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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which # drops all the bazel dependencies for each py_test; this makes all the tests # use the wheel's TensorFlow installation instead of the one made available # through bazel. This must be done in a different root directory, //bazel_pip/..., # because "import tensorflow" run from the root directory would instead import # the folder instead of the venv package. #
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tensorflow/c/experimental/filesystem/modular_filesystem_test.cc
// ensures that two consecutive runs are unlikely to clash. class ModularFileSystemTest : public ::testing::TestWithParam<std::string> { public: // Initializes `root_dir_` to a unique value made of `::testing::TempDir()`, a // static random value unique for all the tests in one invocation, and the // current test name. // // Since the test name contains `/` (due to parameters), this function
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ci/official/containers/linux_arm64/Dockerfile
COPY cuda.packages.txt /cuda.packages.txt RUN /setup.sources.sh && /setup.packages.sh /devel.packages.txt # Install various tools. # - bats: bash unit testing framework # NOTE: v1.6.0 seems to have a bug that made "git" in setup_file break # - bazelisk: always use the correct bazel version # - buildifier: clean bazel build deps # - buildozer: clean bazel build deps
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which # drops all the bazel dependencies for each py_test; this makes all the tests # use the wheel's TensorFlow installation instead of the one made available # through bazel. This must be done in a different root directory, //bazel_pip/..., # because "import tensorflow" run from the root directory would instead import # the folder instead of the venv package. #
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tensorflow/c/c_api.h
// // Questions left to address: // * Might at some point need a way for callers to provide their own Env. // * Maybe add TF_TensorShape that encapsulates dimension info. // // Design decisions made: // * Backing store for tensor memory has an associated deallocation // function. This deallocation function will point to client code // for tensors populated by the client. So the client can do things
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ci/devinfra/docker_windows/Dockerfile
# Add signtool.exe to the PATH. Note this path may need to be edited if updates # are made to the Windows 10 SDK. RUN $old_path = [Environment]::GetEnvironmentVariable(\"PATH\", \"Machine\"); \
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configure.py
Returns: boolean value of the variable. Raises: UserInputError: if an environment variable is set, but it cannot be interpreted as a boolean indicator, assume that the user has made a scripting error, and will continue to provide invalid input. Raise the error to avoid infinitely looping. """ if not question: question = 'Do you wish to build TensorFlow with {} support?'.format(
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ci/official/README.md
# First, set your TFCI variable to choose the environment settings. # TFCI is a comma-separated list of filenames from the envs directory, which # are all settings for the scripts. TF's CI jobs are all made of a combination # of these env files. # # If you've clicked on a test result from our CI (via a dashboard or GitHub link), # click to "Invocation Details" and find BUILD_CONFIG, which will contain a TFCI
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CONTRIBUTING.md
**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. **3. Review** - For a valid PR, reviewer (person familiar with the code/functionality)
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tensorflow/c/eager/gradients.h
absl::Span<AbstractTensorHandle*> grad_inputs) = 0; virtual ~GradientFunction() {} }; // Metadata from the forward operation that is made available to the // gradient registerer to instantiate a GradientFunction. struct ForwardOperation { public: string op_name; std::vector<AbstractTensorHandle*> inputs; std::vector<AbstractTensorHandle*> outputs;
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