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ci/official/wheel_test/README.md
e.g.: bash update_requirements.sh /tmp/tensorflow-2.14.0-cp311-cp311-linux_x86_64.whl 3_11 ``` #### Requirements Updater Script This script automates the process of updating TensorFlow requirements for a specific Python version. ##### Parameters `path_to_tensorflow_wheel`: The local path to the TensorFlow wheel file. Example: `/tmp/tensorflow-2.14.0-cp311-cp311-linux_x86_64.whl`
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ci/official/requirements_updater/README.md
``` --repo_env=TF_PYTHON_VERSION=3.11 ``` ## Requirements updater Requirements updater is a standalone tool, intended to simplify process of updating requirements for multiple minor versions of Python. It takes in a file with a set of dependencies, and produces a more detailed requirements file for each version, with hashes specified for each
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tensorflow/c/eager/c_api.cc
return l; } void TFE_ContextClearCaches(TFE_Context* ctx) { tensorflow::unwrap(ctx)->ClearCachesAndThreadExecutors(); } // Set server_def on the context, possibly updating it. TF_CAPI_EXPORT extern void TFE_ContextSetServerDef(TFE_Context* ctx, int keep_alive_secs, const void* proto,
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tensorflow/c/c_api_experimental.cc
status->status = absl::OkStatus(); return tensorflow::wrap(tensorflow::TensorHandle::CreateLocalHandle(tensor)); } // Set server_def on the context, possibly updating it. TF_CAPI_EXPORT extern void TFE_EnableCollectiveOps(TFE_Context* ctx, const void* proto, size_t proto_len,
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tensorflow/c/c_api.cc
// This modification only updates the destination node for // the purposes of running this graph in a session. Thus, we don't // record the source node as being modified. RecordMutation(graph, *dst.oper, "updating input tensor"); } } // Apis that are corresponding to python c api. -------------------------- void TF_AddOperationControlInput(TF_Graph* graph, TF_Operation* op,
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RELEASE.md
## Keras * `keras.layers.experimental.DynamicEmbedding` * Added `DynamicEmbedding` Keras layer * Added 'UpdateEmbeddingCallback` * `DynamicEmbedding` layer allows for the continuous updating of the vocabulary and embeddings during the training process. This layer maintains a hash table to track the most up-to-date vocabulary based on
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