GT4SD (Generative Toolkit for Scientific Discovery)

License: MIT Code style: black Contributions

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The GT4SD (Generative Toolkit for Scientific Discovery) is an open-source platform to accelerate hypothesis generation in the scientific discovery process. It provides a library for making state-of-the-art generative AI models easier to use.

Installation

pip

You can install gt4sd directly from GitHub:

pip install git+https://github.com/GT4SD/gt4sd-core

Development setup & installation

If you would like to contribute to the package, we recommend the following development setup: Clone the gt4sd-core repository:

git clone [email protected]:GT4SD/gt4sd-core.git
cd gt4ds-core
conda env create -f conda.yml
conda activate gt4sd
pip install -e .

Learn more in CONTRIBUTING.md

Supported packages

Beyond implementing various generative modeling inference and training pipelines GT4SD is designed to provide a high-level API that implement an harmonized interface for several existing packages:

  • GuacaMol: inference pipelines for the baselines models.
  • MOSES: inference pipelines for the baselines models.
  • TAPE: encoder modules compatible with the protein language models.
  • PaccMann: inference pipelines for all algorithms of the PaccMann family as well as traiing pipelines for the generative VAEs.
  • transformers: training and inference pipelines for generative models from the HuggingFace Models

Using GT4SD

Running inference pipelines

Running an algorithm is as easy as typing:

from gt4sd.algorithms.conditional_generation.paccmann_rl.core import (
    PaccMannRLProteinBasedGenerator, PaccMannRL
)
target = 'MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTT'
# algorithm configuration with default parameters
configuration = PaccMannRLProteinBasedGenerator()
# instantiate the algorithm for sampling
algorithm = PaccMannRL(configuration=configuration, target=target)
items = list(algorithm.sample(10))
print(items)

Or you can use the ApplicationRegistry to run an algorithm instance using a serialized representation of the algorithm:

from gt4sd.algorithms.registry import ApplicationsRegistry
target = 'MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTT'
algorithm = ApplicationsRegistry.get_application_instance(
    target=target,
    algorithm_type='conditional_generation',
    domain='materials',
    algorithm_name='PaccMannRL',
    algorithm_application='PaccMannRLProteinBasedGenerator',
    generated_length=32,
    # include additional configuration parameters as **kwargs
)
items = list(algorithm.sample(10))
print(items)

Running training pipelines via the CLI command

GT4SD provides a trainer client based on the gt4sd-trainer CLI command. The trainer currently supports training pipelines for language modeling (language-modeling-trainer), PaccMann (paccmann-vae-trainer) and Granular (granular-trainer, multimodal compositional autoencoders).

$ gt4sd-trainer --help
usage: gt4sd-trainer [-h] --training_pipeline_name TRAINING_PIPELINE_NAME
                     [--configuration_file CONFIGURATION_FILE]

optional arguments:
  -h, --help            show this help message and exit
  --training_pipeline_name TRAINING_PIPELINE_NAME
                        Training type of the converted model, supported types:
                        granular-trainer, language-modeling-trainer, paccmann-
                        vae-trainer. (default: None)
  --configuration_file CONFIGURATION_FILE
                        Configuration file for the trainining. It can be used
                        to completely by-pass pipeline specific arguments.
                        (default: None)

To launch a training you have two options.

You can either specify the training pipeline and the path of a configuration file that contains the needed training parameters:

gt4sd-trainer  --training_pipeline_name ${TRAINING_PIPELINE_NAME} --configuration_file ${CONFIGURATION_FILE}

Or you can provide directly the needed parameters as argumentsL

gt4sd-trainer  --training_pipeline_name language-modeling-trainer --type mlm --model_name_or_path mlm --training_file /pah/to/train_file.jsonl --validation_file /path/to/valid_file.jsonl

To get more info on a specific training pipeleins argument simply type:

gt4sd-trainer --training_pipeline_name ${TRAINING_PIPELINE_NAME} --help

References

If you use gt4sd in your projects, please consider citing the following:

@software{GT4SD,
author = {GT4SD Team},
month = {2},
title = {{GT4SD (Generative Toolkit for Scientific Discovery)}},
url = {https://github.com/GT4SD/gt4sd-core},
version = {main},
year = {2022}
}

License

The gt4sd codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages.

GitHub

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