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ci/official/containers/linux_arm64/builder.devtoolset/build_devtoolset.sh
# `VERSION` parameter. # Note: 'Templatizing' this and the other conditional branches would require # defining several variables (version, os, path) making it difficult to maintain # and extend for future modifications. mkdir -p glibc-src mkdir -p glibc-build cd glibc-src wget --retry-connrefused --waitretry=1 --read-timeout=20 --timeout=15 --tries=5 "https://vault.centos.org/centos/7/os/Source/SPackages/glibc-2.17-317.el7.src.rpm"
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tensorflow/c/BUILD
"//tensorflow/core/common_runtime/next_pluggable_device/c:tf_rendezvous_c_api", "//tensorflow/core/common_runtime/next_pluggable_device/c:tf_rendezvous_c_api_internal", "//tensorflow/core/platform:notification", "@com_google_absl//absl/status", "@com_google_absl//absl/strings", "@local_xla//xla/tsl/c:tsl_status_internal", ] + select({ "//tensorflow:android": [
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SECURITY.md
process images, audio, videos, and text. There are several modules specialized in taking those formats, modifying them, and/or converting them to intermediate formats that can be processed by TensorFlow. These modifications and conversions are handled by a variety of libraries that have different security properties and provide different levels of confidence when dealing with untrusted data. Based on the security history of these
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RELEASE.md
forward compatible code: by explicitly importing either `tensorflow.compat.v1` or `tensorflow.compat.v2`, you can ensure that your code works without modifications against an installation of 1.15 or 2.0. * EagerTensor now supports numpy buffer interface for tensors. * Add toggles `tf.enable_control_flow_v2()` and `tf.disable_control_flow_v2()`
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tensorflow/c/c_api_experimental.cc
TF_Status* status) { tensorflow::EagerContext* context = tensorflow::ContextFromInterface(tensorflow::unwrap(ctx)); auto collective_executor_handle = context->GetCollectiveExecutorHandle(); tensorflow::Notification done; collective_executor_handle->get()->remote_access()->CheckPeerHealth( task, timeout_in_ms, [&done, status](const Status& s) { status->status = s; done.Notify(); });
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tensorflow/c/c_api.h
// // We expose this here to allow fine-grained synchronization in multi-threaded // workloads, which is required since the Python implementation depends on the // above mutation methods. This allows us to prevent modifications to nodes in // the graph after the session has been made aware of them. TF_CAPI_EXPORT extern void TF_ExtendSession(TF_Session* session, TF_Status* status);
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