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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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configure.py
def set_tf_cuda_version(environ_cp): """Set TF_CUDA_VERSION.""" ask_cuda_version = ( 'Please specify the CUDA SDK version you want to use. ' '[Leave empty to default to CUDA %s]: ') % _DEFAULT_CUDA_VERSION tf_cuda_version = get_from_env_or_user_or_default(environ_cp, 'TF_CUDA_VERSION',
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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/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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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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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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