OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
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Mateusz Bencer c0d71900fd
Provide ONNX external data mechanism to ReadNetwork (#2588)
* added unit test

* added python test

* using pword approach

* Added passing path to onnx reader

* support for wstring

* Added more tests

* Apply suggestions from code review

Co-authored-by: Michał Karzyński <4430709+postrational@users.noreply.github.com>

* fix build for Windows

* styles applied

* Fixed Windows tests

* styles applied

* fixed styles in tests

* review remarks

* cmake order

* Used target_compile_definitions instead of add_definitions

* Move ONNX_TEST_MODELS to other scope

Co-authored-by: Michał Karzyński <4430709+postrational@users.noreply.github.com>
2020-10-14 12:30:53 +03:00
.ci Azure CI: Fix nGraph ONNX 2020-10-12 19:23:00 +03:00
.github GitHub CI: Add files_size.yml (#2570) 2020-10-13 13:27:34 +03:00
cmake used native LTO support from cmake 3.9.6 (#2625) 2020-10-14 10:43:24 +03:00
docs used native LTO support from cmake 3.9.6 (#2625) 2020-10-14 10:43:24 +03:00
inference-engine Provide ONNX external data mechanism to ReadNetwork (#2588) 2020-10-14 12:30:53 +03:00
model-optimizer [MO] [Kaldi] Add TDNN Component (#1870) 2020-10-12 14:10:27 +03:00
ngraph Provide ONNX external data mechanism to ReadNetwork (#2588) 2020-10-14 12:30:53 +03:00
openvino Bump cmake version to 3.13 (#2258) 2020-09-18 18:58:12 +03:00
scripts Shellcheck update (#2557) 2020-10-09 13:25:53 +03:00
tests Specify cl_cache_dir env var: (#2581) 2020-10-08 15:02:31 +03:00
tools Adds first inference time measurements in benchmark_app (#1487) 2020-07-27 16:45:07 +03:00
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get-started-linux.md Separate MO configuration for TensorFlow 2 model conversion (#1685) 2020-08-11 18:02:05 +03:00
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README.md doc: add openvino tag link on StackOverflow (#2585) 2020-10-08 16:17:30 +03:00
SECURITY.md Fix link in SECURITY.md (#2259) 2020-09-21 21:35:24 +03:00

OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository

Stable release Apache License Version 2.0 Azure DevOps builds (branch)

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.