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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# This bazelrc can build a CPU-supporting TF package. # Convenient cache configurations # Use a cache directory mounted to /tf/cache. Very useful! build:sigbuild_local_cache --disk_cache=/tf/cache # Use the public-access TF DevInfra cache (read only) build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false
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ci/official/envs/disk_cache
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ci/official/envs/rbe
exit 1 fi TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" # These flags share the user's gcloud credentials with the container, so that bazel # inside the container can authenticate. Note: TF's CI does not have any credential # stored here.
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ci/official/wheel_test/README.md
`_requirements_lock` files will be generated by the presubmit job. A detailed description will be provided once it's integrated into presubmit. ### test_import_api_packages This Python test verifies whether the API v2 packages can be imported from the current build. It utilizes the `_api/v2/api_packages.txt` list of packages from the local wheel file specified in the `requirements_lock_<python_version>.txt`.
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ci/official/utilities/code_check_changed_files.bats
xargs -a $BATS_TEST_TMPDIR/files -i -n1 -P $(nproc --all) \ bash -c 'clang-format-12 --style=Google {} | git diff --no-index {} -' \ | tee $BATS_TEST_TMPDIR/needs_help.txt echo "You can use clang-format --style=Google -i <file> to apply changes to a file." [[ ! -s $BATS_TEST_TMPDIR/needs_help.txt ]] } # Note: this is excluded on the full code base, since any submitted code must
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CONTRIBUTING.md
once you get into the running container so `bazel` can find the `tensorflow` workspace). you can do this by using the following command. As an example- ```bash docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10 ``` Once you have the packages installed, you can run a specific unit test in bazel by doing as follows: ```bash
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ci/official/README.md
1. Creating a PR and observing the presubmit test results 2. Running the CI scripts locally, as explained below 3. **Google employees only**: Google employees can use an internal-only tool called "MLCI" that makes testing more convenient: it can execute any full CI job against a pending change. Search for "MLCI" internally to find it. You may invoke a CI script of your choice by following these instructions: ```bash
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SECURITY.md
Therefore, if you run a `tf.train.Server` in your network, anybody with access to the network can execute arbitrary code with the privileges of the user running the `tf.train.Server`. ## Untrusted inputs during training and prediction TensorFlow supports a wide range of input data formats. For example it can process images, audio, videos, and text. There are several modules specialized
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WORKSPACE
# 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()
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ci/official/requirements_updater/README.md
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: ``` export REQUIREMENTS_FILE_NAME=my_requirements.in ``` To specify the file via a Bazel command argument, use the following: ```
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