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tensorflow/WORKSPACE

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# buildifier: disable=load-on-top
workspace(name = "org_tensorflow")
# buildifier: disable=load-on-top
load("//third_party:repo.bzl", "tf_http_archive", "tf_mirror_urls")
tf_http_archive(
name = "rules_shell",
sha256 = "bc61ef94facc78e20a645726f64756e5e285a045037c7a61f65af2941f4c25e1",
strip_prefix = "rules_shell-0.4.1",
urls = tf_mirror_urls(
"https://github.com/bazelbuild/rules_shell/releases/download/v0.4.1/rules_shell-v0.4.1.tar.gz",
),
)
# Initialize toolchains for ML projects.
#
# A hermetic build system is designed to produce completely reproducible builds for C++.
# Details: https://github.com/google-ml-infra/rules_ml_toolchain
tf_http_archive(
name = "rules_ml_toolchain",
sha256 = "07802f21916a113be78ff2110891239bd5183ad09d8c42f6f9b04e4e0bfa5505",
strip_prefix = "rules_ml_toolchain-802e0dbbcc3cd82ac5b0accbff6f95b70106d0d1",
urls = tf_mirror_urls(
"https://github.com/google-ml-infra/rules_ml_toolchain/archive/802e0dbbcc3cd82ac5b0accbff6f95b70106d0d1.tar.gz",
),
)
load(
"@rules_ml_toolchain//cc/deps:cc_toolchain_deps.bzl",
"cc_toolchain_deps",
)
cc_toolchain_deps()
register_toolchains("@rules_ml_toolchain//cc:linux_x86_64_linux_x86_64")
register_toolchains("@rules_ml_toolchain//cc:linux_x86_64_linux_x86_64_cuda")
register_toolchains("@rules_ml_toolchain//cc:linux_aarch64_linux_aarch64")
register_toolchains("@rules_ml_toolchain//cc:linux_aarch64_linux_aarch64_cuda")
# Initialize the TensorFlow repository and all dependencies.
#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")
tf_workspace3()
load("@rules_shell//shell:repositories.bzl", "rules_shell_dependencies", "rules_shell_toolchains")
rules_shell_dependencies()
rules_shell_toolchains()
# Initialize hermetic Python
load("@local_xla//third_party/py:python_init_rules.bzl", "python_init_rules")
python_init_rules()
load("@local_xla//third_party/py:python_init_repositories.bzl", "python_init_repositories")
python_init_repositories(
default_python_version = "system",
local_wheel_dist_folder = "dist",
local_wheel_inclusion_list = [
"tensorflow*",
"tf_nightly*",
],
local_wheel_workspaces = ["//:WORKSPACE"],
requirements = {
"3.9": "//:requirements_lock_3_9.txt",
"3.10": "//:requirements_lock_3_10.txt",
"3.11": "//:requirements_lock_3_11.txt",
"3.12": "//:requirements_lock_3_12.txt",
"3.13": "//:requirements_lock_3_13.txt",
},
)
load("@local_xla//third_party/py:python_init_toolchains.bzl", "python_init_toolchains")
python_init_toolchains()
load("@local_xla//third_party/py:python_init_pip.bzl", "python_init_pip")
python_init_pip()
load("@pypi//:requirements.bzl", "install_deps")
install_deps()
# End hermetic Python initialization
load("@//tensorflow:workspace2.bzl", "tf_workspace2")
tf_workspace2()
load("@//tensorflow:workspace1.bzl", "tf_workspace1")
tf_workspace1()
load("@//tensorflow:workspace0.bzl", "tf_workspace0")
tf_workspace0()
Introduce hermetic CUDA in Google ML projects. 1) Hermetic CUDA rules allow building wheels with GPU support on a machine without GPUs, as well as running Bazel GPU tests on a machine with only GPUs and NVIDIA driver installed. When `--config=cuda` is provided in Bazel options, Bazel will download CUDA, CUDNN and NCCL redistributions in the cache, and use them during build and test phases. [Default location of CUNN redistributions](https://developer.download.nvidia.com/compute/cudnn/redist/) [Default location of CUDA redistributions](https://developer.download.nvidia.com/compute/cuda/redist/) [Default location of NCCL redistributions](https://pypi.org/project/nvidia-nccl-cu12/#history) 2) To include hermetic CUDA rules in your project, add the following in the WORKSPACE of the downstream project dependent on XLA. Note: use `@local_tsl` instead of `@tsl` in Tensorflow project. ``` load( "@tsl//third_party/gpus/cuda/hermetic:cuda_json_init_repository.bzl", "cuda_json_init_repository", ) cuda_json_init_repository() load( "@cuda_redist_json//:distributions.bzl", "CUDA_REDISTRIBUTIONS", "CUDNN_REDISTRIBUTIONS", ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl", "cuda_redist_init_repositories", "cudnn_redist_init_repository", ) cuda_redist_init_repositories( cuda_redistributions = CUDA_REDISTRIBUTIONS, ) cudnn_redist_init_repository( cudnn_redistributions = CUDNN_REDISTRIBUTIONS, ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_configure.bzl", "cuda_configure", ) cuda_configure(name = "local_config_cuda") load( "@tsl//third_party/nccl/hermetic:nccl_redist_init_repository.bzl", "nccl_redist_init_repository", ) nccl_redist_init_repository() load( "@tsl//third_party/nccl/hermetic:nccl_configure.bzl", "nccl_configure", ) nccl_configure(name = "local_config_nccl") ``` PiperOrigin-RevId: 662981325
2024-08-14 10:57:53 -07:00
Refactor mechanisms of building TF wheel and storing TF project version. This change introduces a uniform way of building the TF wheel and controlling the filename version suffixes. A new repository rule `python_wheel_version_suffix_repository` provides information about project and wheel version suffixes. The final value depends on environment variables passed to Bazel command: `_ML_WHEEL_WHEEL_TYPE, _ML_WHEEL_BUILD_DATE, _ML_WHEEL_GIT_HASH, _ML_WHEEL_VERSION_SUFFIX` `tf_version.bzl` defines the TF project version and loads the version suffix information calculated by `python_wheel_version_suffix_repository`. The targets `//tensorflow/core/public:release_version, //tensorflow:tensorflow_bzl //tensorflow/tools/pip_package:setup_py` use the version chunks defined above. The version of the wheel in the build rule output depends on the environment variables. Environment variables combinations for creating wheels with different versions: * snapshot (default build rule behavior): `--repo_env=ML_WHEEL_TYPE=snapshot` * release: `--repo_env=ML_WHEEL_TYPE=release` * release candidate: `--repo_env=ML_WHEEL_TYPE=release --repo_env=ML_WHEEL_VERSION_SUFFIX=-rc1` * nightly build with date as version suffix: `--repo_env=ML_WHEEL_TYPE=nightly --repo_env=ML_WHEEL_BUILD_DATE=<YYYYmmdd>` * build with git data as version suffix: `--repo_env=ML_WHEEL_TYPE=custom --repo_env=ML_WHEEL_BUILD_DATE=$(git show -s --format=%as HEAD) --repo_env=ML_WHEEL_GIT_HASH=$(git rev-parse HEAD)` PiperOrigin-RevId: 733444080
2025-03-04 13:25:05 -08:00
load(
"@local_xla//third_party/py:python_wheel.bzl",
"nvidia_wheel_versions_repository",
Refactor mechanisms of building TF wheel and storing TF project version. This change introduces a uniform way of building the TF wheel and controlling the filename version suffixes. A new repository rule `python_wheel_version_suffix_repository` provides information about project and wheel version suffixes. The final value depends on environment variables passed to Bazel command: `_ML_WHEEL_WHEEL_TYPE, _ML_WHEEL_BUILD_DATE, _ML_WHEEL_GIT_HASH, _ML_WHEEL_VERSION_SUFFIX` `tf_version.bzl` defines the TF project version and loads the version suffix information calculated by `python_wheel_version_suffix_repository`. The targets `//tensorflow/core/public:release_version, //tensorflow:tensorflow_bzl //tensorflow/tools/pip_package:setup_py` use the version chunks defined above. The version of the wheel in the build rule output depends on the environment variables. Environment variables combinations for creating wheels with different versions: * snapshot (default build rule behavior): `--repo_env=ML_WHEEL_TYPE=snapshot` * release: `--repo_env=ML_WHEEL_TYPE=release` * release candidate: `--repo_env=ML_WHEEL_TYPE=release --repo_env=ML_WHEEL_VERSION_SUFFIX=-rc1` * nightly build with date as version suffix: `--repo_env=ML_WHEEL_TYPE=nightly --repo_env=ML_WHEEL_BUILD_DATE=<YYYYmmdd>` * build with git data as version suffix: `--repo_env=ML_WHEEL_TYPE=custom --repo_env=ML_WHEEL_BUILD_DATE=$(git show -s --format=%as HEAD) --repo_env=ML_WHEEL_GIT_HASH=$(git rev-parse HEAD)` PiperOrigin-RevId: 733444080
2025-03-04 13:25:05 -08:00
"python_wheel_version_suffix_repository",
)
nvidia_wheel_versions_repository(
name = "nvidia_wheel_versions",
versions_source = "//ci/official/requirements_updater:nvidia-requirements.txt",
)
Refactor mechanisms of building TF wheel and storing TF project version. This change introduces a uniform way of building the TF wheel and controlling the filename version suffixes. A new repository rule `python_wheel_version_suffix_repository` provides information about project and wheel version suffixes. The final value depends on environment variables passed to Bazel command: `_ML_WHEEL_WHEEL_TYPE, _ML_WHEEL_BUILD_DATE, _ML_WHEEL_GIT_HASH, _ML_WHEEL_VERSION_SUFFIX` `tf_version.bzl` defines the TF project version and loads the version suffix information calculated by `python_wheel_version_suffix_repository`. The targets `//tensorflow/core/public:release_version, //tensorflow:tensorflow_bzl //tensorflow/tools/pip_package:setup_py` use the version chunks defined above. The version of the wheel in the build rule output depends on the environment variables. Environment variables combinations for creating wheels with different versions: * snapshot (default build rule behavior): `--repo_env=ML_WHEEL_TYPE=snapshot` * release: `--repo_env=ML_WHEEL_TYPE=release` * release candidate: `--repo_env=ML_WHEEL_TYPE=release --repo_env=ML_WHEEL_VERSION_SUFFIX=-rc1` * nightly build with date as version suffix: `--repo_env=ML_WHEEL_TYPE=nightly --repo_env=ML_WHEEL_BUILD_DATE=<YYYYmmdd>` * build with git data as version suffix: `--repo_env=ML_WHEEL_TYPE=custom --repo_env=ML_WHEEL_BUILD_DATE=$(git show -s --format=%as HEAD) --repo_env=ML_WHEEL_GIT_HASH=$(git rev-parse HEAD)` PiperOrigin-RevId: 733444080
2025-03-04 13:25:05 -08:00
python_wheel_version_suffix_repository(name = "tf_wheel_version_suffix")
Introduce hermetic CUDA in Google ML projects. 1) Hermetic CUDA rules allow building wheels with GPU support on a machine without GPUs, as well as running Bazel GPU tests on a machine with only GPUs and NVIDIA driver installed. When `--config=cuda` is provided in Bazel options, Bazel will download CUDA, CUDNN and NCCL redistributions in the cache, and use them during build and test phases. [Default location of CUNN redistributions](https://developer.download.nvidia.com/compute/cudnn/redist/) [Default location of CUDA redistributions](https://developer.download.nvidia.com/compute/cuda/redist/) [Default location of NCCL redistributions](https://pypi.org/project/nvidia-nccl-cu12/#history) 2) To include hermetic CUDA rules in your project, add the following in the WORKSPACE of the downstream project dependent on XLA. Note: use `@local_tsl` instead of `@tsl` in Tensorflow project. ``` load( "@tsl//third_party/gpus/cuda/hermetic:cuda_json_init_repository.bzl", "cuda_json_init_repository", ) cuda_json_init_repository() load( "@cuda_redist_json//:distributions.bzl", "CUDA_REDISTRIBUTIONS", "CUDNN_REDISTRIBUTIONS", ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl", "cuda_redist_init_repositories", "cudnn_redist_init_repository", ) cuda_redist_init_repositories( cuda_redistributions = CUDA_REDISTRIBUTIONS, ) cudnn_redist_init_repository( cudnn_redistributions = CUDNN_REDISTRIBUTIONS, ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_configure.bzl", "cuda_configure", ) cuda_configure(name = "local_config_cuda") load( "@tsl//third_party/nccl/hermetic:nccl_redist_init_repository.bzl", "nccl_redist_init_repository", ) nccl_redist_init_repository() load( "@tsl//third_party/nccl/hermetic:nccl_configure.bzl", "nccl_configure", ) nccl_configure(name = "local_config_nccl") ``` PiperOrigin-RevId: 662981325
2024-08-14 10:57:53 -07:00
load(
"@rules_ml_toolchain//third_party/gpus/cuda/hermetic:cuda_json_init_repository.bzl",
Introduce hermetic CUDA in Google ML projects. 1) Hermetic CUDA rules allow building wheels with GPU support on a machine without GPUs, as well as running Bazel GPU tests on a machine with only GPUs and NVIDIA driver installed. When `--config=cuda` is provided in Bazel options, Bazel will download CUDA, CUDNN and NCCL redistributions in the cache, and use them during build and test phases. [Default location of CUNN redistributions](https://developer.download.nvidia.com/compute/cudnn/redist/) [Default location of CUDA redistributions](https://developer.download.nvidia.com/compute/cuda/redist/) [Default location of NCCL redistributions](https://pypi.org/project/nvidia-nccl-cu12/#history) 2) To include hermetic CUDA rules in your project, add the following in the WORKSPACE of the downstream project dependent on XLA. Note: use `@local_tsl` instead of `@tsl` in Tensorflow project. ``` load( "@tsl//third_party/gpus/cuda/hermetic:cuda_json_init_repository.bzl", "cuda_json_init_repository", ) cuda_json_init_repository() load( "@cuda_redist_json//:distributions.bzl", "CUDA_REDISTRIBUTIONS", "CUDNN_REDISTRIBUTIONS", ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl", "cuda_redist_init_repositories", "cudnn_redist_init_repository", ) cuda_redist_init_repositories( cuda_redistributions = CUDA_REDISTRIBUTIONS, ) cudnn_redist_init_repository( cudnn_redistributions = CUDNN_REDISTRIBUTIONS, ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_configure.bzl", "cuda_configure", ) cuda_configure(name = "local_config_cuda") load( "@tsl//third_party/nccl/hermetic:nccl_redist_init_repository.bzl", "nccl_redist_init_repository", ) nccl_redist_init_repository() load( "@tsl//third_party/nccl/hermetic:nccl_configure.bzl", "nccl_configure", ) nccl_configure(name = "local_config_nccl") ``` PiperOrigin-RevId: 662981325
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"cuda_json_init_repository",
)
cuda_json_init_repository()
load(
"@cuda_redist_json//:distributions.bzl",
"CUDA_REDISTRIBUTIONS",
"CUDNN_REDISTRIBUTIONS",
)
load(
"@rules_ml_toolchain//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl",
Introduce hermetic CUDA in Google ML projects. 1) Hermetic CUDA rules allow building wheels with GPU support on a machine without GPUs, as well as running Bazel GPU tests on a machine with only GPUs and NVIDIA driver installed. When `--config=cuda` is provided in Bazel options, Bazel will download CUDA, CUDNN and NCCL redistributions in the cache, and use them during build and test phases. [Default location of CUNN redistributions](https://developer.download.nvidia.com/compute/cudnn/redist/) [Default location of CUDA redistributions](https://developer.download.nvidia.com/compute/cuda/redist/) [Default location of NCCL redistributions](https://pypi.org/project/nvidia-nccl-cu12/#history) 2) To include hermetic CUDA rules in your project, add the following in the WORKSPACE of the downstream project dependent on XLA. Note: use `@local_tsl` instead of `@tsl` in Tensorflow project. ``` load( "@tsl//third_party/gpus/cuda/hermetic:cuda_json_init_repository.bzl", "cuda_json_init_repository", ) cuda_json_init_repository() load( "@cuda_redist_json//:distributions.bzl", "CUDA_REDISTRIBUTIONS", "CUDNN_REDISTRIBUTIONS", ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl", "cuda_redist_init_repositories", "cudnn_redist_init_repository", ) cuda_redist_init_repositories( cuda_redistributions = CUDA_REDISTRIBUTIONS, ) cudnn_redist_init_repository( cudnn_redistributions = CUDNN_REDISTRIBUTIONS, ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_configure.bzl", "cuda_configure", ) cuda_configure(name = "local_config_cuda") load( "@tsl//third_party/nccl/hermetic:nccl_redist_init_repository.bzl", "nccl_redist_init_repository", ) nccl_redist_init_repository() load( "@tsl//third_party/nccl/hermetic:nccl_configure.bzl", "nccl_configure", ) nccl_configure(name = "local_config_nccl") ``` PiperOrigin-RevId: 662981325
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"cuda_redist_init_repositories",
"cudnn_redist_init_repository",
)
cuda_redist_init_repositories(
cuda_redistributions = CUDA_REDISTRIBUTIONS,
)
cudnn_redist_init_repository(
cudnn_redistributions = CUDNN_REDISTRIBUTIONS,
)
load(
"@rules_ml_toolchain//third_party/gpus/cuda/hermetic:cuda_configure.bzl",
Introduce hermetic CUDA in Google ML projects. 1) Hermetic CUDA rules allow building wheels with GPU support on a machine without GPUs, as well as running Bazel GPU tests on a machine with only GPUs and NVIDIA driver installed. When `--config=cuda` is provided in Bazel options, Bazel will download CUDA, CUDNN and NCCL redistributions in the cache, and use them during build and test phases. [Default location of CUNN redistributions](https://developer.download.nvidia.com/compute/cudnn/redist/) [Default location of CUDA redistributions](https://developer.download.nvidia.com/compute/cuda/redist/) [Default location of NCCL redistributions](https://pypi.org/project/nvidia-nccl-cu12/#history) 2) To include hermetic CUDA rules in your project, add the following in the WORKSPACE of the downstream project dependent on XLA. Note: use `@local_tsl` instead of `@tsl` in Tensorflow project. ``` load( "@tsl//third_party/gpus/cuda/hermetic:cuda_json_init_repository.bzl", "cuda_json_init_repository", ) cuda_json_init_repository() load( "@cuda_redist_json//:distributions.bzl", "CUDA_REDISTRIBUTIONS", "CUDNN_REDISTRIBUTIONS", ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl", "cuda_redist_init_repositories", "cudnn_redist_init_repository", ) cuda_redist_init_repositories( cuda_redistributions = CUDA_REDISTRIBUTIONS, ) cudnn_redist_init_repository( cudnn_redistributions = CUDNN_REDISTRIBUTIONS, ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_configure.bzl", "cuda_configure", ) cuda_configure(name = "local_config_cuda") load( "@tsl//third_party/nccl/hermetic:nccl_redist_init_repository.bzl", "nccl_redist_init_repository", ) nccl_redist_init_repository() load( "@tsl//third_party/nccl/hermetic:nccl_configure.bzl", "nccl_configure", ) nccl_configure(name = "local_config_nccl") ``` PiperOrigin-RevId: 662981325
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"cuda_configure",
)
cuda_configure(name = "local_config_cuda")
load(
"@rules_ml_toolchain//third_party/nccl/hermetic:nccl_redist_init_repository.bzl",
Introduce hermetic CUDA in Google ML projects. 1) Hermetic CUDA rules allow building wheels with GPU support on a machine without GPUs, as well as running Bazel GPU tests on a machine with only GPUs and NVIDIA driver installed. When `--config=cuda` is provided in Bazel options, Bazel will download CUDA, CUDNN and NCCL redistributions in the cache, and use them during build and test phases. [Default location of CUNN redistributions](https://developer.download.nvidia.com/compute/cudnn/redist/) [Default location of CUDA redistributions](https://developer.download.nvidia.com/compute/cuda/redist/) [Default location of NCCL redistributions](https://pypi.org/project/nvidia-nccl-cu12/#history) 2) To include hermetic CUDA rules in your project, add the following in the WORKSPACE of the downstream project dependent on XLA. Note: use `@local_tsl` instead of `@tsl` in Tensorflow project. ``` load( "@tsl//third_party/gpus/cuda/hermetic:cuda_json_init_repository.bzl", "cuda_json_init_repository", ) cuda_json_init_repository() load( "@cuda_redist_json//:distributions.bzl", "CUDA_REDISTRIBUTIONS", "CUDNN_REDISTRIBUTIONS", ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl", "cuda_redist_init_repositories", "cudnn_redist_init_repository", ) cuda_redist_init_repositories( cuda_redistributions = CUDA_REDISTRIBUTIONS, ) cudnn_redist_init_repository( cudnn_redistributions = CUDNN_REDISTRIBUTIONS, ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_configure.bzl", "cuda_configure", ) cuda_configure(name = "local_config_cuda") load( "@tsl//third_party/nccl/hermetic:nccl_redist_init_repository.bzl", "nccl_redist_init_repository", ) nccl_redist_init_repository() load( "@tsl//third_party/nccl/hermetic:nccl_configure.bzl", "nccl_configure", ) nccl_configure(name = "local_config_nccl") ``` PiperOrigin-RevId: 662981325
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"nccl_redist_init_repository",
)
nccl_redist_init_repository()
load(
"@rules_ml_toolchain//third_party/nccl/hermetic:nccl_configure.bzl",
Introduce hermetic CUDA in Google ML projects. 1) Hermetic CUDA rules allow building wheels with GPU support on a machine without GPUs, as well as running Bazel GPU tests on a machine with only GPUs and NVIDIA driver installed. When `--config=cuda` is provided in Bazel options, Bazel will download CUDA, CUDNN and NCCL redistributions in the cache, and use them during build and test phases. [Default location of CUNN redistributions](https://developer.download.nvidia.com/compute/cudnn/redist/) [Default location of CUDA redistributions](https://developer.download.nvidia.com/compute/cuda/redist/) [Default location of NCCL redistributions](https://pypi.org/project/nvidia-nccl-cu12/#history) 2) To include hermetic CUDA rules in your project, add the following in the WORKSPACE of the downstream project dependent on XLA. Note: use `@local_tsl` instead of `@tsl` in Tensorflow project. ``` load( "@tsl//third_party/gpus/cuda/hermetic:cuda_json_init_repository.bzl", "cuda_json_init_repository", ) cuda_json_init_repository() load( "@cuda_redist_json//:distributions.bzl", "CUDA_REDISTRIBUTIONS", "CUDNN_REDISTRIBUTIONS", ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_redist_init_repositories.bzl", "cuda_redist_init_repositories", "cudnn_redist_init_repository", ) cuda_redist_init_repositories( cuda_redistributions = CUDA_REDISTRIBUTIONS, ) cudnn_redist_init_repository( cudnn_redistributions = CUDNN_REDISTRIBUTIONS, ) load( "@tsl//third_party/gpus/cuda/hermetic:cuda_configure.bzl", "cuda_configure", ) cuda_configure(name = "local_config_cuda") load( "@tsl//third_party/nccl/hermetic:nccl_redist_init_repository.bzl", "nccl_redist_init_repository", ) nccl_redist_init_repository() load( "@tsl//third_party/nccl/hermetic:nccl_configure.bzl", "nccl_configure", ) nccl_configure(name = "local_config_nccl") ``` PiperOrigin-RevId: 662981325
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"nccl_configure",
)
nccl_configure(name = "local_config_nccl")
load(
"@rules_ml_toolchain//third_party/nvshmem/hermetic:nvshmem_json_init_repository.bzl",
"nvshmem_json_init_repository",
)
nvshmem_json_init_repository()
load(
"@nvshmem_redist_json//:distributions.bzl",
"NVSHMEM_REDISTRIBUTIONS",
)
load(
"@rules_ml_toolchain//third_party/nvshmem/hermetic:nvshmem_redist_init_repository.bzl",
"nvshmem_redist_init_repository",
)
nvshmem_redist_init_repository(
nvshmem_redistributions = NVSHMEM_REDISTRIBUTIONS,
)