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ci/official/containers/linux_arm64/build.sh
else TAG="pr-${KOKORO_GITHUB_PULL_REQUEST_NUMBER}" fi fi # Build for both JAX and TF usage. We do these in one place because they share # almost all of the same cache layers export DOCKER_BUILDKIT=1 for target in jax tf; do IMAGE="gcr.io/tensorflow-sigs/build-arm64:$target-$TAG" docker pull "$IMAGE" || true # Due to some flakiness of resources pulled in the build, allow the docker
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README.md
* [TensorFlow YouTube](https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ) * [TensorFlow model optimization roadmap](https://www.tensorflow.org/model_optimization/guide/roadmap) * [TensorFlow White Papers](https://www.tensorflow.org/about/bib) * [TensorBoard Visualization Toolkit](https://github.com/tensorflow/tensorboard) * [TensorFlow Code Search](https://cs.opensource.google/tensorflow/tensorflow) Learn more about the
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ci/official/README.md
- Uses `pycpp.sh` - Presubmit jobs (Run on every GitHub PR) - Uses `pycpp.sh`, `code_check_changed_files.sh` These "env" files match up with an environment matrix that roughly covers: - Different Python versions - Linux, MacOS, and Windows machines (these pool definitions are internal) - x86 and arm64 - CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs
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tensorflow/api_template_v1.__init__.py
# Some tf endpoints actually lives under Keras. _current_module.layers = _KerasLazyLoader( globals(), submodule="__internal__.legacy.layers", name="layers", mode="v1") if _tf_uses_legacy_keras: _module_dir = _module_util.get_parent_dir_for_name( "tf_keras.api._v1.keras.__internal__.legacy.layers") else: _module_dir = _module_util.get_parent_dir_for_name(
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RELEASE.md
* Update `tf.keras` to the Keras 2.1.6 API. * Added [`tf.keras.layers.CuDNNGRU`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNGRU) and [`tf.keras.layers.CuDNNLSTM`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNLSTM) layers.
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tensorflow/c/eager/c_api_experimental.h
typedef struct TFE_MonitoringStringGaugeCell TFE_MonitoringStringGaugeCell; TF_CAPI_EXPORT extern void TFE_MonitoringStringGaugeCellSet( TFE_MonitoringStringGaugeCell* cell, const char* value); // Retrieves the string value and saves it in the buffer. TF_CAPI_EXPORT extern const void TFE_MonitoringStringGaugeCellValue( TFE_MonitoringStringGaugeCell* cell, TF_Buffer* buf); // APIs for String Gauge without label.
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SECURITY.md
vulnerabilities to the vendor of the affected hardware accelerator. ## Reporting vulnerabilities ### Vulnerabilities in TensorFlow This document covers different use cases for TensorFlow together with comments whether these uses were recommended or considered safe, or where we recommend some form of isolation when dealing with untrusted data. As a result, this
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tensorflow/c/eager/immediate_execution_context.h
virtual Status AddRemoveFunctionNotifier(const string& func, std::function<void()> notifier) = 0; // Same as `AddFunctionDef`, but additionally saves the `stack_traces` under // the key of the function definition name (to be retrieved during function // instantiation). virtual Status AddFunctionDefWithStackTraces(
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