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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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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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CONTRIBUTING.md
export flags="--config=opt -k" ``` If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and add the `cuda` option flag ```bash export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH" export flags="--config=opt --config=cuda -k" ``` For example, to run all tests under tensorflow/python, do: ```bash
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tensorflow/BUILD
# Config setting that is satisfied when TensorFlow is being built with CUDA # support through e.g. `--config=cuda` (or `--config=cuda_clang` in OSS). alias( name = "is_cuda_enabled", actual = if_oss( "@local_config_cuda//:is_cuda_enabled", "@local_config_cuda//cuda:using_clang", ), ) # Config setting that is satisfied when CUDA device code should be compiled
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tensorflow/c/eager/BUILD
load( "//tensorflow:tensorflow.bzl", "tf_cc_test", "tf_copts", "tf_cuda_cc_test", "tf_cuda_library", ) load("//tensorflow:tensorflow.default.bzl", "cc_header_only_library", "filegroup") load( "//tensorflow/core/platform:build_config_root.bzl", "tf_cuda_tests_tags", ) load("//tensorflow/core/platform:rules_cc.bzl", "cc_library") package(
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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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RELEASE.md
* Move `layers_dense_variational_impl.py` to `layers_dense_variational.py`. ## Known Bugs * Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or `CUDA_ILLEGAL_ADDRESS` failures. Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9 and CUDA 9.1 sometimes does not properly compute the carry bit when
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ci/official/utilities/code_check_full.bats
--@local_config_cuda//:enable_cuda \ --define framework_shared_object=false \ "somepath(//tensorflow/tools/pip_package:wheel, " \ "@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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.bazelrc
build:mkl_aarch64_threadpool -c opt # CUDA: This config refers to building CUDA op kernels with nvcc. build:cuda --repo_env TF_NEED_CUDA=1 build:cuda --crosstool_top=@local_config_cuda//crosstool:toolchain build:cuda --@local_config_cuda//:enable_cuda # CUDA: This config refers to building CUDA op kernels with clang. build:cuda_clang --config=cuda # Enable TensorRT optimizations https://developer.nvidia.com/tensorrt
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