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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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ci/official/utilities/code_check_changed_files.bats
# ============================================================================== setup_file() { bazel version # Start the bazel server # Fixes "fatal: detected dubious ownership in repository" for Docker. git config --system --add safe.directory '*' git config --system protocol.file.allow always # Note that you could generate a list of all the affected targets with e.g.:
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
build --distinct_host_configuration=false # 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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ci/official/utilities/cleanup_docker.sh
cat <<EOF IMPORTANT: These tests ran under docker. This script does not clean up the container for you! You can delete the container with: $ docker rm -f tf You can also execute more commands within the container with e.g.: $ docker exec tf bazel clean $ docker exec -it tf bash EOF
Shell Script - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Aug 10 20:26:29 GMT 2023 - 998 bytes - Viewed (0) -
ci/official/README.md
# container and start fresh, run "docker rm -f tf". Removing the container # destroys some temporary bazel data and causes longer builds. # # You will need the NVIDIA Container Toolkit for GPU testing: # https://github.com/NVIDIA/nvidia-container-toolkit # # Note: if you interrupt a bazel command on docker (ctrl-c), you # will need to run `docker exec tf pkill bazel` to quit bazel. #
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CONTRIBUTING.md
execution time and the sharding [could create an overhead on the test execution](https://github.com/bazelbuild/bazel/issues/2113#issuecomment-264054799). 2. Using [Docker](https://www.docker.com) and TensorFlow's CI scripts. ```bash # Install Docker first, then this will build and run cpu tests tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/... ``` See
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tensorflow/c/eager/c_api_experimental.h
// EagerService to maintain consistency between client and worker. The // context_id is initialized with a dummy value and is later set when the worker // is initialized (either locally or remotely). The context_id can change during // the process lifetime although this should cause the worker to be // reinitialized (e.g. cleared caches) as well.
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Feb 21 22:37:46 GMT 2024 - 39.5K bytes - Viewed (0) -
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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