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ci/official/envs/linux_x86_cuda
source ci/official/envs/linux_x86 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/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# Change the value of CACHEBUSTER when upgrading the toolchain, or when testing # different compilation methods. E.g. for a PR to test a new CUDA version, set # the CACHEBUSTER to the PR number. build --action_env=CACHEBUSTER=20220325 # Use Python 3.X as installed in container image build --action_env PYTHON_BIN_PATH="/usr/local/bin/python3"
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tensorflow/c/c_api_test.cc
TF_DeleteTensor(t); EXPECT_TRUE(deallocator_called); } TEST(CAPI, LibraryLoadFunctions) { // TODO(b/73318067): Fix linking for the GPU test generated by the // tf_cuda_cc_test() bazel rule and remove the next line. if (!GPUDeviceName().empty()) return; #if !defined(TENSORFLOW_NO_SHARED_OBJECTS) { // Load the library. TF_Status* status = TF_NewStatus();
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ci/official/containers/linux_arm64/cuda.packages.txt
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/containers/linux_arm64/devel.usertools/code_check_full.bats
--experimental_cc_shared_library \ --@local_config_cuda//:enable_cuda \ --define framework_shared_object=false \ "somepath(//tensorflow/tools/pip_package:build_pip_package, " \ "@local_config_cuda//cuda:cudart + "\ "@local_config_cuda//cuda:cudart + "\ "@local_config_cuda//cuda:cuda_driver + "\ "@local_config_cuda//cuda:cudnn + "\ "@local_config_cuda//cuda:curand + "\ "@local_config_cuda//cuda:cusolver + "\
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ci/official/containers/linux_arm64/Dockerfile
# Install devtoolset devel dependencies COPY setup.sources.sh /setup.sources.sh COPY setup.packages.sh /setup.packages.sh COPY devel.packages.txt /devel.packages.txt COPY cuda.packages.txt /cuda.packages.txt RUN /setup.sources.sh && /setup.packages.sh /devel.packages.txt # Install various tools. # - bats: bash unit testing framework # NOTE: v1.6.0 seems to have a bug that made "git" in setup_file break
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.github/bot_config.yml
**1. Installing **TensorFlow-GPU** (TF) prebuilt binaries** Make sure you are using compatible TF and CUDA versions. Please refer following TF version and CUDA version compatibility table. | TF | CUDA | | :-------------: | :-------------: | | 2.5.0 | 11.2 | | 2.4.0 | 11.0 | | 2.1.0 - 2.3.0 | 10.1 |
Others - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Tue Oct 17 11:48:07 GMT 2023 - 4K bytes - Viewed (0) -
tensorflow/c/BUILD
load( "//tensorflow:tensorflow.bzl", "check_deps", "if_google", "if_not_mobile", "tf_cc_test", "tf_copts", "tf_cuda_library", "tf_custom_op_library", "tf_kernel_library", ) load("//tensorflow:tensorflow.default.bzl", "filegroup", "tf_cuda_cc_test") load( "//tensorflow/core/tpu:build_defs.bzl", "if_libtpu_tf_status", ) package(
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ci/official/README.md
# build. This should also match the system you're using--you cannot build # the TF MacOS package from Linux. # Ex. linux_x86 -- x86_64 Linux platform # Ex. linux_x86_cuda -- x86_64 Linux platform, with Nvidia CUDA support # Ex. macos_arm64 -- arm64 MacOS platform # 3. Add modifiers. Some modifiers for local execution are: # Ex. disk_cache -- Use a local cache
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ci/official/envs/linux_arm64
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64 # Note: this is not set to "--cpu", because that changes the package name # to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow" # 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
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