
bionemo-framework
BioNeMo Framework: For building and adapting AI models in drug discovery at scale
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NVIDIA BioNeMo Framework is a collection of programming tools, libraries, and models for computational drug discovery. It accelerates building and adapting biomolecular AI models by providing domain-specific, optimized models and tooling for GPU-based computational resources. The framework offers comprehensive documentation and support for both community and enterprise users.
README:
NVIDIA BioNeMo Framework is a comprehensive suite of programming tools, libraries, and models designed 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.
BioNeMo Framework is part of a larger ecosystem of NVIDIA Biopharma products. Get notified of new releases, bug fixes, critical security updates, and more for biopharma. Subscribe.
[!NOTE] BioNeMo Recipes are now available, which demonstrate high-performance model training outside of the NeMo Framework. The recipes show how to train models that derive from HuggingFace
PreTrainedModel
classes, and use NVIDIA TransformerEngine layers for optimized attention kernels. For more information, see the BioNeMo Recipes README.
The bionemo-framework
is organized into independently installable namespace packages. These are located under the
sub-packages/
directory. Please refer to PEP 420 – Implicit Namespace Packages
for details.
- Official Documentation: For user guides, API references, and troubleshooting, visit our official documentation.
-
In-Progress Documentation: To explore the latest features and developments, check the documentation reflecting the current state of the
main
branch here. Note that this may include references to features or APIs that are not yet finalized.
Full documentation on using the BioNeMo Framework is provided in our documentation:
https://docs.nvidia.com/bionemo-framework/latest/user-guide/. To simplify the integration of optimized third-party dependencies, BioNeMo is primarily distributed as a containerized library. You can download the latest released container for the BioNeMo Framework from
NGC. To launch a pre-built container, you can use the brev.dev launchable or execute the following command:
docker run --rm -it \
--gpus=all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
nvcr.io/nvidia/clara/bionemo-framework:nightly \
/bin/bash
The NeMo and Megatron-LM dependencies are included as git submodules in bionemo2. 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 [email protected]:NVIDIA/bionemo-framework.git
cd bionemo-framework
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. Ensure submodules are automatically updated after switching branches or pulling updates by configuring git with:
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.
With a locally cloned repository and initialized submodules, build the BioNeMo container using:
docker buildx build . -t my-container-tag
We distribute a development container configuration for vscode
(.devcontainer/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.
See the tutorials pages for example applications and getting started guides.
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