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tensorflow/c/experimental/gradients/nn_grad_test.cc
UnifiedCAPI, CppGradients, ::testing::Combine(::testing::Values("graphdef", "mlir"), /*tfrt*/ ::testing::Values(false), /*use_function*/ ::testing::Values(true, false))); #else INSTANTIATE_TEST_SUITE_P( UnifiedCAPI, CppGradients, ::testing::Combine(::testing::Values("graphdef", "mlir"), /*tfrt*/ ::testing::Values(false),
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ci/official/wheel_test/README.md
## Wheel Test This directory is dedicated to tests that require a built TensorFlow wheel file for testing, such as: * Ensuring the entire API is importable * Testing downstream projects against the wheel Ensure you have Bazel installed and accessible from your command line. These tests use hermetic Python. They also require a built TensorFlow wheel file and a requirements_lock file. The requirements_lock file is generated by the
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false # Change the value of CACHEBUSTER when upgrading the toolchain, or when testing # different compilation methods. E.g. for a PR to test a new CUDA version, set # the CACHEBUSTER to the PR number. build --action_env=CACHEBUSTER=20220325 # Use Python 3.X as installed in container image
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ci/official/utilities/generate_index_html.sh
<li><a href="http://cs/f:devtools/kokoro/config/prod/$KOKORO_JOB_NAME">Codesearch - job definition</a></li> <li><a href="http://cs/f:learning/brain/testing/kokoro/$(echo "$KOKORO_JOB_NAME" | sed 's!tensorflow/!!g')">Codesearch - build definition & scripts</a></li> <li><a href="http://cs/$KOKORO_JOB_NAME">Codesearch - All references to this job</a></li> </ul> <h2>Non-Googler Links</h2>
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false # Change the value of CACHEBUSTER when upgrading the toolchain, or when testing # different compilation methods. E.g. for a PR to test a new CUDA version, set # the CACHEBUSTER to the PR number. build --action_env=CACHEBUSTER=20220325 # Use Python 3.X as installed in container image
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ci/official/containers/linux_arm64/Dockerfile
COPY devel.packages.txt /devel.packages.txt 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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tensorflow/c/eager/parallel_device/parallel_device_remote_test.cc
tensorflow::ClusterDef* cluster_def = server_def.mutable_cluster(); tensorflow::JobDef* job_def = cluster_def->add_job(); job_def->set_name(job_name); for (int i = 0; i < num_tasks; i++) { int port = tensorflow::testing::PickUnusedPortOrDie(); job_def->mutable_tasks()->insert( {i, tensorflow::strings::StrCat("localhost", ":", port)}); LOG(INFO) << "Picked test port: " << port << " for job: " << job_name
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tensorflow/c/experimental/gradients/array_grad_test.cc
UnifiedCAPI, CppGradients, ::testing::Combine(::testing::Values("graphdef", "mlir"), /*tfrt*/ ::testing::Values(false), /*use_function*/ ::testing::Values(true, false))); #else INSTANTIATE_TEST_SUITE_P( UnifiedCAPI, CppGradients, ::testing::Combine(::testing::Values("graphdef", "mlir"), /*tfrt*/ ::testing::Values(false),
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tensorflow/c/eager/tfe_monitoring_reader_internal.h
explicit TFE_MonitoringCounterReader(const char* name) { counter = std::make_unique< ::tensorflow::monitoring::testing::CellReader<int64_t>>(name); } template <typename... LabelType> int64_t Read(const LabelType&... labels); std::unique_ptr<::tensorflow::monitoring::testing::CellReader<int64_t>> counter; };
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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 cd tensorflow-git-dir
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