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README.md
------------------- | [![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/api_docs/) | [TensorFlow](https://www.tensorflow.org/) is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of [tools](https://www.tensorflow.org/resources/tools), [libraries](https://www.tensorflow.org/resources/libraries-extensions), and
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ci/official/utilities/generate_index_html.sh
<li><a href="http://cs/f:devtools/kokoro/config/prod/$KOKORO_JOB_NAME">Codesearch - job definition</a></li> <li><a href="http://cs/f:learning/brain/testing/kokoro/$(echo "$KOKORO_JOB_NAME" | sed 's!tensorflow/!!g')">Codesearch - build definition & scripts</a></li> <li><a href="http://cs/$KOKORO_JOB_NAME">Codesearch - All references to this job</a></li> </ul>
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tensorflow/c/c_api.cc
mutex_lock session_lock(session->mu); const Graph& graph = session->graph->graph; const string& mutation_warning = session->graph->sessions[session]; if (!mutation_warning.empty()) { // TODO(b/74949947): turn this back into an error status LOG(WARNING) << mutation_warning; session->graph->sessions[session].clear(); } const auto num_nodes = graph.num_node_ids();
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CITATION.cff
cff-version: 1.2.0 message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org." title: TensorFlow, Large-scale machine learning on heterogeneous systems
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ci/README.md
# TensorFlow continuous integration > **Warning** This folder is still under construction. It is part of an ongoing > effort to improve the structure of CI and build related files within the > TensorFlow repo. This warning will be removed when the contents of this > directory are stable and appropriate documentation around its usage is in > place. Maintainer: TensorFlow DevInfra
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tensorflow/c/eager/abstract_context.h
virtual ~AbstractContext() {} public: AbstractContextKind getKind() const { return kind_; } // Release any underlying resources, including the interface object. // // WARNING: The destructor of this class is marked as protected to disallow // clients from directly destroying this object since it may manage its own // lifetime through ref counting. Thus clients MUST call Release() in order to
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tensorflow/c/c_api.h
// called. TF_CAPI_EXPORT extern TF_OperationDescription* TF_NewOperation( TF_Graph* graph, const char* op_type, const char* oper_name); // Specify the device for `desc`. Defaults to empty, meaning unconstrained. TF_CAPI_EXPORT extern void TF_SetDevice(TF_OperationDescription* desc, const char* device); // The calls to TF_AddInput and TF_AddInputList must match (in number,
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.github/workflows/trusted_partners.js
const lowercased_title = (title || '').toLowerCase(); const onednn_assignees = ['penpornk']; if (lowercased_title.includes('onednn')) assignees = onednn_assignees; const intel_windows_assignees = ['nitins17', 'learning-to-play']; if (lowercased_title.includes('intel') && lowercased_title.includes('windows') && domain.includes('intel.com')) assignees = intel_windows_assignees;
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SECURITY.md
## TensorFlow models are programs TensorFlow [**models**](https://developers.google.com/machine-learning/glossary/#model) (to use a term commonly used by machine learning practitioners) are expressed as programs that TensorFlow executes. TensorFlow programs are encoded as computation [**graphs**](https://developers.google.com/machine-learning/glossary/#graph). Since models are practically programs that TensorFlow executes, using untrusted
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.zenodo.json
{ "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.", "license": "Apache-2.0", "title": "TensorFlow", "upload_type": "software", "creators": [ { "name": "TensorFlow Developers" }
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