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tensorflow/c/c_api_test.cc
EXPECT_TRUE(absl::StrContains(s, expected)) << "'" << s << "' does not contain '" << expected << "'"; } // Returns the GPU device name if there is one (with arbitrary tie breaking if // there are more than one), or "" otherwise. string GPUDeviceName(TF_Session* session) { std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status( TF_NewStatus(), TF_DeleteStatus); TF_Status* s = status.get();
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CONTRIBUTING.md
- Ensure you have signed the [Contributor License Agreement (CLA)](https://cla.developers.google.com/). - Check if your changes are consistent with the [guidelines](#general-guidelines-and-philosophy-for-contribution). - Changes are consistent with the [Coding Style](#c-coding-style). - Run the [unit tests](#running-unit-tests). ## How to become a contributor and submit your own code
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tensorflow/api_template.__init__.py
TensorFlow via the command `import tensorflow as tf`. The primary function of this module is to import all of the public TensorFlow interfaces into a single place. The interfaces themselves are located in sub-modules, as described below. Note that the file `__init__.py` in the TensorFlow source code tree is actually only a placeholder to enable test cases to run. The TensorFlow build replaces
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tensorflow/c/experimental/gradients/math_grad.cc
absl::Span<AbstractTensorHandle* const> grad_outputs, absl::Span<AbstractTensorHandle*> grad_inputs) override { /* Given upstream grad U and a matmul op A*B, the gradients are: * * dA = U * B.T * dB = A.T * U * * where A.T means `transpose(A)` */ AbstractTensorHandle* upstream_grad = grad_outputs[0]; // Get transpose attrs
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RELEASE.md
different computation approaches and orders. * To verify if oneDNN optimizations are on, look for a message with *"oneDNN custom operations are on"* in the log. If the exact phrase is not there, it means they are off. * Making the `tf.function` type system fully available:
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tensorflow/c/experimental/filesystem/BUILD
# Experimental filesystem C APIs for TensorFlow. # Will be moved in proper place once all filesystems are converted to the # modular framework. load("//tensorflow:tensorflow.bzl", "tf_cc_test") load("//tensorflow/core/platform:rules_cc.bzl", "cc_library") package( # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"], licenses = ["notice"], ) # This is only for plugins cc_library( name = "filesystem_interface",
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tensorflow/c/experimental/gradients/nn_grad.cc
absl::Span<AbstractTensorHandle* const> grad_outputs, absl::Span<AbstractTensorHandle*> grad_inputs) override { /* Given upstream grad U and a BiasAdd: A + bias, the gradients are: * * dA = U * dbias = reduceSum(U, dims = channel_dim) */ AbstractTensorHandle* upstream_grad = grad_outputs[0]; DCHECK(upstream_grad);
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.github/workflows/update-rbe.yml
map sigbuild-r2.12-python3.9 2.12-python3.9 map sigbuild-r2.12-python3.10 2.12-python3.10 map sigbuild-r2.12-python3.11 2.12-python3.11 # TF 2.12 + Clang (containers are the same, but env vars in configs.bzl are different) map sigbuild-r2.12-clang 2.12-python3.9 map sigbuild-r2.12-clang-python3.8 2.12-python3.8 map sigbuild-r2.12-clang-python3.9 2.12-python3.9
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ci/official/utilities/code_check_full.bats
# the dependencies of that target which include a license notice file are then # included when generating that license. Necessary because the license targets # in TensorFlow are manually enumerated rather than generated automatically. do_external_licenses_check(){ BUILD_TARGET="$1" LICENSES_TARGET="$2" # grep patterns for targets which are allowed to be missing from the licenses
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tensorflow/c/eager/tape.h
}; // If `persistent_tape` is true, op_tape is not changed and none of the // backwards functions are deleted. // If `persistent_tape` is false, op_tape is cleared and backwards functions // not needed for gradient computation are deleted. Backwards functions that // are needed, are copied and returned in BackpropInitialState. template <typename BackwardFunction, typename TapeTensor>
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