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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1 # Prevent double-compilation of some TF code, ref. b/183279666 (internal) # > TF's gen_api_init_files has a genrule to run the core TensorFlow code # > on the host machine. If we don't have --distinct_host_configuration=false, # > the core TensorFlow code will be built once for the host and once for the # > target platform.
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tensorflow/c/eager/parallel_device/BUILD
licenses = ["notice"], ) # Currently pybind extension shared objects must use only C API headers since # the C API has static initializers duplicated in the Python bindings. So we # need a second rule that omits .cc files, in # tensorflow/python:_pywrap_parallel_device. filegroup( name = "lib_headers", srcs = ["parallel_device_lib.h"], ) filegroup( name = "lib_sources",
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tensorflow/c/eager/c_api_test.cc
if (forward_input) { // Since the input was forwarded, we released the input handle right away // and hence expect the input to be forwarded to the return tensor. EXPECT_EQ(orig_ptr, TF_TensorData(t)); } else { // In async mode we expect forwarding to work without releasing the input // handle since by the time the kernel is executed we have released the // handle in the client code.
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tensorflow/c/experimental/gradients/nn_grad_test.cc
// `gradient_checker` only works with model that returns only 1 tensor. // Although, `ops::SparseSoftmaxCrossEntropyWithLogits` returns 2 tensors, the // second tensor isn't needed for computing gradient so we could safely drop // it. outputs[0] = loss; backprop->Unref(); return absl::OkStatus(); } Status BiasAddModel(AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
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tensorflow/c/experimental/filesystem/modular_filesystem.cc
UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus); std::string translated_name = TranslateName(dir); // Note that `children` is allocated by the plugin and freed by core // TensorFlow, so we need to use `plugin_memory_free_` here. char** children = nullptr; const int num_children = ops_->get_children(filesystem_.get(), translated_name.c_str(), &children, plugin_status.get());
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tensorflow/c/c_api_test.cc
src.flat<tstring>()(i) = data[i]; } TF_Tensor* dst = TF_TensorFromTensor(src, &status); ASSERT_TRUE(status.ok()) << status.message(); // Convert back to a C++ Tensor and ensure we get expected output. Tensor output; ASSERT_EQ(absl::OkStatus(), TF_TensorToTensor(dst, &output)) << line; ASSERT_EQ(src.NumElements(), output.NumElements()) << line;
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CONTRIBUTING.md
using a job called "copybara". **5. Copy to Google Internal codebase and run internal CI** - Once the PR is in the Google codebase, we make sure it integrates well with its dependencies and the rest of the system. - Rarely, If the tests fail at this stage, we cannot merge the code. - If needed, we may come to you to make some changes. At times, it may not be
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ci/official/utilities/setup_macos.sh
chmod +x "/usr/local/bin/bazel" fi # "TFCI_MACOS_UPGRADE_PYENV_ENABLE" is used to decide if we need to upgrade the # Pyenv version. We enable this for macOS x86 builds as the default Pyenv on # those VMs does not support installing Python 3.12 and above which we need # for running smoke tests in nightly/release wheel builds. if [[ "${TFCI_MACOS_UPGRADE_PYENV_ENABLE}" == 1 ]]; then
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SECURITY.md
whether these uses were recommended or considered safe, or where we recommend some form of isolation when dealing with untrusted data. As a result, this document also outlines what issues we consider as TensorFlow security vulnerabilities. We recognize issues as vulnerabilities only when they occur in scenarios that we outline as safe; issues that have a security impact only when TensorFlow is used
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ci/official/requirements_updater/requirements.in
typing_extensions == 4.8.0 gast == 0.4.0 termcolor == 2.3.0 wrapt == 1.16.0 tblib == 2.0.0 # Install tensorboard, and keras # Note that here we want the latest version that matches TF major.minor version # Note that we must use nightly here as these are used in nightly jobs # For release jobs, we will pin these on the release branch keras-nightly ~= 3.0.0.dev tb-nightly ~= 2.17.0.a # Test dependencies grpcio >= 1.24.3, < 2.0
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