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CODEOWNERS
# Where component owners are known, add them here. /tensorflow/c/eager @qqfish /tensorflow/core/common_runtime/eager @qqfish /tenosrflow/core/debug @caisq /tensorflow/core/kernels/mkl/ @penpornk /tensorflow/core/kernels/sparse/ @penpornk /tensorflow/core/nccl/ @azaks2 @chsigg /tensorflow/python/autograph/ @mdanatg /tensorflow/python/debug @caisq /tensorflow/python/eager @rohan100jain /tensorflow/tools/docs/ @markdaoust
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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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CONTRIBUTING.md
#### General guidelines and philosophy for contribution * Include unit tests when you contribute new features, as they help to a) prove that your code works correctly, and b) guard against future breaking changes to lower the maintenance cost. * Bug fixes also generally require unit tests, because the presence of bugs usually indicates insufficient test coverage. * Keep API compatibility in mind when you change code in core TensorFlow,
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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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LICENSE
other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the
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RELEASE.md
* `tf.data` * `tf.data` now has an `autotune_options.initial_parallelism` option to control the initial parallelism setting used by autotune before the data pipeline has started running. The default is 16. A lower value reduces initial memory usage, while a higher value improves startup time. ## Keras * `keras.layers.experimental.DynamicEmbedding` * Added `DynamicEmbedding` Keras layer
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