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CONTRIBUTING.md
`comp:` etc. At this stage we check if the PR is valid and meets certain quality requirements. For example, we check if the CLA is signed, PR has sufficient description, if applicable unit tests are added, if it is a reasonable contribution (meaning it is not a single liner cosmetic PR). **2. Valid?** - If the PR passes all the quality checks then we go ahead and assign a reviewer.
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.github/bot_config.yml
----------------------------------------------------------------------------------------------- **2. Installing **TensorFlow** (TF) CPU prebuilt binaries** *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.* Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
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tensorflow/c/experimental/filesystem/plugins/gcs/gcs_filesystem.cc
return -1; } int64_t read; auto content_length = stream.headers().find("content-length"); if (content_length == stream.headers().end()) { // When we read a file with offset that is bigger than the actual file size. // GCS will return an empty header (e.g no `content-length` header). In this // case, we will set read to `0` and continue. read = 0;
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RELEASE.md
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 * Added 'UpdateEmbeddingCallback`
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configure.py
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