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.github/workflows/sigbuild-docker-presubmit.yml
- name: Set up Docker Buildx uses: docker/setup-buildx-action@8c0edbc76e98fa90f69d9a2c020dcb50019dc325 # v2.2.1 - name: Login to GCR if: contains(github.event.pull_request.labels.*.name, 'build and push to gcr.io for staging') uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a # v2.1.0 with:
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.github/workflows/arm-ci-extended-cpp.yml
Others - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Wed Feb 07 17:41:21 GMT 2024 - 2.5K bytes - Viewed (0) -
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
Others - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Tue Mar 05 10:24:16 GMT 2024 - 3K bytes - Viewed (1) -
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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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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tensorflow/c/eager/c_api_remote_function_test.cc
bool remote_func_outputs = false, bool has_packed_input = false) { return TestRemoteExecuteSilentCopies(async, remote, /*func=*/true, heavy_load_on_streaming_rpc, remote_func_outputs, has_packed_input); } TEST(CAPI, RemoteExecuteSilentCopiesAsyncFunc) {
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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/code_check_full.bats
_test_gpu$ _test_gpu.py$ _test_lib$ //tensorflow/cc/saved_model:saved_model_test_files //tensorflow/cc/saved_model:saved_model_half_plus_two //tensorflow:no_tensorflow_py_deps //tensorflow/tools/pip_package:win_pip_package_marker //tensorflow/core:image_testdata //tensorflow/core/lib/lmdb:lmdb_testdata //tensorflow/core/lib/lmdb/testdata:lmdb_testdata //tensorflow/core/kernels/cloud:bigquery_reader_ops //tensorflow/python:extra_py_tests_deps
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tensorflow/c/eager/c_api_experimental.h
TF_CAPI_EXPORT TFE_TensorHandle* TFE_NewTensorHandleFromTensor( TFE_Context* ctx, TF_Tensor* t, TF_Status* status); // Create a packed TensorHandle with the given list of TensorHandles. // If `handles` are on the same device, assign the same device to the packed // handle; if `handles` are on different deivces, assign a CompositeDevice to // it. TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_CreatePackedTensorHandle(
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