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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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RELEASE.md
# Release 2.12.0 ### Breaking Changes * Build, Compilation and Packaging
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# Use a cache directory mounted to /tf/cache. Very useful! build:sigbuild_local_cache --disk_cache=/tf/cache # Use the public-access TF DevInfra cache (read only) build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false # 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
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ci/official/utilities/setup_macos.sh
# Libtensorflow archives) to GCS buckets. TFCI Mac VMs need to authenticate as # a service account that has the right permissions to be able to do so. set +x if [[ -n "${GOOGLE_APPLICATION_CREDENTIALS:-}" ]]; then # Python 3.12 removed the module `imp` which is needed by gcloud CLI so we set # `CLOUDSDK_PYTHON` to Python 3.11 which is the system Python on TFCI Mac # VMs. export CLOUDSDK_PYTHON=$(which python3.11)
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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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.bazelrc
build:resultstore --bes_timeout=600s # Flag to enable remote config common --experimental_repo_remote_exec # Make Bazel not try to probe the host system for a C++ toolchain. build:rbe_base --config=resultstore build:rbe_base --repo_env=BAZEL_DO_NOT_DETECT_CPP_TOOLCHAIN=1 build:rbe_base --define=EXECUTOR=remote build:rbe_base --jobs=800 build:rbe_base --remote_executor=grpcs://remotebuildexecution.googleapis.com
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tensorflow/c/eager/c_api_experimental.h
// Dynamic cluster API. // Update an existing context with a new set of servers defined in a ServerDef // proto. Servers can be added to and removed from the list of remote workers // in the context. A New set of servers identified by the ServerDef must be up // when the context is updated. // // This API is for experimental usage and may be subject to change.
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tensorflow/c/BUILD
visibility = ["//tensorflow/python:__pkg__"], deps = [ ":c_api", ":c_api_internal", "//tensorflow/core:protos_all_cc", # TODO(b/74620627): remove when _USE_C_SHAPES is removed ], alwayslink = 1, ) cc_library( name = "conversion_macros", hdrs = [ "conversion_macros.h", ],
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tensorflow/api_template.__init__.py
import distutils as _distutils import importlib import inspect as _inspect import os as _os import site as _site import sys as _sys # Do not remove this line; See https://github.com/tensorflow/tensorflow/issues/42596 from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow # pylint: disable=unused-import from tensorflow.python.tools import module_util as _module_util
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tensorflow/c/experimental/gradients/nn_grad.cc
* * dA = U * dbias = reduceSum(U, dims = channel_dim) */ AbstractTensorHandle* upstream_grad = grad_outputs[0]; DCHECK(upstream_grad); // Recover data format from forward pass for gradient. std::string data_format; TF_RETURN_IF_ERROR(forward_attrs_.Get("data_format", &data_format)); // Grad for A grad_inputs[0] = upstream_grad;
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