OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
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Szymon Durawa b58e982e6a
Dynamic shape cell (#1683)
* validate_and_infer_types() implementation

* input parameter validation for LSTM, GRU and RNN

* style-check applied

* Add LSTMSequence dynamic shape validation and test props for RNNCell, GRUCell, LSTMCell and LSTMSequence.

* recurrent_sequence.hpp moved to ngraph/core/include/ngraph/op/util/

* style check applied

* removed unused variable from LSTMSequence::validate_and_infer_types

* Add missing newline mark at the end of file.

* Add supression macro for FusedOp deprecation.

* Add element type initialization

* Apply PR review remarks

* Rewrite tests to use gtest exception assertions.
2020-09-01 12:01:20 +03:00
.ci/openvino-onnx Removed NGRAPH_IE_ENABLE flag because it is always ON if unit tests are enabled (#2003) 2020-09-01 06:03:59 +03:00
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cmake Simplified plugin interfaces (#1745) 2020-08-14 12:11:54 +03:00
docs Intepolate-4 nGraph operation (#1412) 2020-09-01 06:57:34 +03:00
inference-engine Intepolate-4 nGraph operation (#1412) 2020-09-01 06:57:34 +03:00
model-optimizer [MO] Fix when Crop asks for MXNet specific cmdarg enable_ssd_gluoncv (#1978) 2020-08-31 18:06:13 +03:00
ngraph Dynamic shape cell (#1683) 2020-09-01 12:01:20 +03:00
openvino Add static library with nGraph reference implementations (#1810) 2020-08-17 19:43:11 +03:00
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Jenkinsfile [Jenkinsfile] Add failFast parameter (#721) 2020-06-02 20:22:25 +03:00
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OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository

Stable release Apache License Version 2.0

This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.

This open source version includes two components: namely Model Optimizer and Inference Engine, as well as CPU, GPU and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as Caffe*, TensorFlow*, MXNet* and ONNX*.

Repository components:

License

Deep Learning Deployment Toolkit is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.

Documentation

How to Contribute

See CONTRIBUTING for contribution to the code. See CONTRIBUTING_DOCS for contribution to the documentation. Thank you!

Support

Please report questions, issues and suggestions using:


* Other names and brands may be claimed as the property of others.