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Contributing To NeMo-Automodel

Environment setup for Automodel

Common workflows used for setting up Automodel environment:

  1. Developing with Automodel container
  2. Developing with UV sync/pip install
  3. Developing with custom docker build

1. Developing with Automodel container

The latest Automodel container can be found: here

The container can be run with the following docker command:

docker run --gpus all --network=host -it --rm --shm-size=32g nvcr.io/nvidia/nemo-automodel:25.11.00 /bin/bash

Mounting local Automodel directory into the container

To sync local Automodel directory into the container, mount the local directory into /opt/Automodel and override the installed Automodel repository. Example docker command:

docker run --gpus all --network=host -it --rm -v <local-Automodel-path>:/opt/Automodel --shm-size=32g nvcr.io/nvidia/nemo-automodel:25.11.00 /bin/bash

Within the container, cd into /opt/Automodel/ and update the pyproject.toml and uv.lock file by running the following command:

bash docker/common/update_pyproject_pytorch.sh /opt/Automodel

Finally, run uv sync to sync the container with the updated repository:

uv sync --locked --extra all --all-groups

Warning

Ensure bash docker/common/update_pyproject_pytorch.sh /opt/Automodel is executed. Without this command, uv sync will attempt to reinstall torch. This leads to errors relating to CUDA version mismatch, TE import failures, etc. This work around is required as uv cannot recognize the torch installed in the PyTorch base container.

2. Developing with uv sync/pip install

Uv sync and pip install are both supported in Automodel. Uv sync is the recommened path.

From the local Automodel directory run the following command:

uv sync --locked --extra all

The following optional dependencies are available, please see [project.optional-dependencies] section in pyproject.toml:

  • cuda (all dependencies that require cuda)
  • extra (additional dependencies for model coverage)
  • fa (flash attention)
  • delta-databricks
  • moe
  • vlm
  • all (installs cuda, delta-databricks, extra and vlm)

Example, installing vlm dependencies:

uv sync --locked --extra vlm

3. Developing with custom docker build

For developers building a custom docker container with Automodel, please refer to Automodel's Dockerfile.

If Nvidia PyTorch container is used as a base image, please review Developing with Automodel container.

Command to build Automodel's Dockerfile:

# Set build arguments
export AUTOMODEL_INSTALL=all #(cuda, moe, vlm, ...)
export BASE_IMAGE=pytorch #(cuda, pytorch)

# Dependency install options [True, False]
export INSTALL_TE=True

docker build -f docker/Dockerfile \
    --build-arg AUTOMODEL_INSTALL=$AUTOMODEL_INSTALL \
    --build-arg BASE_IMAGE=$BASE_IMAGE \
    --build-arg INSTALL_MAMBA=$INSTALL_MAMBA \
    -t automodel --target=automodel_final .

MoE Dependency

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt-get install cudnn-cuda-12
pip install transformer-engine[pytorch]==2.8.0
# Flash-attn version should be selected to satisfy TE requirements
# https://github.com/NVIDIA/TransformerEngine/blob/v2.4/transformer_engine/pytorch/attention/dot_product_attention/utils.py#L108
pip install flash-attn==2.7.4.post1
pip install grouped_gemm

Development Dependencies

We use uv for managing dependencies.

New required dependencies can be added by uv add $DEPENDENCY.

Adding a new dependency will update UV's lock-file. Please check this into your branch:

git add uv.lock pyproject.toml
git commit -s -m "build: Adding dependencies"
git push

Linting and Formatting

We use ruff for linting and formatting.

Installation:

pip install ruff

Format:

ruff check --fix .
ruff format .

Adding Documentation

If your contribution involves documentation changes, please refer to the Documentation Development guide for detailed instructions on building and serving the documentation.

Signing Your Work

  • We require that all contributors "sign-off" on their commits. This certifies that the contribution is your original work, or you have rights to submit it under the same license, or a compatible license.

    • Any contribution which contains commits that are not Signed-Off will not be accepted.

  • To sign off on a commit you simply use the --signoff (or -s) option when committing your changes:

    git commit -s -m "Add cool feature."

    This will append the following to your commit message:

    Signed-off-by: Your Name <your@email.com>

  • Full text of the DCO:

    Developer Certificate of Origin
Version 1.1

Copyright (C) 2004, 2006 The Linux Foundation and its contributors.

Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.


Developer's Certificate of Origin 1.1

By making a contribution to this project, I certify that:

(a) The contribution was created in whole or in part by me and I
    have the right to submit it under the open source license
    indicated in the file; or

(b) The contribution is based upon previous work that, to the best
    of my knowledge, is covered under an appropriate open source
    license and I have the right under that license to submit that
    work with modifications, whether created in whole or in part
    by me, under the same open source license (unless I am
    permitted to submit under a different license), as indicated
    in the file; or

(c) The contribution was provided directly to me by some other
    person who certified (a), (b) or (c) and I have not modified
    it.

(d) I understand and agree that this project and the contribution
    are public and that a record of the contribution (including all
    personal information I submit with it, including my sign-off) is
    maintained indefinitely and may be redistributed consistent with
    this project or the open source license(s) involved.