# 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 arg2 --kwarg1 value1 --kwarg2 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

name: example

pipeline:
stage_0: !Script
script: my_project.train  # my_project is the name of the module
args: ['arg1', 'arg2']
kwargs:
kwarg1: !g [1, 5]  # Run a grid search over any arguments to your script
kwarg2: '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: