wehub-resource-sync
108 lines
6.0 KiB
Python
108 lines
6.0 KiB
Python
"""Configurations of RBE builds used with remote config."""
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load("//tensorflow/tools/toolchains/remote_config:rbe_config.bzl", "ml_build_rbe_config", "sigbuild_tf_configs", "tensorflow_local_config", "tensorflow_rbe_config", "tensorflow_rbe_win_config")
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def initialize_rbe_configs():
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tensorflow_local_config(
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name = "local_execution",
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)
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tensorflow_rbe_config(
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name = "ubuntu20.04-clang_manylinux2014-cuda12.3-cudnn8.9",
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cuda_version = "12.3.2",
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cudnn_version = "8.9.7.29",
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os = "ubuntu20.04-manylinux2014-multipython",
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)
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tensorflow_rbe_config(
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name = "ubuntu20.04-clang_manylinux2014-cuda12.3-cudnn9.1",
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cuda_version = "12.3.2",
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cudnn_version = "9.1.1",
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os = "ubuntu20.04-manylinux2014-multipython",
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)
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tensorflow_rbe_config(
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name = "ubuntu20.04-gcc9_manylinux2014-cuda12.3-cudnn8.9",
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cuda_version = "12.3.2",
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cudnn_version = "8.9.7.29",
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os = "ubuntu20.04-manylinux2014-multipython",
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)
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tensorflow_rbe_config(
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name = "ubuntu22.04-clang_manylinux2014-cuda12.3-cudnn8.9",
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cuda_version = "12.3.2",
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cudnn_version = "8.9.7.29",
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os = "ubuntu22.04-manylinux2014-multipython",
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)
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tensorflow_rbe_config(
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name = "ubuntu22.04-gcc9_manylinux2014-cuda12.3-cudnn8.9",
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cuda_version = "12.3.2",
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cudnn_version = "8.9.7.29",
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os = "ubuntu22.04-manylinux2014-multipython",
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)
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tensorflow_rbe_win_config(
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name = "windows_py37",
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python_bin_path = "C:/Python37/python.exe",
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)
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# The `ml-build`'s base image is a standard `ubuntu22.04` image.
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# Note that in order to use this image with RBE GPU builds, you need to have hermetic CUDA
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# toolchain integrated into your project, and pass
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# `--@cuda_driver//:include_cuda_umd_libs=true` to Bazel command.
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ml_build_rbe_config("docker://us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build@sha256:ea67e8453d8b09c2ba48853da5e79efef4b65804b4a48dfae4b4da89ffd38405")
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# TF-Version-Specific SIG Build RBE Configs. The crosstool generated from these
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# configs are python-version-independent because they only care about the
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# tooling paths; the container mapping is useful only so that TF RBE users
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# may specify a specific Python version container. Yes, we could use the tag name instead,
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# but for vague security reasons we're obligated to use the pinned hash and update manually.
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# The name_container_map is helpfully auto-generated by a GitHub Action. You have to run it
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# manually. See go/tf-devinfra/docker#how-do-i-update-rbe-images
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sigbuild_tf_configs(
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name_container_map = {
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"sigbuild-r2.16": "docker://gcr.io/tensorflow-sigs/build@sha256:842a5ba84d3658c5bf1f8a31e16284f7becc35409da0dfd71816afa3cd28d728",
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"sigbuild-r2.16-python3.10": "docker://gcr.io/tensorflow-sigs/build@sha256:da15288c8464153eadd35da720540a544b76aa9d78cceb42a6821b2f3e70a0fa",
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"sigbuild-r2.16-python3.11": "docker://gcr.io/tensorflow-sigs/build@sha256:842a5ba84d3658c5bf1f8a31e16284f7becc35409da0dfd71816afa3cd28d728",
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"sigbuild-r2.16-python3.12": "docker://gcr.io/tensorflow-sigs/build@sha256:40fcd1d05c672672b599d9cb3784dcf379d6aa876f043b46c6ab18237d5d4e10",
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},
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)
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sigbuild_tf_configs(
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name_container_map = {
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"sigbuild-r2.16-clang": "docker://gcr.io/tensorflow-sigs/build@sha256:842a5ba84d3658c5bf1f8a31e16284f7becc35409da0dfd71816afa3cd28d728",
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"sigbuild-r2.16-clang-python3.10": "docker://gcr.io/tensorflow-sigs/build@sha256:da15288c8464153eadd35da720540a544b76aa9d78cceb42a6821b2f3e70a0fa",
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"sigbuild-r2.16-clang-python3.11": "docker://gcr.io/tensorflow-sigs/build@sha256:842a5ba84d3658c5bf1f8a31e16284f7becc35409da0dfd71816afa3cd28d728",
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"sigbuild-r2.16-clang-python3.12": "docker://gcr.io/tensorflow-sigs/build@sha256:40fcd1d05c672672b599d9cb3784dcf379d6aa876f043b46c6ab18237d5d4e10",
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},
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)
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sigbuild_tf_configs(
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name_container_map = {
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"sigbuild-r2.17": "docker://gcr.io/tensorflow-sigs/build@sha256:b6f572a897a69fa3311773f949b9aa9e81bc393e4fbe2c0d56d8afb03a6de080",
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"sigbuild-r2.17-python3.10": "docker://gcr.io/tensorflow-sigs/build@sha256:64e68a1d65ac265a2a59c8c2f6eb1f2148a323048a679a08e53239d467fa1478",
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"sigbuild-r2.17-python3.11": "docker://gcr.io/tensorflow-sigs/build@sha256:b6f572a897a69fa3311773f949b9aa9e81bc393e4fbe2c0d56d8afb03a6de080",
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"sigbuild-r2.17-python3.12": "docker://gcr.io/tensorflow-sigs/build@sha256:8b856ad736147bb9c8bc9e1ec2c8e1ab17d36397905da7a5b63dadeff9310f0c",
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},
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)
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sigbuild_tf_configs(
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name_container_map = {
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"sigbuild-r2.17-clang": "docker://gcr.io/tensorflow-sigs/build@sha256:2d737fc9fe931507a89927eee792b1bb934215e6aaae58b1941586e3400e2645",
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"sigbuild-r2.17-clang-python3.10": "docker://gcr.io/tensorflow-sigs/build@sha256:89730ded5c2268e53238bf8c5fb6d162b105baa31ab0480a94a7d0204203de66",
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"sigbuild-r2.17-clang-python3.11": "docker://gcr.io/tensorflow-sigs/build@sha256:2d737fc9fe931507a89927eee792b1bb934215e6aaae58b1941586e3400e2645",
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"sigbuild-r2.17-clang-python3.12": "docker://gcr.io/tensorflow-sigs/build@sha256:45ea78e79305f91cdae5a26094f80233bba54bbfbc612623381012f097035b9a",
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},
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)
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sigbuild_tf_configs(
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name_container_map = {
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"sigbuild-r2.17-clang-cudnn9": "docker://gcr.io/tensorflow-sigs/build@sha256:daa5bdd802fe3def188e2200ed707c73d278f6f1930bf26c933d6ba041b0e027",
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"sigbuild-r2.17-clang-cudnn9-python3.10": "docker://gcr.io/tensorflow-sigs/build@sha256:c3df6982305d70dfb44cbfbedee3465782d6cbf791f7920e6246de0140216da0",
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"sigbuild-r2.17-clang-cudnn9-python3.11": "docker://gcr.io/tensorflow-sigs/build@sha256:daa5bdd802fe3def188e2200ed707c73d278f6f1930bf26c933d6ba041b0e027",
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"sigbuild-r2.17-clang-cudnn9-python3.12": "docker://gcr.io/tensorflow-sigs/build@sha256:23e477895dd02e45df1056d4a0a9c4229dec3a20c23fb2f3fb5832ecbd0a29bc",
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},
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)
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