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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 23 12:39:09 GMT 2024 - Last Modified: Thu Aug 10 20:26:29 GMT 2023 - 998 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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.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/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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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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