average gradient norm.

Returns:The current average gradient norm Return type:float

parameter_norm :float

Compute the average parameter norm.

Returns:The current average parameter norm Return type:float

num_parameters(self, trainable=False)

Gets the number of parameters in the model.

Returns:number of model params Return type:int

clip_params(self, threshold: float)

Clip the parameters to the given range.

Parameters:float – Values are clipped between -threshold, threshold

clip_gradient_norm(self, threshold: float)

Clip the norm of the gradient by the given value.

Parameters:float – Threshold to clip at

class flambe.nn.SoftmaxLayer(input_size: int, output_size: int, take_log: bool = True)

Bases: flambe.nn.module.Module

Implement an SoftmaxLayer module.

Can be used to form a classifier out of any encoder.

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

class flambe.nn.MixtureOfSoftmax(input_size: int, output_size: int, k: int = 1, take_log: bool = True)

Bases: flambe.nn.module.Module

Implement the MixtureOfSoftmax output layer.

pi

softmax layer over the different softmax

Type:FullyConnected

layers

list of the k softmax layers

Type:[FullyConnected]

forward(self, data: Tensor)

Implement mixture of softmax for language modeling.

Parameters:data (torch.Tensor) – seq_len x batch_size x hidden_size Returns:out – output matrix of shape seq_len x batch_size x out_size Return type:Variable

class flambe.nn.Embeddings

Bases: flambe.nn.module.Module, torch.nn.Embedding

Implement an Embedding module.

This object replicates the usage of nn.Embedding but registers the from_pretrained classmethod to be used inside a Flambé configuration, as this does not happen automatically during the registration of PyTorch objects.

classmethod from_pretrained(cls, embeddings: Tensor, freeze: bool = True, paddinx_idx: Optional[int] = None, max_norm: Optional[float] = None, norm_type: float = 2.0, scale_grad_by_freq: bool = False, sparse: bool = False)

Create Embedding instance from given 2-dimensional Tensor.

Parameters:

• embeddings (torch.Tensor) – FloatTensor containing weights for the Embedding. First dimension is being passed to Embedding as num_embeddings, second as embedding_dim.
• freeze (bool) – If True, the tensor does not get updated in the learning process. Default: True
• (int, optional) (padding_idx) – See module initialization documentation.
• max_norm (float, optional) – See module initialization documentation.
• norm_type (float, optional) – See module initialization documentation. Default 2.
• scale_grad_by_freq (bool, optional) – See module initialization documentation. Default False.
• (bool, optional) (sparse) – See module initialization documentation. Default False.

class flambe.nn.Embedder(embedding: nn.Embedding, encoder: Module, embedding_dropout: float = 0, pad_index: Optional[int] = 0)

Bases: flambe.nn.module.Module

Implements an Embedder module.

An Embedder takes as input a sequence of index tokens, and computes the corresponding embedded representations, and padding mask. The encoder may be initialized using a pretrained embedding matrix.

embeddings

The embedding layer

Type:Embedding

encoder

The sub-encoder that this object is wrapping

Type:Encoder

drop

The dropout layer

Type:nn.Dropout

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor, batch first Returns:The encoded output, as a float tensor. May return a state if the encoder is an RNN Return type:Union[Tensor, Tuple[Tensor, Tensor]]

class flambe.nn.MLPEncoder(input_size: int, output_size: int, n_layers: int = 1, output_activation: Optional[nn.Module] = None, hidden_size: Optional[int] = None, hidden_activation: Optional[nn.Module] = None)

Bases: flambe.nn.module.Module

Implements a multi layer feed forward network.

This module can be used to create output layers, or more complex multi-layer feed forward networks.

seq

the sequence of layers and activations

Type:nn.Sequential

forward(self, data: torch.Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – input to the model of shape (batch_size, input_size) Returns:output – output of the model of shape (batch_size, output_size) Return type:torch.Tensor

class flambe.nn.RNNEncoder(input_size: int, hidden_size: int, n_layers: int = 1, rnn_type: str = 'lstm', dropout: float = 0, bidirectional: bool = False, layer_norm: bool = False, highway_bias: float = 0, rescale: bool = True, enforce_sorted: bool = False)

Bases: flambe.nn.module.Module

Implements a multi-layer RNN.

This module can be used to create multi-layer RNN models, and provides a way to reduce to output of the RNN to a single hidden state by pooling the encoder states either by taking the maximum, average, or by taking the last hidden state before padding.

Padding is delt with by using torch’s PackedSequence.

rnn

The rnn submodule

Type:nn.Module

forward(self, data: Tensor, state: Optional[Tensor] = None, mask: Optional[Tensor] = None)

Performs a forward pass through the network.

Parameters:data (Tensor) – The input data, as a float tensor Returns:

• Tensor – The encoded output, as a float tensor
• Tensor – The encoded state, as a float tensor

class flambe.nn.PooledRNNEncoder(input_size: int, hidden_size: int, n_layers: int = 1, rnn_type: str = 'lstm', dropout: float = 0, bidirectional: bool = False, layer_norm: bool = False, highway_bias: float = 0, rescale: bool = True, pooling: str = 'last')

Bases: flambe.nn.module.Module

Implement an RNNEncoder with additional pooling.

This class can be used to obtan a single encoded output for an input sequence. It also ignores the state of the RNN.

forward(self, data: Tensor, state: Optional[Tensor] = None, mask: Optional[Tensor] = None)

Perform a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:torch.Tensor

class flambe.nn.CNNEncoder(input_channels: int, channels: List[int], conv_dim: int = 2, kernel_size: Union[int, List[Union[Tuple[int, ...], int]]] = 3, activation: nn.Module = None, pooling: nn.Module = None, dropout: float = 0, batch_norm: bool = True, stride: int = 1, padding: int = 0)

Bases: flambe.nn.module.Module

Implements a multi-layer n-dimensional CNN.

This module can be used to create multi-layer CNN models.

cnn

The cnn submodule

Type:nn.Module

forward(self, data: Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – The input data, as a float tensor Returns:The encoded output, as a float tensor Return type:Union[Tensor, Tuple[Tensor, ..]]

class flambe.nn.Sequential(**kwargs: Dict[str, Union[Module, torch.nn.Module]])

Bases: flambe.nn.Module

Implement a Sequential module.

This class can be used in the same way as torch’s nn.Sequential, with the difference that it accepts kwargs arguments.

forward(self, data: torch.Tensor)

Performs a forward pass through the network.

Parameters:data (torch.Tensor) – input to the model Returns:output – output of the model Return type:torch.Tensor

flambe.runnable

Submodules

flambe.runnable.cluster_runnable

Module Contents

class flambe.runnable.cluster_runnable.ClusterRunnable(env: Optional[RemoteEnvironment] = None, **kwargs)

Bases: flambe.runnable.Runnable

Base class for all runnables that are able to run on cluster.

This type of Runnables must include logic in the ‘run’ method to deal with the fact that they could be running in a distributed cluster of machines.

To provide useful information about the cluster, a RemoteEnvironment object will be injected when running remotely.

config

The secrets that the user provides. For example, ‘config[“AWS”][“ACCESS_KEY”]’

Type:configparser.ConfigParser

env

The remote environment has information about the cluster where this ClusterRunnable will be running. IMPORTANT: this object will be available only when the ClusterRunnable is running remotely.

Type:RemoteEnvironment

setup(self, cluster: Cluster, extensions: Dict[str, str], force: bool, **kwargs)

Setup the cluster.

Parameters:

• cluster (Cluster) – The cluster where this Runnable will be running
• extensions (Dict[str, str]) – The ClusterRunnable extensions
• force (bool) – The force value provided to Flambe

setup_inject_env(self, cluster: Cluster, extensions: Dict[str, str], force: bool, **kwargs)

Call setup and inject the RemoteEnvironment

Parameters:

• cluster (Cluster) – The cluster where this Runnable will be running
• extensions (Dict[str, str]) – The ClusterRunnable extensions
• force (bool) – The force value provided to Flambe

set_serializable_attr(self, attr, value)

Set an attribute while keep supporting serializaton.

flambe.runnable.context

Module Contents

flambe.runnable.context.logger

class flambe.runnable.context.SafeExecutionContext(yaml_file: str)

Context manager handling the experiment’s creation and execution.

Parameters:yaml_file (str) – The experiment filename

__enter__(self)

A SafeExecutionContext should be used as a context manager to handle all possible errors in a clear way.

Examples

>>> with SafeExecutionContext(...) as ex:
>>>     ...

__exit__(self, exc_type: Optional[Type[BaseException]], exc_value: Optional[BaseException], tb: Optional[TracebackType])

Exit method for the context manager.

This method will catch any exception, and return True. This means that all exceptions produced in a SafeExecutionContext (used with the context manager) will not continue to raise.

Returns:True, as an exception should not continue to raise. Return type:Optional[bool]

preprocess(self, secrets: Optional[str] = None, download_ext: bool = True, install_ext: bool = False, import_ext: bool = True, **kwargs)

Preprocess the runnable file.

Looks for syntax errors, import errors, etc. Also injects the secrets into the runnables.

If this method runs and ends without exceptions, then the experiment is ok to be run. If this method raises an Error and the SafeExecutionContext is used as context manager, then the __exit__ method will be executed.

Parameters:

• secrets (Optional[str]) – Optional path to the secrets file
• install_ext (bool) – Whether to install the extensions or not. This process also downloads the remote extensions. Defaults to False
• install_ext – Whether to import the extensions or not. Defaults to True.

Returns:

A tuple containing the compiled Runnable and a dict containing the extensions the Runnable uses.

Return type:

Tuple[Runnable, Dict[str, str]]

Raises:

Exception – Depending on the error.

first_parse(self)

Check if valid YAML file and also load config

In this first parse the runnable does not get compiled because it could be a custom Runnable, so it needs the extensions to be imported first.

check_tags(self, content: str)

Check that all the tags are valid.

Parameters:content (str) – The content of the YAML file Raises:TagError

compile_runnable(self, content: str)

Compiles and returns the Runnable.

IMPORTANT: This method should run after all extensions were registered.

Parameters:content (str) – The runnable, as a YAML string Returns:The compiled experiment. Return type:Runnable

flambe.runnable.environment

Module Contents

class flambe.runnable.environment.RemoteEnvironment(key: str, orchestrator_ip: str, factories_ips: List[str], user: str, public_orchestrator_ip: Optional[str] = None, public_factories_ips: Optional[List[str]] = None, **kwargs)

Bases: flambe.compile.Registrable

This objects contains information about the cluster

This object will be available on the remote execution of the ClusterRunnable (as an attribute).

IMPORTANT: this object needs to be serializable, hence it Needs to be created using ‘compile’ method.

key

The key that communicates the cluster

Type:str

orchestrator_ip

The orchestrator visible IP for the factories (usually the private IP)

Type:str

factories_ips

The list of factories IPs visible for other factories and orchestrator (usually private IPs)

Type:List[str]

user

The username of all machines. This implementations assumes same usename for all machines

Type:str

public_orchestrator_ip

The public orchestrator IP, if available.

Type:Optional[str]

public_factories_ips

The public factories IPs, if available.

Type:Optional[List[str]]

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

Use representer to create yaml representation of node

classmethod from_yaml(cls, constructor: Any, node: Any, factory_name: str)

Use constructor to create an instance of cls

flambe.runnable.error

Module Contents

exception flambe.runnable.error.ProtocolError(message: str)

Bases: Exception

__repr__(self)

Override output message to show ProtocolError

Returns:The output message Return type:str

exception flambe.runnable.error.LinkError(block_id: str, target_block_id: str)

Bases: flambe.runnable.error.ProtocolError

exception flambe.runnable.error.SearchComponentError(block_id: str)

Bases: flambe.runnable.error.ProtocolError

exception flambe.runnable.error.UnsuccessfulRunnableError

Bases: RuntimeError

exception flambe.runnable.error.RunnableFileError

Bases: Exception

exception flambe.runnable.error.ResourceError

Bases: flambe.runnable.error.RunnableFileError

exception flambe.runnable.error.NonExistentResourceError

Bases: flambe.runnable.error.RunnableFileError

exception flambe.runnable.error.ExistentResourceError

Bases: flambe.runnable.error.RunnableFileError

exception flambe.runnable.error.ParsingRunnableError

Bases: flambe.runnable.error.RunnableFileError

exception flambe.runnable.error.TagError

Bases: flambe.runnable.error.RunnableFileError

exception flambe.runnable.error.MissingSecretsError

Bases: Exception

flambe.runnable.runnable

Module Contents

class flambe.runnable.runnable.Runnable(**kwargs)

Bases: flambe.compile.MappedRegistrable

Base class for all runnables.

A runnable contains a single run method that needs to be implemented. It must contain all the logic for the runnable.

Each runnable has also access to the secrets the user provides.

Examples of Runnables: Experiment, Cluster

config

The secrets that the user provides. For example, ‘config[“AWS”][“ACCESS_KEY”]’

Type:configparser.ConfigParser

inject_secrets(self, secrets: str)

Inject the secrets once the Runnable was created.

Parameters:secrets (str) – The filepath to the secrets

inject_extensions(self, extensions: Dict[str, str])

Inject extensions to the Runnable

Parameters:extensions (Dict[str, str]) – The extensions

run(self, **kwargs)

Run the runnable.

Each implementation will implement its own logic, with the parameters it needs.

parse(self)

Parse the runnable to determine if it’s able to run. :raises: ParsingExperimentError – In case a parsing error is found.

flambe.runnable.utils

Module Contents

flambe.runnable.utils._contains_path(nested_dict) → bool

Whether the nested dict contains any value that could be a path.

Parameters:nested_dict – The nested dict to evaluate Returns: Return type:bool

flambe.runnable.utils.is_dev_mode() → bool

Detects if flambe was installed in editable mode.

For more information: https://pip.pypa.io/en/latest/reference/pip_install/#editable-installs

Returns: Return type:bool

flambe.runnable.utils.get_flambe_repo_location() → str

Return where flambe repository is located

Returns:The local path where flambe is located Return type:str Raises:ValueError – If flambe was not installed in editable mode

flambe.runnable.utils.get_commit_hash() → str

Get the commit hash of the current flambe development package.

This will only work if flambe was install from github in dev mode.

Returns:The commit hash Return type:str Raises:Exception – In case flambe was not installed in dev mode.

flambe.runnable.utils.rsync_hosts(orch_ip: str, factories_ips: List[str], user: str, folder: str, key: str, exclude: List[str]) → None

Rsync the hosts in the cluster.

IMPORTANT: this method is intended to be run in the cluster.

Parameters:

• orch_ip (str) – The Orchestrator’s IP that is visible by the factories (usually the private IP)
• factories_ips (List[str]) – The factories IPs that are visible by the Orchestrator (usually the private IPs)
• user (str) – The username of all machines. IMPORTANT: only machines with same username are supported
• key (str) – The key that communicate all machines
• exclude (List[str]) – A list of files to be excluded in the rsync

Package Contents

class flambe.runnable.Runnable(**kwargs)

Bases: flambe.compile.MappedRegistrable

Base class for all runnables.

A runnable contains a single run method that needs to be implemented. It must contain all the logic for the runnable.

Each runnable has also access to the secrets the user provides.

Examples of Runnables: Experiment, Cluster

config

The secrets that the user provides. For example, ‘config[“AWS”][“ACCESS_KEY”]’

Type:configparser.ConfigParser

inject_secrets(self, secrets: str)

Inject the secrets once the Runnable was created.

Parameters:secrets (str) – The filepath to the secrets

inject_extensions(self, extensions: Dict[str, str])

Inject extensions to the Runnable

Parameters:extensions (Dict[str, str]) – The extensions

run(self, **kwargs)

Run the runnable.

Each implementation will implement its own logic, with the parameters it needs.

parse(self)

Parse the runnable to determine if it’s able to run. :raises: ParsingExperimentError – In case a parsing error is found.

class flambe.runnable.ClusterRunnable(env: Optional[RemoteEnvironment] = None, **kwargs)

Bases: flambe.runnable.Runnable

Base class for all runnables that are able to run on cluster.

This type of Runnables must include logic in the ‘run’ method to deal with the fact that they could be running in a distributed cluster of machines.

To provide useful information about the cluster, a RemoteEnvironment object will be injected when running remotely.

config

The secrets that the user provides. For example, ‘config[“AWS”][“ACCESS_KEY”]’

Type:configparser.ConfigParser

env

The remote environment has information about the cluster where this ClusterRunnable will be running. IMPORTANT: this object will be available only when the ClusterRunnable is running remotely.

Type:RemoteEnvironment

setup(self, cluster: Cluster, extensions: Dict[str, str], force: bool, **kwargs)

Setup the cluster.

Parameters:

• cluster (Cluster) – The cluster where this Runnable will be running
• extensions (Dict[str, str]) – The ClusterRunnable extensions
• force (bool) – The force value provided to Flambe

setup_inject_env(self, cluster: Cluster, extensions: Dict[str, str], force: bool, **kwargs)

Call setup and inject the RemoteEnvironment

Parameters:

• cluster (Cluster) – The cluster where this Runnable will be running
• extensions (Dict[str, str]) – The ClusterRunnable extensions
• force (bool) – The force value provided to Flambe

set_serializable_attr(self, attr, value)

Set an attribute while keep supporting serializaton.

class flambe.runnable.SafeExecutionContext(yaml_file: str)

Context manager handling the experiment’s creation and execution.

Parameters:yaml_file (str) – The experiment filename

__enter__(self)

A SafeExecutionContext should be used as a context manager to handle all possible errors in a clear way.

Examples

>>> with SafeExecutionContext(...) as ex:
>>>     ...

__exit__(self, exc_type: Optional[Type[BaseException]], exc_value: Optional[BaseException], tb: Optional[TracebackType])

Exit method for the context manager.

This method will catch any exception, and return True. This means that all exceptions produced in a SafeExecutionContext (used with the context manager) will not continue to raise.

Returns:True, as an exception should not continue to raise. Return type:Optional[bool]

preprocess(self, secrets: Optional[str] = None, download_ext: bool = True, install_ext: bool = False, import_ext: bool = True, **kwargs)

Preprocess the runnable file.

Looks for syntax errors, import errors, etc. Also injects the secrets into the runnables.

If this method runs and ends without exceptions, then the experiment is ok to be run. If this method raises an Error and the SafeExecutionContext is used as context manager, then the __exit__ method will be executed.

Parameters:

• secrets (Optional[str]) – Optional path to the secrets file
• install_ext (bool) – Whether to install the extensions or not. This process also downloads the remote extensions. Defaults to False
• install_ext – Whether to import the extensions or not. Defaults to True.

Returns:

A tuple containing the compiled Runnable and a dict containing the extensions the Runnable uses.

Return type:

Tuple[Runnable, Dict[str, str]]

Raises:

Exception – Depending on the error.

first_parse(self)

Check if valid YAML file and also load config

In this first parse the runnable does not get compiled because it could be a custom Runnable, so it needs the extensions to be imported first.

check_tags(self, content: str)

Check that all the tags are valid.

Parameters:content (str) – The content of the YAML file Raises:TagError

compile_runnable(self, content: str)

Compiles and returns the Runnable.

IMPORTANT: This method should run after all extensions were registered.

Parameters:content (str) – The runnable, as a YAML string Returns:The compiled experiment. Return type:Runnable

class flambe.runnable.RemoteEnvironment(key: str, orchestrator_ip: str, factories_ips: List[str], user: str, public_orchestrator_ip: Optional[str] = None, public_factories_ips: Optional[List[str]] = None, **kwargs)

Bases: flambe.compile.Registrable

This objects contains information about the cluster

This object will be available on the remote execution of the ClusterRunnable (as an attribute).

IMPORTANT: this object needs to be serializable, hence it Needs to be created using ‘compile’ method.

key

The key that communicates the cluster

Type:str

orchestrator_ip

The orchestrator visible IP for the factories (usually the private IP)

Type:str

factories_ips

The list of factories IPs visible for other factories and orchestrator (usually private IPs)

Type:List[str]

user

The username of all machines. This implementations assumes same usename for all machines

Type:str

public_orchestrator_ip

The public orchestrator IP, if available.

Type:Optional[str]

public_factories_ips

The public factories IPs, if available.

Type:Optional[List[str]]

classmethod to_yaml(cls, representer: Any, node: Any, tag: str)

Use representer to create yaml representation of node

classmethod from_yaml(cls, constructor: Any, node: Any, factory_name: str)

Use constructor to create an instance of cls

flambe.runner

Submodules

flambe.runner.garbage_collector

Script to run the garbage collector.

The garbage collector is in charge of destroying the factories once the experiment is over.

All useful information was sent to the orchestrator machine, so the Factories can be safely deleted.

To do this, the GarbageCollector gets the tasks from the Redis DB running in the Orchestrator. Once all tasks are over, it gets the factories from the Redis DB also (that’s why the Instances should be pickeable) and terminates them.

Module Contents

flambe.runner.garbage_collector.logger

flambe.runner.garbage_collector.main(args: argparse.Namespace) → None

flambe.runner.garbage_collector.parser

flambe.runner.report_site_run

Script to run the report web site

It takes the app defined in flambe.remote.webapp.app and runs it.

Module Contents

flambe.runner.report_site_run.logger

flambe.runner.report_site_run.launch_tensorboard(tracking_address) → Optional[str]

flambe.runner.report_site_run.parser

flambe.runner.run

Local run script for flambe.

Module Contents

flambe.runner.run.TORCH_TAG_PREFIX = torch

flambe.runner.run.TUNE_TAG_PREFIX = tune

flambe.runner.run.main(args: argparse.Namespace) → None

Execute command based on given config

flambe.runner.run.parser

flambe.runner.utils

Module Contents

flambe.runner.utils.logger

flambe.runner.utils.MB

flambe.runner.utils.WARN_LIMIT_MB = 100

flambe.runner.utils.get_folder_size_MB(path: str) → float

Return the size of a folder in MB.

Parameters:path (str) – The path to the folder Returns:The size in MB Return type:float

flambe.runner.utils.check_system_reqs() → None

Run system checks and prepare the system before a run.

This method should:

• Create folders, files that are needed for flambe
• Raise errors in case requirements are not met. This should

run under the SafeExecutionContext, so errors will be handled * Warn the user in case something needs attention.

flambe.sampler

Submodules

flambe.sampler.base

Module Contents

flambe.sampler.base._bfs(obs: List, obs_idx: int) → Tuple[Dict[int, List], Set[Tuple[int, ...]]]

Given a single obs, itself a nested list, run BFS.

This function enumerates:

1. The lengths of each of the intermediary lists, by depth
2. All paths to the child nodes

Parameters:

• obs (List) – A nested list of lists of arbitrary depth, with the child nodes, i.e. deepest list elements, as `torch.Tensor`s
• obs_idx (int) – The index of obs in the batch.

Returns:

• Set[Tuple[int]] – A set of all distinct paths to all children
• Dict[int, List[int]] – A map containing the lengths of all intermediary lists, by depth

flambe.sampler.base._batch_from_nested_col(col: Tuple, pad: int, batch_first: bool) → torch.Tensor

Compose a batch padded to the max-size along each dimension.

Parameters:col (List) –

A nested list of lists of arbitrary depth, with the child nodes, i.e. deepest list elements, as `torch.Tensor`s

For example, a col might be:

[

[torch.Tensor([1, 2]), torch.Tensor([3, 4, 5])], [torch.Tensor([5, 6, 7]), torch.Tensor([4, 5]),

torch.Tensor([5, 6, 7, 8])]

]

Level 1 sizes: [2, 3] Level 2 sizes: [2, 3]; [3, 2, 4]

The max-sizes along each dimension are:

• Dim 1: 3
• Dim 2: 4

As such, since this column contains 2 elements, with max-sizes 3 and 4 along the nested dimensions, our resulting batch would have size (4, 3, 2), and the padded `Tensor`s would be inserted at their respective locations.

Returns:A (n+1)-dimensional torch.Tensor, where n is the nesting depth, padded to the max-size along each dimension Return type:torch.Tensor

flambe.sampler.base.collate_fn(data: List[Tuple[torch.Tensor, ...]], pad: int, batch_first: bool) → Tuple[torch.Tensor, ...]

Turn a list of examples into a mini-batch.

Handles padding on the fly on simple sequences, as well as nested sequences.

Parameters:

• data (List[Tuple[torch.Tensor, ..]]) – The list of sampled examples. Each example is a tuple, each dimension representing a column from the original dataset
• pad (int) – The padding index
• batch_first (bool) – Whether to place the batch dimension first

Returns:

The output batch of tensors

Return type:

Tuple[torch.Tensor, ..]

class flambe.sampler.base.BaseSampler(batch_size: int = 64, shuffle: bool = True, pad_index: Union[int, Sequence[int]] = 0, n_workers: int = 0, pin_memory: bool = False, batch_first: bool = True, seed: Optional[int] = None, downsample: Optional[float] = None, downsample_seed: Optional[int] = None, drop_last: bool = False)

Bases: flambe.sampler.sampler.Sampler

Implements a BaseSampler object.

This is the most basic implementation of a sampler. It uses Pytorch’s DataLoader object internally, and offers the possiblity to override the sampling of the examples and how to from a batch from them.

sample(self, data: Sequence[Sequence[torch.Tensor]], n_epochs: int = 1)

Sample from the list of features and yields batches.

Parameters:

• data (Sequence[Sequence[torch.Tensor, ..]]) – The input data to sample from
• n_epochs (int, optional) – The number of epochs to run in the output iterator. Use -1 to run infinitely.

Yields:

Iterator[Tuple[Tensor]] – A batch of data, as a tuple of Tensors

length(self, data: Sequence[Sequence[torch.Tensor]])

Return the number of batches in the sampler.

Parameters:data (Sequence[Sequence[torch.Tensor, ..]]) – The input data to sample from Returns:The number of batches that would be created per epoch Return type:int

flambe.sampler.episodic

Module Contents

class flambe.sampler.episodic.EpisodicSampler(n_support: int, n_query: int, n_episodes: int, n_classes: int = None, pad_index: int = 0, balance_query: bool = False)

Bases: flambe.sampler.Sampler

Implements an EpisodicSample object.

sample(self, data: Sequence[Sequence[torch.Tensor]], n_epochs: int = 1)

Sample from the list of features and yields batches.

Parameters:

• data (Sequence[Sequence[torch.Tensor, torch.Tensor]]) – The input data as a list of (source, target) pairs
• n_epochs (int, optional) – The number of epochs to run in the output iterator. For this object, the total number of batches will be (n_episodes * n_epochs)

Yields:

Iterator[Tuple[Tensor, Tensor, Tensor, Tensor]] – In order: the query_source, the query_target the support_source, and the support_target tensors. For sequences, the batch is used as first dimension.

length(self, data: Sequence[Sequence[torch.Tensor]])

Return the number of batches in the sampler.

Parameters:data (Sequence[Sequence[torch.Tensor, ..]]) – The input data to sample from Returns:The number of batches that would be created per epoch Return type:int

flambe.sampler.sampler

Module Contents

class flambe.sampler.sampler.Sampler

Bases: flambe.compile.Component

Base Sampler interface.

Objects implementing this interface should implement two methods:

• sample: takes a set of data and returns an iterator

• lenght: takes a set of data and return the length of the

  iterator that would be given by the sample method

Sampler objects are used inside the Trainer to provide the data to the models. Note that pushing the data to the appropriate device is usually done inside the Trainer.

sample(self, data: Sequence[Sequence[torch.Tensor]], n_epochs: int = 1)

Sample from the list of features and yields batches.

Parameters:

• data (Sequence[Sequence[torch.Tensor, ..]]) – The input data to sample from
• n_epochs (int, optional) – The number of epochs to run in the output iterator.

Yields:

Iterator[Tuple[Tensor]] – A batch of data, as a tuple of Tensors

length(self, data: Sequence[Sequence[torch.Tensor]])

Return the number of batches in the sampler.

Parameters:data (Sequence[Sequence[torch.Tensor, ..]]) – The input data to sample from Returns:The number of batches that would be created per epoch Return type:int

Package Contents

class flambe.sampler.Sampler

Bases: flambe.compile.Component

Base Sampler interface.

Objects implementing this interface should implement two methods:

• sample: takes a set of data and returns an iterator

• lenght: takes a set of data and return the length of the

  iterator that would be given by the sample method

Sampler objects are used inside the Trainer to provide the data to the models. Note that pushing the data to the appropriate device is usually done inside the Trainer.

sample(self, data: Sequence[Sequence[torch.Tensor]], n_epochs: int = 1)

Sample from the list of features and yields batches.

Parameters:

• data (Sequence[Sequence[torch.Tensor, ..]]) – The input data to sample from
• n_epochs (int, optional) – The number of epochs to run in the output iterator.

Yields:

Iterator[Tuple[Tensor]] – A batch of data, as a tuple of Tensors

length(self, data: Sequence[Sequence[torch.Tensor]])

Return the number of batches in the sampler.

Parameters:data (Sequence[Sequence[torch.Tensor, ..]]) – The input data to sample from Returns:The number of batches that would be created per epoch Return type:int

class flambe.sampler.BaseSampler(batch_size: int = 64, shuffle: bool = True, pad_index: Union[int, Sequence[int]] = 0, n_workers: int = 0, pin_memory: bool = False, batch_first: bool = True, seed: Optional[int] = None, downsample: Optional[float] = None, downsample_seed: Optional[int] = None, drop_last: bool = False)

Bases: flambe.sampler.sampler.Sampler

Implements a BaseSampler object.

This is the most basic implementation of a sampler. It uses Pytorch’s DataLoader object internally, and offers the possiblity to override the sampling of the examples and how to from a batch from them.

sample(self, data: Sequence[Sequence[torch.Tensor]], n_epochs: int = 1)

Sample from the list of features and yields batches.

Parameters:

• data (Sequence[Sequence[torch.Tensor, ..]]) – The input data to sample from
• n_epochs (int, optional) – The number of epochs to run in the output iterator. Use -1 to run infinitely.

Yields:

Iterator[Tuple[Tensor]] – A batch of data, as a tuple of Tensors

length(self, data: Sequence[Sequence[torch.Tensor]])

Return the number of batches in the sampler.

Parameters:data (Sequence[Sequence[torch.Tensor, ..]]) – The input data to sample from Returns:The number of batches that would be created per epoch Return type:int

flambe.tokenizer

Submodules

flambe.tokenizer.char

Module Contents

class flambe.tokenizer.char.CharTokenizer

Bases: flambe.tokenizer.Tokenizer

Implement a character level tokenizer.

tokenize(self, example: str)

Tokenize an input example.

Parameters:example (str) – The input example, as a string Returns:The output charachter tokens, as a list of strings Return type:List[str]

flambe.tokenizer.label

Module Contents

class flambe.tokenizer.label.LabelTokenizer(multilabel_sep: Optional[str] = None)

Bases: flambe.tokenizer.Tokenizer

Base label tokenizer.

This object tokenizes string labels into a list of a single or multiple elements, depending on the provided separator.

tokenize(self, example: str)

Tokenize an input example.

Parameters:example (str) – The input example, as a string Returns:The output tokens, as a list of strings Return type:List[str]

flambe.tokenizer.tokenizer

Module Contents

class flambe.tokenizer.tokenizer.Tokenizer

Bases: flambe.Component

Base interface to a Tokenizer object.

Tokenizers implement the tokenize method, which takes a string as input and produces a list of strings as output.

tokenize(self, example: str)

Tokenize an input example.

Parameters:example (str) – The input example, as a string Returns:The output tokens, as a list of strings Return type:List[str]

__call__(self, example: str)

Make a tokenizer callable.

flambe.tokenizer.word

Module Contents

class flambe.tokenizer.word.WordTokenizer

Bases: flambe.tokenizer.Tokenizer

Implement a word level tokenizer.

tokenize(self, example: str)

Tokenize an input example.

Parameters:example (str) – The input example, as a string Returns:The output word tokens, as a list of strings Return type:List[str]

class flambe.tokenizer.word.NGramsTokenizer(ngrams: Union[int, List[int]] = 1)

Bases: flambe.tokenizer.Tokenizer

Implement a n-gram tokenizer

Examples

>>> t = NGramsTokenizer(ngrams=2).tokenize("hi how are you?")
['hi, how', 'how are', 'are you?']

>>> t = NGramsTokenizer(ngrams=[1,2]).tokenize("hi how are you?")
['hi,', 'how', 'are', 'you?', 'hi, how', 'how are', 'are you?']

Parameters:ngrams (Union[int, List[int]]) – An int or a list of ints. If it’s a list of ints, all n-grams (for each int) will be considered in the tokenizer.

static _tokenize(example: str, n: int)

Tokenize an input example using ngrams.

tokenize(self, example: str)

Tokenize an input example.

Parameters:example (str) – The input example, as a string. Returns:The output word tokens, as a list of strings Return type:List[str]

Package Contents

class flambe.tokenizer.Tokenizer

Bases: flambe.Component

Base interface to a Tokenizer object.

Tokenizers implement the tokenize method, which takes a string as input and produces a list of strings as output.

tokenize(self, example: str)

Tokenize an input example.

Parameters:example (str) – The input example, as a string Returns:The output tokens, as a list of strings Return type:List[str]

__call__(self, example: str)

Make a tokenizer callable.

class flambe.tokenizer.CharTokenizer

Bases: flambe.tokenizer.Tokenizer

Implement a character level tokenizer.

tokenize(self, example: str)

Tokenize an input example.

Parameters:example (str) – The input example, as a string Returns:The output charachter tokens, as a list of strings Return type:List[str]

class flambe.tokenizer.WordTokenizer

Bases: flambe.tokenizer.Tokenizer

Implement a word level tokenizer.

tokenize(self, example: str)

Tokenize an input example.

Parameters:example (str) – The input example, as a string Returns:The output word tokens, as a list of strings Return type:List[str]

class flambe.tokenizer.NGramsTokenizer(ngrams: Union[int, List[int]] = 1)

Bases: flambe.tokenizer.Tokenizer

Implement a n-gram tokenizer

Examples

>>> t = NGramsTokenizer(ngrams=2).tokenize("hi how are you?")
['hi, how', 'how are', 'are you?']

>>> t = NGramsTokenizer(ngrams=[1,2]).tokenize("hi how are you?")
['hi,', 'how', 'are', 'you?', 'hi, how', 'how are', 'are you?']

Parameters:ngrams (Union[int, List[int]]) – An int or a list of ints. If it’s a list of ints, all n-grams (for each int) will be considered in the tokenizer.

static _tokenize(example: str, n: int)

Tokenize an input example using ngrams.

tokenize(self, example: str)

Tokenize an input example.

Parameters:example (str) – The input example, as a string. Returns:The output word tokens, as a list of strings Return type:List[str]

class flambe.tokenizer.LabelTokenizer(multilabel_sep: Optional[str] = None)

Bases: flambe.tokenizer.Tokenizer

Base label tokenizer.

This object tokenizes string labels into a list of a single or multiple elements, depending on the provided separator.

tokenize(self, example: str)

Tokenize an input example.

Parameters:example (str) – The input example, as a string Returns:The output tokens, as a list of strings Return type:List[str]

flambe.vision