BioNeMo Framework (v2.0)

NVIDIA BioNeMo Framework is a collection of programming tools, libraries, and models for computational drug discovery. It accelerates the most time-consuming and costly stages of building and adapting biomolecular AI models by providing domain-specific, optimized models and tooling that are easily integrated into GPU-based computational resources for the fastest performance on the market. You can access BioNeMo Framework as a free community resource here in this repository or learn more at https://www.nvidia.com/en-us/clara/bionemo/ about getting an enterprise license for improved expert-level support.

The bionemo-framework is partitioned into independently installable namespace packages. These are located under the sub-packages/ directory. Please refer to PEP 420 – Implicit Namespace Packages for details.

Documentation

Comprehensive documentation, including user guides, API references, and troubleshooting information, can be found in our official documentation at https://docs.nvidia.com/bionemo-framework/latest/

For those interested in exploring the latest developments and features not yet included in the released container, we also maintain an up-to-date documentation set that reflects the current state of the main branch. This in-progress documentation can be accessed at https://nvidia.github.io/bionemo-framework/

Please note that while this documentation is generally accurate and helpful, it may contain references to features or APIs not yet stabilized or released. As always, we appreciate feedback on our documentation and strive to continually improve its quality.

Using the BioNeMo Framework

Full documentation on using the BioNeMo Framework is provided in our documentation: https://docs.nvidia.com/bionemo-framework/latest/user-guide/. To facilitate the process of linking against optimized versions of third-party dependencies, BioNeMo is primarily distributed as a containerized library. The latest released container for the BioNeMo Framework is available for download through NGC. Launching a pre-built container can be accomplished through the brev.dev link at the top of the page, or by running

docker run --rm -it \
  --gpus=all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
  nvcr.io/nvidia/clara/bionemo-framework:main--nightly \
  /bin/bash

Setting up a local development environment

Initializing 3rd-party dependencies as git submodules

The NeMo and Megatron-LM dependencies are vendored in the bionemo-2 repository workspace as git submodules for development purposes. The pinned commits for these submodules represent the "last-known-good" versions of these packages that are confirmed to be working with bionemo2 (and those that are tested in CI).

To initialize these sub-modules when cloning the repo, add the --recursive flag to the git clone command:

git clone --recursive git@github.com:NVIDIA/bionemo-framework.git

To download the pinned versions of these submodules within an existing git repository, run

git submodule update --init --recursive

Different branches of the repo can have different pinned versions of these third-party submodules. Make sure you update submodules after switching branches or pulling recent changes!

To configure git to automatically update submodules when switching branches, run

git config submodule.recurse true

NOTE: this setting will not download new or remove old submodules with the branch's changes. You will have to run the full git submodule update --init --recursive command in these situations.

Building the bionemo-framework docker image

With a locally cloned bionemo-framework repository, an appropriately configured nvidia-container-toolkit build toolchain, and initialized submodules, the bionemo container can be built with

docker buildx build . -t my-container-tag

Intellisense and interactive debugging with the VSCode Devcontainer

We distribute a development container configuration for vscode (.vscode/devcontainer.json) that simplifies the process of local testing and development. Opening the bionemo-framework folder with VSCode should prompt you to re-open the folder inside the devcontainer environment.

Note

The first time you launch the devcontainer, it may take a long time to build the image. Building the image locally (using the command shown above) will ensure that most of the layers are present in the local docker cache.

Quick Start

See the tutorials pages for example applications and getting started guides.