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In this change we rename 3rdparty/README.md (which contains the process playboook for C++ dependencies) to 3rdparty/cpp-thirdparty.md and add a new 3rdparty/py-thirdparty.md file which contains the process playbook for python dependencies. We also update the main 3rdparty/README.md file to serve as a starting-point referring to both of these files. Signed-off-by: Josh Bialkowski <1309820+cheshirekow@users.noreply.github.com> Co-authored-by: Josh Bialkowski <1309820+cheshirekow@users.noreply.github.com>
70 lines
3.7 KiB
Markdown
70 lines
3.7 KiB
Markdown
# Adding new python dependencies via pip
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If you add a new python dependency and that dependency will be installed in
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(and, thus, distributed with) the container, please follow this process.
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## Third-party packages without modification
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If the package you wish to add does not require modification, then please follow
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these steps:
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1. Add your new dependency to one of the "pip install" invocations among the
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scripts in docker/common.sh. If none of the existing ones make sense, then
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add a new script to install your package and add a new line to
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Dockerfile.multi to run your script.
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2. Update ATTRIBUTIONS-Python.md to include all new dependencies. Note that this
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must cover the transitive closure of all dependencies. The dependency you
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added may have pulled in new transitive dependencies and we must ensure all
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are attributed in this file.
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3. Verify that your newly added package is listed in the compliance reports and
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that sources are pulled via the compliance tooling.
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## Third-party packages with modification
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If you wish to depend on a package with nvidia-contributed modifications that
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haven't been upstreamed then please follow these steps:
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1. File an OSRB request to fork/contribute to a 3rd party open source package.
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https://confluence.nvidia.com/display/OSS/Contribution+to+Open+Source
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2. Clone the original repository to a new public nvidia-controlled location
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(e.g. https://gitlab.com/nvidia/tensorrt-llm/oss-components/)
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3. Register this new repository under nspec
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4. Make modifications in that public repository. Ensure that the clone
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repository clearly indicates the software license via /LICENSE.txt in the
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root of the repository. Ensure that this file contains a copyright statement
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indicating copyright held by the original author(s) and Nvidia.
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5. Publish the modified package to pypi under a new name (e.g. nvidia-<package>)
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6. Add your new dependency to one of the "pip install" invocations among the
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scripts in docker/common.sh. If none of the existing ones make sense, then
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add a new script to install your package and add a new line to
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Dockerfile.multi to run your script.
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7. Update ATTRIBUTIONS-Python.md to include all new dependencies. Note that this
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must cover the transitive closure of all dependencies. The dependency you
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added may have pulled in new transitive dependencies and we must ensure all
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are attributed in this file.
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8. Verify that your newly added package is listed in the compliance reports and
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that sources are pulled via the compliance tooling.
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Notes:
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* For pip/uv-installed versions of TensorRT-LLM, the modified package will be
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installed as a transitive dependency by the package manager
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* For the container distribution of TensorRT-LLM, the modified package will be
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pre-installed from the same pypi location via pip
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## Individual third-party sources with modification
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If you wish to integrate third-party source files with nvidia-contributed
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modifications that haven't been upstreamed then please follow these steps:
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1. File an OSRB request to use open source:
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https://confluence.nvidia.com/display/OSS/So+you+want+to+use+open+source+in+your+product
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2. Clone the original repository to a new nvidia-controlled location
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(e.g. https://gitlab.com/nvidia/tensorrt-llm/oss-components/)
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3. Make modifications in that repository on branch so that the versions
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"as-used" can be easily found and the diff against upstream easily viewed.
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4. Copy the desired source files into the TensorRT-LLM repository.
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5. Update ATTRIBUTIONS-Python.md to include attribution for the source files
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you have added. Note the terms of the license on the original repository
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and see the examples already in the file to understand what all needs to be
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stated.
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