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tensorflow/c/eager/abstract_function.h
#include "tensorflow/core/platform/status.h" namespace tensorflow { class FunctionRecord; // A traced function: this hides the complexity of converting the serialized // representation between various supported formats e.g. FunctionDef and Mlir // function. class AbstractFunction : public core::RefCounted { protected: enum AbstractFunctionKind { kGraph, kMlir };
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RELEASE.md
* `tf.lite`: * New operations supported: * tflite SelectV2 now supports 5D. * `tf.einsum` is supported with multiple unknown shapes. * `tf.unsortedsegmentprod` op is supported. * `tf.unsortedsegmentmax` op is supported. * `tf.unsortedsegmentsum` op is supported. * Updates to existing operations:
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README.md
package from source. ## Continuous build status You can find more community-supported platforms and configurations in the [TensorFlow SIG Build community builds table](https://github.com/tensorflow/build#community-supported-tensorflow-builds). ### Official Builds
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tensorflow/c/experimental/filesystem/modular_filesystem.cc
std::unique_ptr<RandomAccessFile>* result) { if (ops_->new_random_access_file == nullptr) return errors::Unimplemented(tensorflow::strings::StrCat( "Filesystem for ", fname, " does not support NewRandomAccessFile()")); UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus); auto file = std::make_unique<TF_RandomAccessFile>(); std::string translated_name = TranslateName(fname);
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.bazelrc
# v2: Build TF v2 # # Feature and Third party library support options: # xla: Build TF with XLA # tpu: Build TF with TPU support # cuda: Build with CUDA support. # cuda_clang Build with CUDA Clang support. # rocm: Build with AMD GPU support (rocm) # mkl: Enable full mkl support. # tensorrt: Enable Tensorrt support. # noaws: Disable AWS S3 storage support
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tensorflow/c/c_api_experimental.cc
return {}; } const auto& fdef_lib = gdef.library(); if (fdef_lib.gradient_size() > 0) { status->status = tensorflow::errors::Internal( "GradientDef is not supported in reading Dataset related functions: ", text_proto); return {}; } std::vector<UniqueFuncPtr> ret; for (const FunctionDef& fdef : fdef_lib.function()) {
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tensorflow/c/eager/gradients_test.cc
"or NotDifferentiableGradientFunction.", s.message()); ASSERT_EQ(nullptr, outputs[0]); } // TODO(b/164171226): Enable this test with tfrt after AddInputList is // supported. It is needed for IdentityN. #ifdef PLATFORM_GOOGLE INSTANTIATE_TEST_SUITE_P( UnifiedCAPI, CppGradients, ::testing::Combine(::testing::Values("graphdef", "mlir"),
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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/containers/linux_arm64/builder.devtoolset/gcc9-fixups.patch
{ @@ -332,6 +333,7 @@ unsetenv (const char *name) } else ++ep; + } UNLOCK; diff --git a/support/Makefile b/support/Makefile index a253698..2f4e2a9 100644 --- a/support/Makefile +++ b/support/Makefile @@ -167,13 +167,6 @@ CFLAGS-support_paths.c = \ -DINSTDIR_PATH=\"$(prefix)\" \ -DLIBDIR_PATH=\"$(libdir)\" -ifeq (,$(CXX)) -LINKS_DSO_PROGRAM = links-dso-program-c
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.github/bot_config.yml
* Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup). * If error still persists then, apparently your CPU model does not support AVX instruction sets. * Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements). -----------------------------------------------------------------------------------------------
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