Ba3l: the sacred lightening of a torch!

The researcher friendly pytorch environment. Ba3l= sacred+pytorch-lightening

Docs

View the docs here

Why Ba3l?

The main design principle is to be ETHIC (Every THing Is a Config). Sacred provides a very research friendly tools for configuration and logging experiments. Lightening for all the features that you'd expect from the pytorch wraper! We redesign much of lightening Object-Oriented desgin to fit with ETHIC. The result is scalable, very modular, very configuarble framework.

Caution

Sacred and pytorch-lightening should not be installed on the same environment as Ba3l, since there is a modified and packaged version with pyzeus.

If the Sacred and pytorch-lightening versions were not installed automaticaly. you can use the following commands:

pip install -e 'git+https://github.com/kkoutini/pytorch-lightning@v0.0.1#egg=pytorch-lightning'
# or
pip install -e 'git+https://github.com/kkoutini/sacred@v0.0.1#egg=sacred' 

Sacred modifications

• Allow adding configs dynamically from command line.
• Allow Ingredients to override configuration of there sub-ingredients. For example, you can override any dataset configurations from the experiment configs.
• Add feature to automaticaly capture a function or a class and add its arguments as config. This allows to use the default values of a function as a sacred config, instead of explicitly adding them.
• Defining a new type of config CMD which allows to specify a function by it's name in config. Examples iter or dataset.

Pytorch-lightening modifications

• Allow multiple inhertince to use pytorch-lightening trainer as sacred ingredient.

Features

• We Kept all the features of sacred and pytorch-lightening.
• We use sacred to capture the workflow of lightening which allows to modify the behaviour of lightening using sacred configs (default configs, named configs, command-line configs, etc... refer to sacred docs).
• All the loggers supported by both lightening and Sacred (WIP), It's recommended to use sacred for meta analysis for a big chunk of experiments and tensorboard (for example) for specific experiment.
• We introduced CMD config, a special string that refers to a function (sacred command). This allows to seamlessly using functions in the config. and allows further flexibility to modify the behaviour using only config.
• We allow capturing of any function and class, and adding it's arguments to the config (with the default values). You don't have to explicitly declare the config params. For example: capturing the Dataloader will add batch_size, shuffle, etc.. to the config and pass them automatic when the function is called. for a brief overview take a look at the tests/examples.

Examples