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ci/official/envs/rbe
# an entry in tensorflow/.bazelrc for build:rbe_TFCI_BAZEL_TARGET... # ^^^^ # This env is only valid when RBE is available on the selected platform, since # it enables a derived --config setting. If RBE is not available (i.e. there # is no --config setting), bazel would fail and quit. This script does a quick # check This script checks for such errors early
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CONTRIBUTING.md
sufficient description, if applicable unit tests are added, if it is a reasonable contribution (meaning it is not a single liner cosmetic PR). **2. Valid?** - If the PR passes all the quality checks then we go ahead and assign a reviewer. - If the PR didn't meet the validation criteria, we request for additional changes to be made to PR to pass quality checks and send it back or on a rare occasion we may reject it.
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ci/official/README.md
# # Docker is enabled by default on Linux. You may disable it if you prefer: # export TFCI=py311,linux_x86,no_docker # Advanced: Use Remote Build Execution (RBE) (internal developers only) # # RBE dramatically speeds up builds and testing. It also gives you a # public URL to share your build results with collaborators. However, # it is only available to a limited set of internal TensorFlow developers. #
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ci/official/requirements_updater/README.md
updating requirements for multiple minor versions of Python. It takes in a file with a set of dependencies, and produces a more detailed requirements file for each version, with hashes specified for each dependency required, as well as their sub-dependencies. ### How to update/add requirements By default, the name of the base requirements file is `requirements.in`, but it can be set using the `REQUIREMENTS_FILE_NAME` variable. \ For example:
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ci/official/containers/linux_arm64/cuda.packages.txt
libcudnn8=8.9.6.50-1+cuda12.2 libcudnn8-dev=8.9.6.50-1+cuda12.2 # This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image. # For now it ensures that we install at least version 12.3.107 of PTXAS, # since 12.3.103 has a bug.
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RELEASE.md
* Add `wait` to `tf.data.Dataset.load`. If `True`, for snapshots written with `distributed_save`, it reads the snapshot while it is being written. For snapshots written with regular `save`, it waits for the snapshot until it's finished. The default is `False` for backward compatibility. Users of `distributed_save` are recommended to set it to `True`. * `tf.tpu.experimental.embedding.TPUEmbeddingV2`
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ci/official/envs/versions_upload
# Release jobs are very basic. They don't use any caching or RBE, # but they do upload logs to resultstore. # IMPORTANT: trailing slash is required on GCS URIs, as it tells gcloud to # pretend the path is a directory. TFCI_ARTIFACT_FINAL_GCS_ENABLE=1 TFCI_ARTIFACT_FINAL_GCS_SA_PATH="${KOKORO_KEYSTORE_DIR}/73361_tensorflow_release_binary_uploader_service_account"
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tensorflow/BUILD
# with clang. It does not imply that CUDA support has been enabled. alias( name = "is_cuda_compiler_clang", actual = if_oss( "@local_config_cuda//:is_cuda_compiler_clang", "@local_config_cuda//cuda:TRUE", ), ) # Config setting that is satisfied when CUDA device code should be compiled # with nvcc. It does not imply that CUDA support has been enabled. alias(
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requirements_lock_3_11.txt
markdown==3.5.2 \ --hash=sha256:d43323865d89fc0cb9b20c75fc8ad313af307cc087e84b657d9eec768eddeadd \ --hash=sha256:e1ac7b3dc550ee80e602e71c1d168002f062e49f1b11e26a36264dafd4df2ef8 # via tb-nightly markdown-it-py==3.0.0 \ --hash=sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1 \ --hash=sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb # via rich markupsafe==2.1.5 \
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requirements_lock_3_10.txt
markdown==3.5.2 \ --hash=sha256:d43323865d89fc0cb9b20c75fc8ad313af307cc087e84b657d9eec768eddeadd \ --hash=sha256:e1ac7b3dc550ee80e602e71c1d168002f062e49f1b11e26a36264dafd4df2ef8 # via tb-nightly markdown-it-py==3.0.0 \ --hash=sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1 \ --hash=sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb # via rich markupsafe==2.1.5 \
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