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CONTRIBUTING.md
using a job called "copybara". **5. Copy to Google Internal codebase and run internal CI** - Once the PR is in the Google codebase, we make sure it integrates well with its dependencies and the rest of the system. - Rarely, If the tests fail at this stage, we cannot merge the code. - If needed, we may come to you to make some changes. At times, it may not be
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tensorflow/BUILD
":macos_arm64", ], visibility = ["//visibility:public"], ) config_setting( name = "ios", constraint_values = if_google( ["//third_party/bazel_platforms/os:ios"], [], ), values = if_oss( {"apple_platform_type": "ios"}, {}, ), visibility = ["//visibility:public"], ) # TODO(jakeharmon8): Remove in favor of TSL version
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RELEASE.md
configure a `work_dir` when running your dispatcher server and set `dispatcher_fault_tolerance=True`. The dispatcher will store its state to `work_dir`, so that on restart it can continue from its previous state after restart. * Adds support for sharing dataset graphs via shared filesystem instead of over RPC. This reduces load on the dispatcher, improving performance of
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.bazelrc
build:macos_arm64 --macos_minimum_os=11.0 # iOS configs for each architecture and the fat binary builds. build:ios --apple_platform_type=ios build:ios --apple_bitcode=embedded --copt=-fembed-bitcode build:ios --copt=-Wno-c++11-narrowing build:ios_armv7 --config=ios build:ios_armv7 --cpu=ios_armv7 build:ios_arm64 --config=ios build:ios_arm64 --cpu=ios_arm64 build:ios_arm64e --config=ios build:ios_arm64e --cpu=ios_arm64e
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configure.py
] # List of files to configure when building Bazel on Apple platforms. APPLE_BAZEL_FILES = [ 'tensorflow/lite/ios/BUILD', 'tensorflow/lite/objc/BUILD', 'tensorflow/lite/swift/BUILD', 'tensorflow/lite/tools/benchmark/experimental/ios/BUILD' ] # List of files to move when building for iOS. IOS_FILES = [ 'tensorflow/lite/objc/TensorFlowLiteObjC.podspec', 'tensorflow/lite/swift/TensorFlowLiteSwift.podspec', ]
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tensorflow/c/eager/tape.h
// targets (not all since targets which have outputs in the tape will not have // gradients available initially). // // Then we repeatedly pop an entry from the stack, run its backprop, and update // the gradients of its inputs. Once we have computed all gradients for a single // input we can mark this input as done, and this can trigger adding an entry to // the stack if all outputs of that entry are now done. //
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