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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/eager/gradients_test.cc
UnifiedCAPI, CppGradients, ::testing::Combine(::testing::Values("graphdef", "mlir"), /*tfrt*/ ::testing::Values(false), /*executing_eagerly*/ ::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/experimental/filesystem/plugins/gcs/gcs_filesystem_test.cc
if (TF_GetCode(status) != TF_OK) return ::testing::AssertionFailure() << TF_Message(status); auto metadata = gcs_client->InsertObject(bucket, object, content); if (metadata) return ::testing::AssertionSuccess(); else return ::testing::AssertionFailure() << metadata.status().message(); } ::testing::AssertionResult CompareSubString(int64_t offset, size_t length,
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tensorflow/c/experimental/next_pluggable_device/tensor_pjrt_buffer_util_test.cc
#include "tsl/platform/casts.h" #include "tsl/platform/status_matchers.h" #include "tsl/protobuf/error_codes.pb.h" namespace tensorflow { namespace { using ::testing::HasSubstr; using ::testing::NotNull; using ::tsl::testing::StatusIs; PJRT_Buffer* CreateCBuffer() { auto status = pjrt::PjrtApi(DEVICE_CPU); if (!status.ok()) { CHECK_OK(pjrt::SetPjrtApi(DEVICE_CPU, GetPjrtApi())); }
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tensorflow/c/eager/parallel_device/parallel_device_testlib.cc
#include "tensorflow/core/platform/test.h" // NOTE(allenl): These tests currently go through TFE_Execute and so are // integration testing rather than purely testing the parallel device. They // correspond fairly well to the implementation, but testing the C++ directly is // another option. namespace tensorflow { namespace parallel_device { Variable* Variable::Create(TFE_Context* context, TF_DataType type,
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tensorflow/c/eager/parallel_device/parallel_device_test.cc
// NOTE(allenl): These tests currently go through TFE_Execute and so are // integration testing rather than purely testing the parallel device. They // correspond fairly well to the implementation, but testing the C++ directly is // another option. namespace tensorflow { namespace parallel_device { using ::testing::HasSubstr; TEST(PARALLEL_DEVICE, TestBasicCPU) {
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tensorflow/c/experimental/filesystem/plugins/gcs/gcs_filesystem.h
// of block_size. std::unique_ptr<ExpiringLRUCache<GcsFileStat>> stat_cache; GCSFile(google::cloud::storage::Client&& gcs_client); // This constructor is used for testing purpose only. GCSFile(google::cloud::storage::Client&& gcs_client, bool compose, uint64_t block_size, size_t max_bytes, uint64_t max_staleness, uint64_t stat_cache_max_age, size_t stat_cache_max_entries);
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README.md
A smaller CPU-only package is also available: ``` $ pip install tensorflow-cpu ``` To update TensorFlow to the latest version, add `--upgrade` flag to the above commands. *Nightly binaries are available for testing using the [tf-nightly](https://pypi.python.org/pypi/tf-nightly) and [tf-nightly-cpu](https://pypi.python.org/pypi/tf-nightly-cpu) packages on PyPi.* #### *Try your first TensorFlow program* ```shell $ python ```
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