Finetune-ESM

Scalable Protein Language Model Finetuning with Distributed Learning and Advanced Training Techniques such as LoRA.


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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

This project explores scalable and efficient finetuning of protein language models like ESM-2 using advanced training techniques like FSDP (Fully Sharded Data Parallel) and LoRA (Low-Rank Adaptation).

Highlights

• Distributed training: Leverage distributed computing for finetuning large protein language models on multiple GPUs.
• Advanced techniques: Explore LoRA and other methods to improve finetuning efficiency and performance.
• Reproducibility: Track and manage finetuning experiments using tools like MLflow.

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Built With

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Getting Started

1. Clone the repo:

git clone https://github.com/naity/finetune-esm.git

2. Run the train.py script to see a list of available parameters:

python finetune-esm/train.py --help

Prerequisites

The requirements.txt file lists the Python packages that need to be installed in order to run the scripts. Please use the command below for installation.

pip install -r requirements.txt

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Usage

In this example, we will finetune ESM-2 for the CAFA 5 Protein Function Prediction Challenge to predict the biological function of a protein based on its primary sequence. I have already preprocessed the data and formatted the problem as a multi-class, multi-label problem. This means that for a given protein sequence, we will predict whether it is positive for each of the 100 preselected Gene Ontology (GO) terms. Thus, the target for each protein sequence is a binary vector with a length of 100.

The processed datasets can be downloaded from here. Details about the preprocessing steps can be found in the notebooks/cafa5_data_processing.ipynb notebook.

Run the following example command to finetune ESM-2 models with the processed datasets. Here, we are using the smallest model esm2_t6_8M_UR50D with 1 GPU and the LoRA approach. If you want to finetune a larger model and have multiple GPUs, please adjust num_workers and/or num-devices accordingly.

python finetune_esm/train.py \
  --experiment-name esm2_t6_8M_UR50D_lora \
  --dataset-loc data/cafa5/top100_train_split.parquet \
  --targets-loc data/cafa5/train_bp_top100_targets.npy \
  --esm-model esm2_t6_8M_UR50D \
  --num-workers 1 \
  --num-devices 1 \
  --training-mode lora \
  --learning-rate 0.0001 \
  --num-epochs 5

Once training is done, we can use MLflow to view the experiment using:

mlflow server --host 127.0.0.1 --port 8080 --backend-store-uri ./finetune_results/mlflow

Below are screenshots of the example experiment. We can view parameters, artifacts, and visualize the metrics results.

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Roadmap

• Data Processing
• Training
• Serving

See the open issues for a full list of proposed features (and known issues).

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

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Acknowledgments

• Made With ML

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