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tensorflow/c/eager/parallel_device/parallel_device_remote_test.cc
BasicTestsForTwoDevices(context.get(), "/job:worker/replica:0/task:1/device:CPU:0", "/job:worker/replica:0/task:2/device:CPU:0"); worker_server1.release(); worker_server2.release(); } TEST(PARALLEL_DEVICE, TestAsyncCopyOff) { std::unique_ptr<TFE_ContextOptions, decltype(&TFE_DeleteContextOptions)> opts(
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.github/workflows/arm-ci-extended-cpp.yml
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ci/official/envs/linux_arm64
# despite lacking Nvidia CUDA support. TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ENABLE=1 TFCI_DOCKER_IMAGE=gcr.io/tensorflow-sigs/build-arm64:tf-2-16-multi-python TFCI_DOCKER_PULL_ENABLE=1 TFCI_DOCKER_REBUILD_ARGS="--target=tf ci/official/containers/linux_arm64" TFCI_INDEX_HTML_ENABLE=1 TFCI_LIB_SUFFIX="-cpu-linux-arm64" TFCI_OUTPUT_DIR=build_output
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.github/workflows/arm-cd.yml
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ci/official/containers/linux_arm64/cuda.packages.txt
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation libcudnn8=8.9.6.50-1+cuda12.2 libcudnn8-dev=8.9.6.50-1+cuda12.2 # This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image. # For now it ensures that we install at least version 12.3.107 of PTXAS, # since 12.3.103 has a bug.
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tensorflow/c/eager/immediate_execution_distributed_manager.h
int64_t init_timeout_in_ms, int retries, bool clear_existing_contexts = false) = 0; // Initializes context for the local worker and no contexts will be created // for remote workers. Currently this only works for resetting context. // TODO(b/289445025): Consider removing this when we find a proper fix.
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
build --copt=-Wno-gnu-offsetof-extensions # Store performance profiling log in the mounted artifact directory. # The profile can be viewed by visiting chrome://tracing in a Chrome browser. # See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling build --profile=/tf/pkg/profile.json.gz # Use the rebuilt gcc toolchain to compile for manylinux2014
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.github/workflows/arm-ci.yml
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ci/official/envs/rbe
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ci/official/envs/linux_x86_cuda
TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_gpu_linux" TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cuda TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ARGS="--gpus all" TFCI_LIB_SUFFIX="-gpu-linux-x86_64"
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