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Cellarium ML: a machine learning framework for single-cell biology

Cellarium ML is a PyTorch Lightning-based library for distributed single-cell data analysis. It provides tools for training deep learning models on large-scale single-cell datasets, including distributed data loading, model training, and evaluation. Designed to be modular and extensible, Cellarium ML allows users to easily define custom models, data transformations, and training pipelines.


Code Organization

The code is organized as follows:

cellarium/
└── ml/
    ├── "callbacks"        # Custom PyTorch Lightning callbacks
    ├── "core"             # Essential components
    │   ├── "CellariumModule"              # PyTorch Lightning Module for model, training step, and optimizer
    │   ├── "CellariumAnnDataDataModule"   # DataModule for multi-GPU DataLoader for AnnData objects
    │   └── "CellariumPipeline"            # Pipeline for data transformations and model inference
    ├── "data"             # Distributed AnnData Collection and multi-GPU Iterable Datasets
    ├── "lr_schedulers"    # Custom learning rate schedulers
    ├── "models"           # Cellarium ML models
    ├── "preprocessing"    # Pre-processing functions
    ├── "transforms"       # Data transformation modules
    ├── "utilities"        # Utility functions for various submodules
    └── "cli.py"           # Implements the "cellarium-ml" CLI. Models must be registered here

Important Notes

cellarium/ml/models/*

  • Models must subclass CellariumModel and implement the following:

  • reset_parameters: Initializes model parameters.

  • forward: Returns a dictionary containing the computed loss under the loss key.

Optional hooks for training include:

  • on_train_start: Called at the start of training.

  • on_train_epoch_end: Triggered at the end of each epoch.

  • on_train_batch_end: Triggered at the end of each batch.

cellarium/ml/transforms/*

  • All transforms must subclass torch.nn.Module.

  • The forward method must output a dictionary where keys correspond to the input arguments for subsequent transforms or the model.

cellarium/ml/cli.py

  • Models must be registered here to be accessible via the command-line interface (cellarium-ml CLI).


Installation

To install via pip:

pip install cellarium-ml

To install the developer version from source:

git clone https://github.com/cellarium-ai/cellarium-ml.git
cd cellarium-ml
make install  # runs pip install -e .[dev]

API Documentation and Tutorials

For detailed API documentation and tutorials, visit: Cellarium ML Documentation


For Developers

To run the tests:

make test-examples                   # runs single-device cli example tests
make test-dataloader                 # runs single-device dataloader related tests
TEST_DEVICES=2 make test-dataloader  # runs multi-device dataloader related test
make test                            # runs single-device (all other) tests
TEST_DEVICES=2 make test             # runs multi-device (all other) tests

To format the code automatically:

make format                # runs ruff formatter and fixes linter errors

To run the linters:

make lint                  # runs ruff linter and checks for formatter errors

To build the documentation:

make docs                  # builds the documentation at docs/build/html

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