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tensorflow/c/eager/BUILD
"//tensorflow/core/distributed_runtime:remote_device", "//tensorflow/core/distributed_runtime:server_lib", "//tensorflow/core/distributed_runtime:worker_env", "//tensorflow/core/distributed_runtime:worker_interface", "//tensorflow/core/distributed_runtime/eager:cluster_function_library_runtime", "//tensorflow/core/distributed_runtime/eager:eager_client",
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ci/official/envs/rbe
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RELEASE.md
distributing datasets. For this to work, the dispatcher's `work_dir` must be accessible from workers. If the worker fails to read from the `work_dir`, it falls back to using RPC for dataset graph transfer. * Adds support for a new "distributed_epoch" processing mode. This processing mode distributes a dataset across all tf.data workers, instead of having each worker process the full dataset. See
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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/envs/no_docker
# See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # Disable Docker
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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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.bazelrc
build:rbe_base --jobs=800 build:rbe_base --remote_executor=grpcs://remotebuildexecution.googleapis.com build:rbe_base --remote_timeout=3600 build:rbe_base --spawn_strategy=remote,worker,standalone,local # Attempt to minimize the amount of data transfer between bazel and the remote # workers: build:rbe_base --remote_download_toplevel test:rbe_base --test_env=USER=anon # TODO(kanglan): Check if we want to merge rbe_linux into rbe_linux_cpu.
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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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