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ci/README.md
> **Warning** This folder is still under construction. It is part of an ongoing > effort to improve the structure of CI and build related files within the > TensorFlow repo. This warning will be removed when the contents of this > directory are stable and appropriate documentation around its usage is in > place. Maintainer: TensorFlow DevInfra ********************************************************************************
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ci/official/containers/linux_arm64/builder.devtoolset/rpm-patch.sh
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tensorflow/c/eager/c_api_distributed_test.cc
TFE_ContextAsyncWait(ctx, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); // Pack 3 variable handles into one TFE_TensorHandle. // When remote is false, function device is placed on task0. Handle types are // REMOTE, REMOTE, LOCAL on task0. When remote is true, function device is // placed on task1, Handle types are LOCAL, REMOTE, LOCAL on task1. int num_replicas = 3;
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ci/official/envs/nightly_upload
# 2. The internal version numbers get changed TFCI_NIGHTLY_UPDATE_VERSION_ENABLE=1 # 3. Push results to public build cache (unique cache for MacOS) if [[ $(uname -s) == "Darwin" ]]; then TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache_push" else TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_cache_push"
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tensorflow/c/eager/parallel_device/parallel_device.h
// "/job:localhost/replica:0/task:0/device:CUSTOM:0" // and `underlying_devices` is // {"/job:localhost/replica:0/task:0/device:GPU:0", // "/job:localhost/replica:0/task:0/device:GPU:1"} // Then executing an operation on CUSTOM:0 will execute it on GPU:0 and GPU:1. // // Implicit copies onto `device_name` are allowed, replicating the value once // per device in `underlying_devices`. Implicit copies off of the device throw
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ci/official/utilities/extract_resultstore_links.py
# limitations under the License. # ============================================================================== """Extracts ResultStore links from a log containing Bazel invocations. The links and the invocations' status can then be printed out, or output in the form of JUnit-based XML. """ import argparse import datetime import os import re from typing import Dict, Union import xml.etree.ElementTree as ElemTree
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tensorflow/c/c_api_function.cc
} else { // This node is referenced in inputs. Currently, we place an // artificial restriction and require that when num_opers=-1, such // nodes must have a single output. if (node->num_outputs() != 1) { return InvalidArgument( "When `num_opers` is set to -1, nodes referenced in `inputs` " "must have a single output. Node ",
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tensorflow/c/eager/gradient_checker.cc
TF_Tensor* model_out_tensor; TF_RETURN_IF_ERROR(GetValue(model_out.get(), &model_out_tensor)); int num_dims_out = TF_NumDims(model_out_tensor); TF_DeleteTensor(model_out_tensor); // If the output is a scalar, then return the scalar output if (num_dims_out == 0) { outputs[0] = model_out.release(); return absl::OkStatus(); } // Else, reduce sum the output to get a scalar
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tensorflow/c/eager/parallel_device/parallel_device.cc
std::unique_ptr<ParallelTensor> t, TF_Status* status) { // The resulting TensorHandle owns an opaque pointer to "device memory", which // for a ParallelDevice is really a ParallelTensor. When the TensorHandle is // deleted, it will call ParallelTensorDeallocator to free the struct. ParallelTensor* t_released = t.release(); TFE_CustomDeviceTensorHandleMethods handle_methods;
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tensorflow/c/eager/parallel_device/parallel_device_lib.cc
PartialTensorShape first_shape; TF_RETURN_IF_ERROR(unwrap(tensors_[0].get())->Shape(&first_shape)); return errors::Unimplemented(absl::StrCat( "Computing the shape of a ParallelTensor when the components do " "not all have the same rank is not supported. One tensor had " "shape ", first_shape.DebugString(), " and another had shape ",
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