OpenVINO™ Toolkit repository

Overview

OpenVINO™ Toolkit

Stable release Apache License Version 2.0 GitHub branch checks state 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 several components: namely Model Optimizer, nGraph and Inference Engine, as well as CPU, GPU, MYRIAD, multi device 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.

Resources:

Support

Please report questions, issues and suggestions using:


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

Issues
  • Bump wheel from 0.36.2 to 0.37.0 in /ngraph/python

    Bump wheel from 0.36.2 to 0.37.0 in /ngraph/python

    ⚠️ Dependabot is rebasing this PR ⚠️

    Rebasing might not happen immediately, so don't worry if this takes some time.

    Note: if you make any changes to this PR yourself, they will take precedence over the rebase.


    Bumps wheel from 0.36.2 to 0.37.0.

    Changelog

    Sourced from wheel's changelog.

    Release Notes

    UNRELEASED

    • Fixed wheel pack duplicating the WHEEL contents when the build number has changed (#415)

    0.37.0 (2021-08-09)

    • Added official Python 3.10 support
    • Updated vendored packaging library to v20.9

    0.36.2 (2020-12-13)

    • Updated vendored packaging library to v20.8
    • Fixed wheel sdist missing LICENSE.txt
    • Don't use default macos/arm64 deployment target in calculating the platform tag for fat binaries (PR by Ronald Oussoren)

    0.36.1 (2020-12-04)

    • Fixed AssertionError when MACOSX_DEPLOYMENT_TARGET was set to 11 (PR by Grzegorz Bokota and François-Xavier Coudert)
    • Fixed regression introduced in 0.36.0 on Python 2.7 when a custom generator name was passed as unicode (Scikit-build) (TypeError: 'unicode' does not have the buffer interface)

    0.36.0 (2020-12-01)

    • Added official Python 3.9 support
    • Updated vendored packaging library to v20.7
    • Switched to always using LF as line separator when generating WHEEL files (on Windows, CRLF was being used instead)
    • The ABI tag is taken from the sysconfig SOABI value. On PyPy the SOABI value is pypy37-pp73 which is not compliant with PEP 3149, as it should have both the API tag and the platform tag. This change future-proofs any change in PyPy's SOABI tag to make sure only the ABI tag is used by wheel.
    • Fixed regression and test for bdist_wheel --plat-name. It was ignored for C extensions in v0.35, but the regression was not detected by tests.

    0.35.1 (2020-08-14)

    • Replaced install dependency on packaging with a vendored copy of its tags module
    • Fixed bdist_wheel not working on FreeBSD due to mismatching platform tag name (it was not being converted to lowercase)

    0.35.0 (2020-08-13)

    • Switched to the packaging_ library for computing wheel tags

    ... (truncated)

    Commits
    • b8c4aa0 Created a new release
    • 88320ee Updated the flake8 action to the latest version
    • 57cec4e Readded macOS 11 to the testing matrix
    • 82758ff Added classifier for Python 3.10 support
    • d746ddc Added Python 3.10 to the testing matrix (#414)
    • 0b29b69 Test using FreeBSD 13.0 (#405)
    • e197cf3 Fix typo of comments (#404)
    • 6e86e6b Readded codecov reporting (#402)
    • 41b375e Removed MacOS 11.0 from the test matrix
    • a6fa483 Updated vendored packaging to v20.9
    • Additional commits viewable in compare view

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    ExternalPR category: dependencies 
    opened by dependabot[bot] 308
  • Bump flake8-quotes from 3.2.0 to 3.3.0 in /ngraph/python

    Bump flake8-quotes from 3.2.0 to 3.3.0 in /ngraph/python

    Bumps flake8-quotes from 3.2.0 to 3.3.0.

    Commits

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    ExternalPR category: dependencies 
    opened by dependabot[bot] 307
  • Bump tox from 3.23.1 to 3.24.1 in /ngraph/python

    Bump tox from 3.23.1 to 3.24.1 in /ngraph/python

    Bumps tox from 3.23.1 to 3.24.1.

    Changelog

    Sourced from tox's changelog.

    v3.24.1 (2021-07-31)

    Bugfixes ^^^^^^^^

    • get_requires_for_build_sdist hook (PEP 517) is assumed to return an empty list if left unimplemented by the backend build system - by :user:oczkoisse [#2130](https://github.com/tox-dev/tox/issues/2130) <https://github.com/tox-dev/tox/issues/2130>_

    Documentation ^^^^^^^^^^^^^

    • The documentation of install_command now also mentions that you can provide arbitrary commands - by :user:jugmac00 [#2081](https://github.com/tox-dev/tox/issues/2081) <https://github.com/tox-dev/tox/issues/2081>_

    v3.24.0 (2021-07-14)

    Bugfixes ^^^^^^^^

    • --devenv no longer modifies the directory in which the .tox environment is provisioned - by :user:isaac-ped [#2065](https://github.com/tox-dev/tox/issues/2065) <https://github.com/tox-dev/tox/issues/2065>_
    • Fix show config when the package names are not in canonical form - by :user:gaborbernat. [#2103](https://github.com/tox-dev/tox/issues/2103) <https://github.com/tox-dev/tox/issues/2103>_

    Documentation ^^^^^^^^^^^^^

    • Extended environment variables section - by :user:majiang [#2036](https://github.com/tox-dev/tox/issues/2036) <https://github.com/tox-dev/tox/issues/2036>_

    Miscellaneous ^^^^^^^^^^^^^

    • tox no longer shows deprecation warnings for distutils.sysconfig on Python 3.10 - by :user:9999years [#2100](https://github.com/tox-dev/tox/issues/2100) <https://github.com/tox-dev/tox/issues/2100>_
    Commits

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    ExternalPR category: dependencies 
    opened by dependabot[bot] 307
  • Bump mypy from 0.812 to 0.910 in /ngraph/python

    Bump mypy from 0.812 to 0.910 in /ngraph/python

    Bumps mypy from 0.812 to 0.910.

    Commits
    • f5fc579 Bump version to 0.910
    • 46ce325 Fix crash when inferring multiple assignment with overloaded function (#10689)
    • 96366d1 Don't ask to install a stub package if stubs are installed (#10670)
    • 9637f99 Fix crash with assignment to variable guarded with TypeGuard (#10683)
    • 680fded Document --install-types --non-interactive (#10684)
    • 64deb99 pkg_resources is now in types-setuptools (#10681)
    • e8cf526 Run build a second time when using --install-types --non-interactive (#10669)
    • f5a3405 Improve error reporting when --install-types has no cache (#10667)
    • 0b65866 pyopenssl types package name was corrected (#10656)
    • 4905aad Don't suggest to install stubs with py.typed files (#10652)
    • Additional commits viewable in compare view

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    category: Core ExternalPR category: dependencies 
    opened by dependabot[bot] 307
  • Bump tox from 3.23.1 to 3.24.3 in /ngraph/python

    Bump tox from 3.23.1 to 3.24.3 in /ngraph/python

    Bumps tox from 3.23.1 to 3.24.3.

    Changelog

    Sourced from tox's changelog.

    v3.24.3 (2021-08-21) Bugfixes ^^^^^^^^

    • --parallel reports now show ASCII OK/FAIL/SKIP lines when full Unicode output is not available - by :user:brettcs [#1421](https://github.com/tox-dev/tox/issues/1421) <https://github.com/tox-dev/tox/issues/1421>_

    Miscellaneous ^^^^^^^^^^^^^

    • Started enforcing valid references in Sphinx docs -- :user:webknjaz [#2168](https://github.com/tox-dev/tox/issues/2168) <https://github.com/tox-dev/tox/issues/2168>_

    v3.24.2 (2021-08-18)

    Bugfixes ^^^^^^^^

    • include LC_ALL to implicit list of passenv variables - by :user:ssbarnea [#2162](https://github.com/tox-dev/tox/issues/2162) <https://github.com/tox-dev/tox/issues/2162>_

    v3.24.1 (2021-07-31)

    Bugfixes ^^^^^^^^

    • get_requires_for_build_sdist hook (PEP 517) is assumed to return an empty list if left unimplemented by the backend build system - by :user:oczkoisse [#2130](https://github.com/tox-dev/tox/issues/2130) <https://github.com/tox-dev/tox/issues/2130>_

    Documentation ^^^^^^^^^^^^^

    • The documentation of install_command now also mentions that you can provide arbitrary commands - by :user:jugmac00 [#2081](https://github.com/tox-dev/tox/issues/2081) <https://github.com/tox-dev/tox/issues/2081>_

    v3.24.0 (2021-07-14)

    Bugfixes ^^^^^^^^

    • --devenv no longer modifies the directory in which the .tox environment is provisioned - by :user:isaac-ped [#2065](https://github.com/tox-dev/tox/issues/2065) <https://github.com/tox-dev/tox/issues/2065>_

    ... (truncated)

    Commits

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    ExternalPR category: dependencies 
    opened by dependabot[bot] 305
  • Bump wheel from 0.36.2 to 0.37.0 in /runtime/bindings/python

    Bump wheel from 0.36.2 to 0.37.0 in /runtime/bindings/python

    Bumps wheel from 0.36.2 to 0.37.0.

    Changelog

    Sourced from wheel's changelog.

    Release Notes

    UNRELEASED

    • Fixed wheel pack duplicating the WHEEL contents when the build number has changed (#415)

    0.37.0 (2021-08-09)

    • Added official Python 3.10 support
    • Updated vendored packaging library to v20.9

    0.36.2 (2020-12-13)

    • Updated vendored packaging library to v20.8
    • Fixed wheel sdist missing LICENSE.txt
    • Don't use default macos/arm64 deployment target in calculating the platform tag for fat binaries (PR by Ronald Oussoren)

    0.36.1 (2020-12-04)

    • Fixed AssertionError when MACOSX_DEPLOYMENT_TARGET was set to 11 (PR by Grzegorz Bokota and François-Xavier Coudert)
    • Fixed regression introduced in 0.36.0 on Python 2.7 when a custom generator name was passed as unicode (Scikit-build) (TypeError: 'unicode' does not have the buffer interface)

    0.36.0 (2020-12-01)

    • Added official Python 3.9 support
    • Updated vendored packaging library to v20.7
    • Switched to always using LF as line separator when generating WHEEL files (on Windows, CRLF was being used instead)
    • The ABI tag is taken from the sysconfig SOABI value. On PyPy the SOABI value is pypy37-pp73 which is not compliant with PEP 3149, as it should have both the API tag and the platform tag. This change future-proofs any change in PyPy's SOABI tag to make sure only the ABI tag is used by wheel.
    • Fixed regression and test for bdist_wheel --plat-name. It was ignored for C extensions in v0.35, but the regression was not detected by tests.

    0.35.1 (2020-08-14)

    • Replaced install dependency on packaging with a vendored copy of its tags module
    • Fixed bdist_wheel not working on FreeBSD due to mismatching platform tag name (it was not being converted to lowercase)

    0.35.0 (2020-08-13)

    • Switched to the packaging_ library for computing wheel tags

    ... (truncated)

    Commits
    • b8c4aa0 Created a new release
    • 88320ee Updated the flake8 action to the latest version
    • 57cec4e Readded macOS 11 to the testing matrix
    • 82758ff Added classifier for Python 3.10 support
    • d746ddc Added Python 3.10 to the testing matrix (#414)
    • 0b29b69 Test using FreeBSD 13.0 (#405)
    • e197cf3 Fix typo of comments (#404)
    • 6e86e6b Readded codecov reporting (#402)
    • 41b375e Removed MacOS 11.0 from the test matrix
    • a6fa483 Updated vendored packaging to v20.9
    • Additional commits viewable in compare view

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    ExternalPR category: dependencies 
    opened by dependabot[bot] 118
  • Bump onnx from 1.10.1 to 1.10.2 in /runtime/bindings/python

    Bump onnx from 1.10.1 to 1.10.2 in /runtime/bindings/python

    ⚠️ Dependabot is rebasing this PR ⚠️

    Rebasing might not happen immediately, so don't worry if this takes some time.

    Note: if you make any changes to this PR yourself, they will take precedence over the rebase.


    Bumps onnx from 1.10.1 to 1.10.2.

    Release notes

    Sourced from onnx's releases.

    v1.10.2

    ONNX v1.10.2 is a patch release based on v1.10.1.

    Bug fixes:

    • Fix compilation error on older compilers (#3683)
    • Stricter check for Shape's input: check input type (#3757)
    Commits

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    ExternalPR category: dependencies 
    opened by dependabot[bot] 117
  • Bump flake8-quotes from 3.2.0 to 3.3.1 in /runtime/bindings/python

    Bump flake8-quotes from 3.2.0 to 3.3.1 in /runtime/bindings/python

    ⚠️ Dependabot is rebasing this PR ⚠️

    Rebasing might not happen immediately, so don't worry if this takes some time.

    Note: if you make any changes to this PR yourself, they will take precedence over the rebase.


    Bumps flake8-quotes from 3.2.0 to 3.3.1.

    Commits
    • ef0d9a9 Release 3.3.1
    • 9d2a722 Updated setup.py classifiers as per #104
    • e3ef262 Use assertRegex instead of assertRegexpMatches for Python 3.11 compatibility....
    • 19e05d5 Release 3.3.0
    • 07f161c Make the error message show what kind of string is bad. (#103)
    • See full diff in compare view

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    ExternalPR category: dependencies 
    opened by dependabot[bot] 117
  • Bump mypy from 0.812 to 0.910 in /runtime/bindings/python

    Bump mypy from 0.812 to 0.910 in /runtime/bindings/python

    Bumps mypy from 0.812 to 0.910.

    Commits
    • f5fc579 Bump version to 0.910
    • 46ce325 Fix crash when inferring multiple assignment with overloaded function (#10689)
    • 96366d1 Don't ask to install a stub package if stubs are installed (#10670)
    • 9637f99 Fix crash with assignment to variable guarded with TypeGuard (#10683)
    • 680fded Document --install-types --non-interactive (#10684)
    • 64deb99 pkg_resources is now in types-setuptools (#10681)
    • e8cf526 Run build a second time when using --install-types --non-interactive (#10669)
    • f5a3405 Improve error reporting when --install-types has no cache (#10667)
    • 0b65866 pyopenssl types package name was corrected (#10656)
    • 4905aad Don't suggest to install stubs with py.typed files (#10652)
    • Additional commits viewable in compare view

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    ExternalPR category: dependencies 
    opened by dependabot[bot] 117
  • Bump flake8-quotes from 3.2.0 to 3.3.0 in /runtime/bindings/python

    Bump flake8-quotes from 3.2.0 to 3.3.0 in /runtime/bindings/python

    Bumps flake8-quotes from 3.2.0 to 3.3.0.

    Commits

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    ExternalPR category: dependencies 
    opened by dependabot[bot] 117
  • Bump tox from 3.23.1 to 3.24.4 in /runtime/bindings/python

    Bump tox from 3.23.1 to 3.24.4 in /runtime/bindings/python

    ⚠️ Dependabot is rebasing this PR ⚠️

    Rebasing might not happen immediately, so don't worry if this takes some time.

    Note: if you make any changes to this PR yourself, they will take precedence over the rebase.


    Bumps tox from 3.23.1 to 3.24.4.

    Changelog

    Sourced from tox's changelog.

    v3.24.4 (2021-09-16)

    Bugfixes ^^^^^^^^

    • Fixed handling of -e ALL in parallel mode by ignoring the ALL in subprocesses -- by :user:guahki. [#2167](https://github.com/tox-dev/tox/issues/2167) <https://github.com/tox-dev/tox/issues/2167>_
    • Prevent tox from using a truncated interpreter when using TOX_LIMITED_SHEBANG -- by :user:jdknight. [#2208](https://github.com/tox-dev/tox/issues/2208) <https://github.com/tox-dev/tox/issues/2208>_

    Documentation ^^^^^^^^^^^^^

    • Enabled the use of the favicon in the Sphinx docs first introduced in :pull:764 but not integrated fully -- :user:webknjaz [#2177](https://github.com/tox-dev/tox/issues/2177) <https://github.com/tox-dev/tox/issues/2177>_

    v3.24.3 (2021-08-21)

    Bugfixes ^^^^^^^^

    • --parallel reports now show ASCII OK/FAIL/SKIP lines when full Unicode output is not available - by :user:brettcs [#1421](https://github.com/tox-dev/tox/issues/1421) <https://github.com/tox-dev/tox/issues/1421>_

    Miscellaneous ^^^^^^^^^^^^^

    • Started enforcing valid references in Sphinx docs -- :user:webknjaz [#2168](https://github.com/tox-dev/tox/issues/2168) <https://github.com/tox-dev/tox/issues/2168>_

    v3.24.2 (2021-08-18)

    Bugfixes ^^^^^^^^

    • include LC_ALL to implicit list of passenv variables - by :user:ssbarnea [#2162](https://github.com/tox-dev/tox/issues/2162) <https://github.com/tox-dev/tox/issues/2162>_

    v3.24.1 (2021-07-31)

    ... (truncated)

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    ExternalPR category: dependencies 
    opened by dependabot[bot] 116
  • [Bug]

    [Bug]

    System information (version)
    • OpenVINO=> 2022.1.0
    • Operating System / Platform => Ubuntu 20.04.4 LTS
    • Compiler => gcc 9.4.0
    • Problem classification => Model loading
    Detailed description

    I have a strange behavior when I try to load a model several times (refer to the code snippet below). We obtain

    malloc(): invalid size (unsorted) malloc(): invalid size (unsorted) malloc(): invalid size (unsorted) Aborted (core dumped)

    I am aware that it is not a normal use case but we are required to run several model loading process during our code testing. The code shown below is just to show you that when loading a model multiple times in the same program, it crashes.

    Also, it only does it when using "MULTI:GPU,CPU". GPU only and CPU only works fine.

    Steps to reproduce

    Using any model (I chose GoogleNet-v1 FP16 with the model downloader), run the following code:

    #include "openvino/openvino.hpp"
    #include <chrono>
    #include <iostream>
    #include <thread>
    int main(int argc, char* argv[])
    {    
        for (size_t i = 0; i < 4; i++)
        {
            ov::Core core;
            std::shared_ptr<ov::Model> model = core.read_model(PATH_TO_MODEL);
            ov::CompiledModel compiled_model = core.compile_model(model, "MULTI:GPU,CPU");
            
            model = {};
            compiled_model = {};
        }
    
        return 0;
    }
    
    
    Issue submission checklist
    • [x] I report the issue, it's not a question
    • [x] I checked the problem with documentation, FAQ, open issues, Stack Overflow, etc and have not found solution
    • [x] There is reproducer code and related data files: images, videos, models, etc.
    category: MULTI support_request 
    opened by svutran1 1
Releases(2022.1.0)
  • 2022.1.0(Mar 22, 2022)

    Major Features and Improvements Summary

    This release is the biggest upgrade in 3.5 years! Read the release notes below for a summary of changes.

    2022.1 release provides functional bug fixes, and capability changes for the previous 2021.4.2 LTS release. This new release empowers developers with new performance enhancements, more deep learning models, more device portability, and higher inferencing performance with fewer code changes.

    Note: This is a standard release intended for developers that prefer the very latest features and leading performance. Standard releases will continue to be made available three to four times a year. Long Term Support (LTS) releases are also available. A new LTS version is released every year and is supported for 2 years (1 year of bug fixes, and 2 years for security patches). ead Intel® Distribution of OpenVINO™ toolkit Long-Term Support (LTS) Policy to get details. Latest LTS releases: 2020.x LTS and 2021.x LTS.

    • Updated, cleaner API:

      • New OpenVINO API 2.0 was introduced. The API aligns OpenVINO inputs/outputs with frameworks. Input and output tensors use native framework layouts and element types. Old Inference Engine and nGraph APIs are available but will be deprecated in a future release down the road.

      • inference_engine, inference_engine_transformations, inferencengine_lp_transformations and ngraph libraries were merged to common openvino library. Other libraries were renamed. Please, use common ov:: namespace inside all OpenVINO components. See how to implement Inference Pipeline using OpenVINO API v2.0 for details.

      • Model Optimizer’s API parameters have been reduced to minimize complexity. Performance has been significantly improved for model conversion on ONNX models.

      • It’s highly recommended to migrate to API 2.0 because it already has additional features and this list will be extended later. The following list of additional features is supported by API 2.0:

        • Working with dynamic shapes. The feature is quite useful for best performance for Neural Language Processing (NLP) models, super-resolution models, and other which accepts dynamic input shapes. Note: Models compiled with dynamic shapes may show reduced performance and consume more memory than models configured with a static shape on the same input tensor size. Setting upper bounds to reshape the model for dynamic shapes or splitting the input into several parts is recommended.

        • Preprocessing of the model to add preprocessing operations to the inference models and fully occupy the accelerator and free CPU resources.

      • Read the Transition Guide for migrating to the new API 2.0.

    • Portability and Performance:

      • New AUTO plugin self-discovers available system inferencing capacity based on model requirements, so applications no longer need to know its compute environment in advance.

      • The OpenVINO™ performance hints are the new way to configure the performance with portability in mind. The hints “reverse” the direction of the configuration in the right fashion: rather than map the application needs to the low-level performance settings, and keep an associated application logic to configure each possible device separately, the idea is to express a target scenario with a single config key and let the device to configure itself in response. As the hints are supported by every OpenVINO™ device, this is a completely portable and future-proof solution.

      • Automatic batching functionality via code hints automatically scale batch size based on XPU and available memory.

    • Broader Model Support:

      • With Dynamic Input Shapes capabilities on CPU, OpenVINO will be able to adapt to multiple input dimensions in a single model providing more complete NLP support. Dynamic Shapes support on additional XPUs expected in a future dot release.
    • New Models with focus on NLP and a new category, Anomaly detection, and support for conversion and inference of select PaddlePaddle models:

      • Pre-trained Models: Anomaly segmentation focus on industrial inspection making Speech denoising trainable plus updates on speech recognition and speech synthesis

      • Combined Demo: Noise reduction + speech recognition + question answering + translation+ text to speech

      • Public Models: Focus on NLP ContextNet, Speech-Transformer, HiFi-GAN, Glow-TTS, FastSpeech2, and Wav2Vec

    • Built with 12th Gen Intel® Core™ 'Alder Lake' in mind. Supports the hybrid architecture to deliver enhancements for high-performance inferencing on CPU & integrated GPU

    You can find OpenVINO™ toolkit 2022.1 release here:

    Release documentation is available here: https://docs.openvino.ai/2022.1/

    Release Notes are available here: https://www.intel.com/content/www/us/en/developer/articles/release-notes/openvino-relnotes.html

    Source code(tar.gz)
    Source code(zip)
  • 2022.1.0.dev20220316(Mar 16, 2022)

    OpenVINO™ toolkit pre-release definition:

    • It is introduced to get early feedback from the community.
    • The scope and functionality of the pre-release version is subject to change in the future.
    • Using the pre-release in production is strongly discouraged.

    You can find OpenVINO™ toolkit 2022.1.0.dev20220316 pre-release version here:

    Release documentation is available here: https://docs.openvino.ai/nightly/

    * - sha256 sums for archives

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    cb5be734f7cf2ea9a48ead91313f19edc513be982a5a90bf967d334ac5700d2b  openvino_opencv_windows.tgz
    3c2d9defba4db131c4d023bf2df171a5de5a335147d53db6c4c1805ea5da8466  openvino_opencv_ubuntu20.tgz
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    8cfc8d9e39b2e9b91d4b6faa1bbe561d21a9524b748faf1bd0c9285093fb8363  openvino_opencv_osx.tgz
    

    NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.

    Source code(tar.gz)
    Source code(zip)
  • 2022.1.0.dev20220302(Mar 4, 2022)

    OpenVINO™ toolkit pre-release definition:

    • It is introduced to get early feedback from the community.
    • The scope and functionality of the pre-release version is subject to change in the future.
    • Using the pre-release in production is strongly discouraged.

    You can find OpenVINO™ toolkit 2022.1.0.dev20220302 pre-release version here:

    Release documentation is available here: https://docs.openvino.ai/nightly/

    * - sha256 sums for archives

    78f42300b84b66db551bf650a122f7f793d4c1bdf57fa6ac7e7a2ef1eb19a897  openvino_windows_dev_2022.1.0-6935-7cd3c8e86e9.zip
    4c070ea22816d852a9335249a27562b2d85bf54fd8358a674d6f895c022136bc  openvino_ubuntu20_dev_2022.1.0-6935-7cd3c8e86e9.tgz
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    7f750e07c7a5e5e6ab7d1fd1aa113b059a39793e094da8aa5a984c29f7060dc7  openvino_rhel8_dev_2022.1.0-6935-7cd3c8e86e9.tgz
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    ba26dfa5d81eb31c42ec8b8a55ba5e7c6dc225ef05aef8c3dd82a814138fa4bd  openvino_opencv_windows.tgz
    29c5ff6e9a3a840642aee76308cff37df48fcafa197fe5acd8bd6cfa11197e7f  openvino_opencv_ubuntu20.tgz
    4b0d4aecf9cf0957380bd84a907e033294835d176933b164c47bc95f8862b0b3  openvino_opencv_ubuntu18.tgz
    4d9337347f30eb5e865ec5059485c766bfc8bdfcc5329bedd0959fb106efdbe1  openvino_opencv_osx.tgz
    

    NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.

    Source code(tar.gz)
    Source code(zip)
  • 2022.1.0.dev20220215(Feb 17, 2022)

    OpenVINO™ toolkit pre-release definition:

    • It is introduced to get early feedback from the community.
    • The scope and functionality of the pre-release version is subject to change in the future.
    • Using the pre-release in production is strongly discouraged.

    You can find OpenVINO™ toolkit 2022.1.0.dev20220215 pre-release version here:

    * - sha256 sums for archives

    76d21c7f1a6abcf3c3e6b3bfaab7e1dbe11d45de035bf906188ef926221d15c4  l_openvino_toolkit_rhel8_dev_2022.1.0.dev20220215.tgz
    6bc9bf7b3a417798d6649b68b8054fcf70be7ddf39f3cf27301dbdedd2f38c1d  w_openvino_toolkit_windows_dev_2022.1.0.dev20220215.zip
    47eaaeb8e73329f5d245df21493a4ac04a27287c6124348fc395f61f7dea9d93  m_openvino_toolkit_osx_dev_2022.1.0.dev20220215.tgz
    8e66c9f7edc1d619c702a08f448fd4773667478dfabed3d34b43f3d94b1c1722  l_openvino_toolkit_ubuntu20_dev_2022.1.0.dev20220215.tgz
    a45e1d387a04b306ebb115f3f1480091f62bebb7fb75d70f9baf3af03f659d03  l_openvino_toolkit_ubuntu18_dev_2022.1.0.dev20220215.tgz
    d0c9051e1ac7db174cfd357f79129a1c8ec7879140532a7ac7267cfdf5227d13  openvino_opencv_windows.tgz
    dac828f564221f4b0f785168556c6e61b5a9210143ad86fbe01b8172d5db862a  openvino_opencv_osx.tgz
    587b27a429583510f6d74633eaf358dea2d4d6e963ff8e2c22a6aad8a946ddbc  openvino_opencv_ubuntu20.tgz
    169efcdc21317177e5d8d747c4cbbd50f92ef7fce93999d7207b8e48918d2b81  openvino_opencv_ubuntu18.tgz
    

    NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.

    Source code(tar.gz)
    Source code(zip)
  • 2022.1.0.dev20220131(Feb 1, 2022)

    OpenVINO™ toolkit pre-release definition:

    • It is introduced to get early feedback from the community.
    • The scope and functionality of the pre-release version is subject to change in the future.
    • Using the pre-release in production is strongly discouraged.

    You can find OpenVINO™ toolkit 2022.1.0.dev20220131 pre-release version here:

    * - sha256 sums for archives

    569762a88bb914236f772a0d545520f4b8704e73594097ab2d5ae724e81a1673  l_openvino_toolkit_dev_rhel8_p_2022.1.0.dev20220131.tgz
    c9b3c5471af91d2e8ddaade78b96f90ee38f89ddf224f4601e45d170854a4f7e  l_openvino_toolkit_dev_ubuntu18_p_2022.1.0.dev20220131.tgz
    15802037618d784ac9d0646be6d4c3be1d2538819cdf7d992b04ce62e0458bcd  l_openvino_toolkit_dev_ubuntu20_p_2022.1.0.dev20220131.tgz
    6b10269e60c208f81d1dfafd01756762fe95b1ebd3c0e35b6ed028b9d6a0ae0a  m_openvino_toolkit_dev_p_2022.1.0.dev20220131.tgz
    0bc02727d952f3ac47e4528da4b87dba22cbc070775cf055b3485fd870ae18da  w_openvino_toolkit_dev_p_2022.1.0.dev20220131.zip
    fe2cd7aba2046c8eed25150cdc10cffce807f0e4231f7774f97e8488a57c2300  opencv_osx.tgz
    3b0eaab3209a1e91085b71d5ce12a51747c07f63ec2a6cf471bb9f8e016eab99  opencv_ubuntu18_rhel8.tgz
    1d73d11d7d651c8959c012800fa9ae4a6f9f2b550622030fe41b05892a2327cc  opencv_ubuntu20.tgz
    7d04b788d139f1ab198d80e10aac974089bf2a2c734e7cf2c3c70036bded2058  opencv_windows.tgz
    

    NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.

    Source code(tar.gz)
    Source code(zip)
  • 2021.4.2(Nov 16, 2021)

    Release Notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-2021-4-lts-relnotes.html

    This 2021.4.2 LTS release provides functional bug fixes, and minor capability changes for the previous 2021.4.1 Long-Term Support (LTS) release, enabling developers to deploy applications powered by Intel® Distribution of OpenVINO™ toolkit with confidence. To learn more about long-term support and maintenance, go to the Long Term Support Policy.

    Source code(tar.gz)
    Source code(zip)
  • 2021.4.1(Sep 9, 2021)

    Release Notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-2021-4-lts-relnotes.html

    This 2021.4.1 LTS release provides functional bug fixes, and minor capability changes for the previous 2021.4 Long-Term Support (LTS) release, enabling developers to deploy applications powered by Intel® Distribution of OpenVINO™ toolkit with confidence. To learn more about long-term support and maintenance, go to the Long Term Support Policy.

    Source code(tar.gz)
    Source code(zip)
  • 2021.4(Jun 29, 2021)

    Release Notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-2021-4-lts-relnotes.html

    What's New

    • This new 2021.4 Long-Term Support (LTS) Release provides bug fixes, longer-term maintenance and support with a focus on stability and compatibility enabling developers to deploy applications powered by Intel® Distribution of OpenVINO™ toolkit with confidence. A new LTS version is released every year and supported for two years. For those developers that prefer the very latest features and leading performance, standard releases will continue to be made available 3-4 times a year. Read more about the long-term support and maintenance, go to the Long Term Support Policy.
    • New Jupyter Notebooks, demos and support for additional public models to make development easier:
      • Ready-to-run Jupyter Notebooks with tutorials for converting TensorFlow and PyTorch models, image classification, segmentation, depth estimation, post-training quantization and more.
      • Audio Noise Suppression & Time Series Forecasting demos
      • Public Models: RCAN and IseeBetter (image super-resolution), Attention OCR (image text prediction), Tacotron 2 (text-to-speech) and ModNet (portrait/image matting)
    • Time-to-first-inference latency performance enhancements: Initialization has been optimized on CPU and integrated GPU (iGPU), significantly improving performance at inferencing startup. Setting up inferencing always involves additional initialization time as the network is loaded and configured on the device, especially on GPUs due to their architecture. This setup time has been reduced significantly for many networks by doing more initialization work in parallel among other optimizations.
    • Preview of OpenVINO ™ integration with TensorFlow: Although not a part of the 2021.4 LTS release, a new open source component called the OpenVINO™ integration with TensorFlow is available as a public preview. This component is designed for TensorFlow developers newly exploring OpenVINO™ toolkit to try it with minimal code changes, maximizing TensorFlow API compatibility. For highest performance, lowest memory footprint and complete hardware control, adopting native OpenVINO APIs continues to be the recommended approach.
    Source code(tar.gz)
    Source code(zip)
  • 2020.3.2(Apr 16, 2021)

    Release notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-2020-3-lts-relnotes.html

    This release provides bug fixes for the previous 2020.3 Long-Term Support (LTS) release, a new release type that provides longer-term maintenance and support with a focus on stability and compatibility. Read more about the support details: Long Term Support Release

    Source code(tar.gz)
    Source code(zip)
  • 2021.3(Mar 23, 2021)

    Release Notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html

    What's New

    • Upgrade to the latest version for new capabilities and performance improvements.
    • Introduces a preview of Conditional Compilation (available in open-source distribution) which enables a significant reduction to the binary footprint of the runtime components (Inference Engine linked into applications) for particular models.
    • Introducing support for the 3rd Gen Intel® Xeon® Scalable platform (code-named Ice Lake), which delivers advanced performance, security, efficiency, and built-in AI acceleration to handle unique workloads and more powerful AI.
    • New pre-trained models and support for public models to streamline development:
      • Pre-trained Models: machine-translation, person-vehicle-bike-detection, text-recognition and text-to-speech.
      • Public Models: aclnet-int8 (sound_classification), deblurgan-v2 (image_processing), fastseg-small and fastseg-large (semantic segmentation) and more.
    • Developer tools now available as Python wheel packages for Windows*, Linux*, and macOS* for easy package installation and upgrades (pip install openvino-dev)
    Source code(tar.gz)
    Source code(zip)
  • 2021.2(Dec 15, 2020)

    Release Notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html

    What's New

    • Integrates the Deep Learning Workbench with the Intel® DevCloud for the Edge as a Beta release. Graphically analyze models using the Deep Learning Workbench on the Intel® DevCloud for the Edge (instead of a local machine only) to compare, visualize and fine-tune a solution against multiple remote hardware configurations.
    • Introduces support for Red Hat Enterprise Linux (RHEL) 8.2.
    • Introduces per-channel quantization support in the Model Optimizer for models quantized with TensorFlow Quantization-Aware Training containing per-channel quantization for weights, which improves performance by model compression and latency reduction.
    • Pre-trained models and support for public models to streamline development:
      • Public Models: Yolov4 (for object detection), AISpeech (for speech recognition), and DeepLabv3 (for semantic segmentation)
      • Pre-trained Models: Human Pose Estimation (update), Formula Recognition Polynomial Handwritten (new), Machine Translation (update), Common Sign Language Recognition (New), and Text-to-Speech (new)
    • New OpenVINO™ Security Add-on, which controls access to model(s) through secure packaging and execution. Based on KVM Virtual machines and Docker* containers and compatible with the OpenVINO™ Model Server, this new add-on enables packaging for flexible deployment and controlled model access.
    • PyPI project moved from openvino-python to openvino, and 2021.1 version to be removed in the default view. The specific version is still available for users depending on this exact version by using openvino-python==2021.1
    Source code(tar.gz)
    Source code(zip)
  • 2020.3.1(Nov 12, 2020)

    Release notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-2020-3-lts-relnotes.html

    What's New

    • This release provides bug fixes for the previous 2020.3 Long-Term Support (LTS) release, a new release type that provides longer-term maintenance and support with a focus on stability and compatibility. Read more about the support details: Long Term Support Release
    • Based on v.2020.3 LTS, the v.2020.3.1 LTS release includes security and functionality bug fixes, and minor capability changes.
    • Includes improved support for 11th Generation Intel® Core™ Processor (formerly codenamed Tiger Lake), which includes Intel® Iris® Xe Graphics and Intel® DL Boost instructions.
    • Intel® Distribution of OpenVINO™ toolkit 2020.3.X LTS releases will continue to support Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA and the Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA. For questions about next-generation programmable deep-learning solutions based on FPGAs, talk to your sales representative or contact us to get the latest FPGA updates.
    Source code(tar.gz)
    Source code(zip)
  • 2021.1(Oct 6, 2020)

    Release Notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html

    What's New

    • Introducing a major release in October 2020 (v.2021). You are highly encouraged to upgrade to this version because there it introduces new and important capabilities, as well as breaking changes and backward-incompatible changes.
    • Support for TensorFlow 2.2.x. Introduces official support for models trained in the TensorFlow 2.2.x framework.
    • Support for the Latest Hardware. Introduces official support for 11th Generation Intel® Core™ Processor Family for Internet of Things (IoT) Applications (formerly codenamed Tiger Lake) including new inference performance enhancements with Iris® Xe Graphics and Intel® DL Boost instructions, as well as Intel® Gaussian & Neural Accelerators 2.0 for low-power speech processing acceleration.
    • Going Beyond Vision. Enables end-to-end capabilities to leverage the Intel® Distribution of OpenVINO™ toolkit for workloads beyond computer vision, which include audio, speech, language, and recommendation, with new pre-trained models, support for public models, code samples and demos, and support for non-vision workloads in OpenVINO™ toolkit DL Streamer.
    • Coming in Q4 2020: (Beta Release) Integration of DL Workbench and the Intel® DevCloud for the Edge. Developers can now graphically analyze models using the DL Workbench on Intel® DevCloud for the Edge (instead of a local machine only) to compare, visualize and fine-tune a solution against multiple remote hardware configurations.
    • OpenVINO™ Model Server. An add-on to the Intel® Distribution of OpenVINO™ toolkit and a scalable microservice, which provides a gRPC or HTTP/REST endpoint for inference, makes it easier to deploy models in cloud or edge server environments. It is now implemented in C++ to enable reduced container footprint (for example, less than 500MB) and deliver higher throughput and lower latency.
    • Now available through Gitee* and PyPI* distribution methods. You are encouraged to choose from the distribution methods and download.
    Source code(tar.gz)
    Source code(zip)
  • 2020.4(Jul 14, 2020)

    Release notes: https://software.intel.com/content/www/us/en/develop/articles/openvino-relnotes.html

    What's New

    • Improves performance while maintaining accuracy close to full precision (for example, FP32 data type) by introducing support for the Bfloat16 data type for inferencing using the 3rd generation Intel® Xeon® Scalable processor (formerly code-named Cooper Lake).
    • Increases accuracy when layers have varying bit-widths by extending the Post-Training Optimization Tool to support mixed-precision quantization.
    • Allows greater compatibility of models by supporting directly reading Open Neural Network Exchange (ONNX*) model format to the Inference Engine.
      • For users looking to take full advantage of Intel® Distribution of OpenVINO™ toolkit, it is recommended to follow the native workflow of using the Intermediate Representation from the Model Optimizer as input to the Inference Engine.
      • For users looking to more easily take a converted model in ONNX model format (for example, PyTorch to ONNX using torch.onnx), they are now able to input the ONNX format directly to the Inference Engine to run models on Intel architecture.
    • Enables initial support for TensorFlow* 2.2.0 for computer vision use cases.
    • Enables users to connect to and profile multiple remote hosts; collect and store data in one place for further analysis by extending the Deep Learning Workbench with remote profiling capability.
    Source code(tar.gz)
    Source code(zip)
  • 2020.3.0(Jun 3, 2020)

  • 2020.2(Apr 13, 2020)

  • 2020.1(Feb 12, 2020)

  • 2019_R3.1(Oct 28, 2019)

  • 2019_R3(Oct 9, 2019)

  • 2019_R2(Aug 9, 2019)

  • 2019_R1.1(May 27, 2019)

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