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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/envs/linux_x86_cuda
source ci/official/envs/linux_x86 TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_gpu_linux" TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cuda TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ARGS="--gpus all" TFCI_LIB_SUFFIX="-gpu-linux-x86_64"
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.github/bot_config.yml
**1. Installing **TensorFlow-GPU** (TF) prebuilt binaries** Make sure you are using compatible TF and CUDA versions. Please refer following TF version and CUDA version compatibility table. | TF | CUDA | | :-------------: | :-------------: | | 2.5.0 | 11.2 | | 2.4.0 | 11.0 | | 2.1.0 - 2.3.0 | 10.1 |
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tensorflow/c/experimental/gradients/BUILD
"//tensorflow/core:test", "//tensorflow/core:test_main", ], ) tf_cuda_cc_test( name = "nn_grad_test", size = "small", srcs = [ "nn_grad_test.cc", ], args = ["--heap_check="], # TODO(b/174752220): Remove tags = tf_cuda_tests_tags() + ["no_cuda_asan"], # b/173654156, deps = [ ":grad_test_helper", ":nn_grad",
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ci/official/utilities/rename_and_verify_wheels.sh
fi # VERY basic check to ensure the [and-cuda] package variant is installable. # Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is # relevant. All of the GPU test machines have CUDA installed via other means, # so I am not sure how to verify that the dependencies themselves are valid for # the moment. if [[ "$TFCI_BAZEL_COMMON_ARGS" =~ gpu|cuda ]]; then
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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/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/ISSUE_TEMPLATE/tensorflow_issue_template.yaml
description: If compiling from source - type: input id: Compiler attributes: label: GCC/compiler version description: If compiling from source - type: input id: Cuda attributes: label: CUDA/cuDNN version - type: input id: Gpu attributes: label: GPU model and memory description: If compiling from source - type: textarea id: what-happened
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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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ci/official/libtensorflow.sh
# limitations under the License. # ============================================================================== source "${BASH_SOURCE%/*}/utilities/setup.sh" # Record GPU count and CUDA version status if [[ "$TFCI_NVIDIA_SMI_ENABLE" == 1 ]]; then tfrun nvidia-smi fi # Update the version numbers for Nightly only if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
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