Release Notes for Intel® Distribution of OpenVINO™ Toolkit 2021

By Andrey Zaytsev, Alina Alborova

Published:10/06/2020   Last Updated:10/06/2020

Note For the Release Notes for the 2020 version, refer to Release Notes for Intel® Distribution of OpenVINO™ toolkit 2020


The Intel® Distribution of OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, and many others. Based on latest generations of artificial neural networks, including Convolutional Neural Networks (CNNs), recurrent and attention-based networks, the toolkit extends computer vision and non-vision workloads across Intel® hardware, maximizing performance. It accelerates applications with high-performance, AI and deep learning inference deployed from edge to cloud.

The Intel® Distribution of OpenVINO™ toolkit:

  • Enables deep learning inference from the edge to cloud.
  • Supports heterogeneous execution across Intel accelerators, using a common API for the Intel® CPU, Intel® Integrated Graphics, Intel® Gaussian & Neural Accelerator, Intel® Neural Compute Stick 2, Intel® Vision Accelerator Design with Intel® Movidius™ VPUs.
  • Speeds time-to-market through an easy-to-use library of CV functions and pre-optimized kernels.
  • Includes optimized calls for CV standards, including OpenCV* and OpenCL™.

New and Changed in the Release 1

Executive Summary

  • 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 pr-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 ServerAn 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. 

Backward Incompatible Changes Compared with 2020.4

  • List of Deprecated APIAPI Changes
  • IRv7 has been deprecated since 2020.3, and it is no longer supported in this release. You cannot read IRv7 and lower Core::ReadNetwork and are recommended to migrate to IRv10, the highest version. IRv10 provides a streamlined and future-ready operation set that is aligned with public frameworks along with better support for low precision models representation in order to keep accuracy when running in the quantized mode as well as the support for reshapeable models.
  • Removed the Inference Engine NNBuilder API. Use nGraph instead to create a CNN graph from C++ code.
  • Removed the following Inference Engine public API:
    • InferencePlugin, IInferencePlugin, and InferencEnginePluginPtr classes. Use the Core class instead.
    • PluginDispatcher class. Use the Core class instead.
    • CNNNetReader class. Use Core::ReadNetwork instead.
    • PrimitiveInfo, TensorInfo and ExecutableNetwork::GetMappedTopology. Use ExecutableNetwork::GetExecGraphInfo instead.
    • ICNNNetworkStats, NetworkNodeStats, CNNNetwork::getStats and CNNNetwork::setStat. Use IRv10 with FakeQuantize approach for INT8 flow replacement.
    • IShapeInferExtension and CNNNetwork::addExtension. Use IExtension class as a container for nGraph::Nodes which implement shape inference.
    • IEPlugin from the Inference Engine Python API. Use the Core API instead.
    • Data::getCreatorLayer, Data::getInputTo and CNNLayer. Use CNNNetwork::getFunction to iterate over a graph.
  • Starting with the OpenVINO™ toolkit 2020.2 release, all of the features previously available through nGraph have been merged into the OpenVINO™ toolkit. As a result, all the features previously available through the ONNX RT Execution Provider for nGraph have been merged with the ONNX RT Execution Provider for OpenVINO™ toolkit. Therefore, the ONNX RT Execution Provider for nGraph will be deprecated starting June 1, 2020 and will be completely removed on December 1, 2020. Migrate to the ONNX RT Execution Provider for the OpenVINO™ toolkit as the unified solution for all AI inferencing on Intel® hardware.
  • Deprecated or removed the following nGraph public API:
    • Removed all nGraph methods and classes, which have been deprecated in previous releases.
    • Removed the GetOutputElement operation.
    • Replaced copy_with_new_args() by clone_with_new_inputs().
    • Removed opset0 and back propagation operations.
    • Removed some operations from opset0, deprecated operations from the opset, which are not used in newer opsets.
    • Removed the support for the serialization nGraph function to the JSON format.
    • Deprecated FusedOp.
  • Changed the structure of the nGraph public API. Removed nGraph builders and reference implementations from nGraph public API. Joined subfolders that have fused and experimental operations with the common operation catalog.
  • Changed the System Requirements. Review the section below.
  • Intel® will be transitioning to the next-generation programmable deep-learning solution based on FPGAs in order to increase the level of customization possible in FPGA deep-learning. As a part of this transition, future standard, that is non-LTS, releases of the Intel® Distribution of OpenVINO™ toolkit will no longer include the Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA and the Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA. Intel® Distribution of OpenVINO™ toolkit 2020.3.X LTS release 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.

Model Optimizer

Model Optimizer

Common changes

  • Implemented several optimization transformations to replace sub-graphs of operations with HSwish, Mish, Swish and SoftPlus operations.
  • Model Optimizer generates IR keeping shape-calculating sub-graphs by default. Previously, this behavior was triggered if the "--keep_shape_ops" command line parameter was provided. The key is ignored in this release and will be deleted in the next release. To trigger the legacy behavior to generate an IR for a fixed input shape (folding ShapeOf operations and shape-calculating sub-graphs to Constant), use the "--static_shape" command line parameter. Changing model input shape using the Inference Engine API in runtime may fail for such an IR.
  • Fixed Model Optimizer conversion issues resulted in non-reshapeable IR using the Inference Engine reshape API.
  • Enabled transformations to fix non-reshapeable patterns in the original networks:
    • Hardcoded Reshape
      • In Reshape(2D)->MatMul pattern
      • Reshape->Transpose->Reshape when the pattern can be fused to the ShuffleChannels or DepthToSpace operation
    • Hardcoded Interpolate
      • In Interpolate->Concat pattern
  • Added a dedicated requirements file for TensorFlow 2.X as well as the dedicated install prerequisites scripts.
  • Replaced the SparseToDense operation with ScatterNDUpdate-4.


  • Enabled an ability to specify the model output tensor name using the "--output" command line parameter.
  • Added support for the following operations:
    • Acosh
    • Asinh
    • Atanh
    • DepthToSpace-11, 13
    • DequantizeLinear-10 (zero_point must be constant)
    • HardSigmoid-1,6
    • QuantizeLinear-10 (zero_point must be constant)
    • ReduceL1-11, 13
    • ReduceL2-11, 13
    • Resize-11, 13 (except mode="nearest" with 5D+ input, mode="tf_crop_and_resize", and attributes exclude_outside and extrapolation_value with non-zero values)
    • ScatterND-11, 13
    • SpaceToDepth-11, 13


  • Added support for the following operations:
    • Acosh
    • Asinh
    • Atanh
    • CTCLoss
    • EuclideanNorm
    • ExtractImagePatches
    • FloorDiv


  • Added support for the following operations:
    • Acosh
    • Asinh
    • Atanh


  • Fixed bug with ParallelComponent support. Now it is fully supported with no restrictions.

Inference Engine

Inference Engine

Common changes

  • Migrated to the Microsft Studio C++ (MSVC) 2019 Compiler as default for Windows, which enables you to 2.5x reduce the binary size of OpenVINO™ runtime. See Reduce Application Footprint with the Latest Features in Intel® Distribution of OpenVINO™ toolkit for details.
  • See the Deprecation messages and backward-incompatible changes compared with v.2020 Release 4 section for detailed changes in the API.
  • Ported the CPU-based preprocessing path, namely resizing for different numbers of channels, layout conversions, and color space conversions, to AVX2 and AVX512 instruction sets.

Inference Engine Python API

Inference Engine Python API

  • Enabled the nGraph Python API, which enables communicating with the nGraph function using Python. This enables performing analysis of the loaded graph.
  • Enabled setting parameters of the nodes for the graph. 
  • Enabled reading ONNX models with the Python API.

Inference Engine C API

Inference Engine C API

  • No changes

CPU Plugin

CPU Plugin

  • Improved performance of CPU plugin built with MSVC compiler to align with the version built with the Intel® compiler, which enables the use of MSVC as the default compiler for binary distribution on Windows. This change resulted in more than 2x binary size reduction for CPU plugin and other components. See Reduce Application Footprint with the Latest Features in Intel® Distribution of OpenVINO™ toolkit for details.
  • Added the support for new operations:
    • ScatterUpdate-3
    • ScatterElementsUpdate-3
    • ScatterNDUpdate-3
    • Interpolate-4
    • CTC-Loss-4
    • Mish-4
    • HSwish-4

GPU Plugin

GPU Plugin

  • Support for 11th Generation Intel® Core™ Processor Family for Internet of Things (IoT) Applications (formerly codenamed Tiger Lake) 
  • Support for INT8 inference pipeline with optimizations based on Intel® DL Boost for integrated graphics.
  • Support for new operations:
    • Mish
    • Swish
    • SoftPlus
    • HSwish



  • Added the support for ONNX Faster R-CNN with fixed input shape and dynamic outputs shapes.
  • Added the support for automatic-DMA for custom OpenCL layers.
  • Added the support for new operations:
    • Mish
    • Swish
    • SoftPlus
    • Gelu
    • StridedSlice
    • I32 data type support in Div
  • Improved the performance of existing operations:
    • ROIAlign
    • Broadcast
    • GEMM
  • Added a new option VPU_TILING_CMX_LIMIT_KB to myriad_compile that enables limiting DMA transaction size.
  • OpenCL compiler, targeting Intel® Neural Compute Stick 2 for the SHAVE* processor only, is redistributed with OpenVINO. OpenCL support is provided by ComputeAorta*, and is distributed under a license agreement between Intel® and Codeplay* Software Ltd.

HDDL Plugin

HDDL Plugin

  • Supported automatic-DMA for custom OpenCL layers.
  • Same new operations and optimizations as in the MYRIAD plugin.
  • OpenCL compiler, targeting Intel® Vision Accelerator Design with Intel® Movidius™ VPUs for the SHAVE* processor only, is redistributed with OpenVINO. OpenCL support is provided by ComputeAorta*, and is distributed under a license agreement between Intel® and Codeplay* Software Ltd.

GNA Plugin

GNA Plugin

  • Added the support for 11th Generation Intel® Core™ Processor Family for Internet of Things (IoT) Applications (formerly codenamed Tiger Lake).
  • Added the support for a number of additional layers and layer combinations, including:
    • Convolution layers for models prepared from TensorFlow framework
    • Power layer with the power parameter different from 1
    • Concat layer with the number of input layers greater than 2
    • 4D element-wise operations
  • Added the support for importing a model from a stream.
  • Added the support for the QoS mechanism on Windows.
  • Added the support for GNA-specific parameters in the Python Benchmark app.


  • Introduced opset4. The new opset contains the new operations listed below. Not all OpenVINO™ toolkit plugins support the operations.
    • Acosh-4
    • Asinh-4
    • Atanh-4
    • CTCLoss-4
    • HSwish-4
    • Interpolate-4
    • LSTMCell-4
    • Mish-4
    • Proposal-4
    • Range-4
    • ReduceL1-4
    • ReduceL2-4
    • ScattenNDUpdate-4
    • SoftPlus-4
    • Swish-4
  • Enabled the nGraph Python API, which enables communicating with the nGraph function using Python. This enables you to perform analysis of a loaded graph.
    • Enabled setting parameters of the nodes for the graph. 

    • Enabled reading ONNX models with the Python API.

  • Refactored the nGraph Transformation API to give it a transparent structure and make it more user-friendly. Read more in the nGraph Developer's Guide.
  • Changed the structure of the nGraph folder. nGraph public API was separated from the rest of the code, ONNX importer was moved to the frontend folder.

Neural Networks Compression Framework (NNCF)

  • Released NNCF v1.4 for PyTorch:
  • Enabled exporting pruned models to ONNX.
  • Added the support for FP16 fine-tuning for quantization.
  • Added the support for the BatchNorm adaptation as a common compression algorithm initialization step.
  • Improved the performance for per-channel quantization training. Performance is almost on par with per-tensor training.
  • Enabled default quantization of nn.Embedding and nn.Conv1d weights.
  • See NNCF Release Notes for details.

Post-Training Optimization Tool

Post-Training Optimization Tool

  • Enabled auto-tuning of quantization parameters in the Accuracy Aware algorithm.
  • Accelerated the Honest Bias Correction algorithm. The average boost of quantization time is ~4x comparing to 2020.4 for cases when 'use_fast_bias' = false.
  • Productized the Post-training Optimization Toolkit API. Provided samples and documentation to show how to use the API, which covers:
    • Integration to a user’s pipeline
    • Custom data loader, metric calculation, and execution engine
  • Default quantization scheme corresponds to compatibility mode, which needs to provide almost the same accuracy across different hardware.
  • Extended models coverage: enabled new 44 models.

Deep Learning Workbench

Deep Learning Workbench

  • Enabled import and profiling of  pretrained TensorFlow2.0 models. 
  • Enabled INT8 calibration using different presets exposed by the POT.
  • Enabled INT8 calibration on remote targets.
  • Improved visualization of an IR and a runtime graph, including graph interactions and heat maps. 
  • Added visualization of inference results in an image of user choice. The feature is in experimental mode.


  • Updated version to 4.5.0.
  • Changed the upstream license to Apache 2 (PR#18073).
  • Added the support for multiple OpenCL contexts in OpenCV applications.


  • Updated Inference Engine C++ Samples to demonstrate how to load ONNX* models directly.

Open Model Zoo

  • Extended the Open Model Zoo with additional CNN-pretrained models and pregenerated Intermediate Representations (.xml + .bin). Color coding: replacing 2020.4 models, new, end-of-lifed:

    • Replaced the 2020.4 models:

      • face-detection-0200
      • face-detection-0202
      • face-detection-0204
      • face-detection-0205
      • face-detection-0206
      • person-detection-0200
      • person-detection-0201
      • person-detection-0202
      • person-reidentification-retail-0277
      • person-reidentification-retail-0286
      • person-reidentification-retail-0287
      • person-reidentification-retail-0288
    • Added new models:
      • bert-large-uncased-whole-word-masking-squad-emb-0001
      • bert-small-uncased-whole-word-masking-squad-0002
      • formula-recognition-medium-scan-0001-im2latex-decoder
      • formula-recognition-medium-scan-0001-im2latex-encoder
      • horizontal-text-detection-0001
      • machine-translation-nar-en-ru-0001
      • machine-translation-nar-ru-en-0001
      • person-attributes-recognition-crossroad-0234
      • person-attributes-recognition-crossroad-0238
      • person-vehicle-bike-detection-2000
      • person-vehicle-bike-detection-2001
      • person-vehicle-bike-detection-2002
      • person-vehicle-bike-detection-crossroad-yolov3-1020
      • vehicle-detection-0200
      • vehicle-detection-0201
      • vehicle-detection-0202
    • End-of-lifed models:
      • face-detection-adas-binary-0001
      • pedestrian-detection-adas-binary-0001
      • vehicle-detection-adas-binary-0001
  • The list of public models extended with the support for the following models:

    Model Name




    resnest-50 PyTorch
    mozilla-deepspeech-0.6.1 Tensorflow
    yolo-v3-tiny-tf Tensorflow
  • Added new demo applications:
    • bert_question_answering_embedding_demo, Python
    • formula_recognition_demo, Python
    • machine_translation_demo, Python
    • sound_classification_demo, Python
    • speech_recognition_demo, Python 
  • Open Model Zoo tools:
    • Improved the downloader speed.
    • Added the Accuracy Checker config files to each model folder. For compatibility, in their old location soft links are kept to the new location. In future releases, soft links will be removed.
    • Simplified the Accuracy Checker configuration files, no need to specify the path to the model IR or target device and precision in a configuration file. Apply these parameters as Accuracy Checker command-line options. See details in the instruction on how to use predefined configuration files.
    • Extended the Accuracy Checker with the support for optimized preprocessing operations via the Inference Engine preprocessing API.
    • Enabled ONNX models evaluation in the Accuracy Checker without conversion to the IR format.

Deep Learning Streamer

  • Expanded the DL Streamer beyond video by adding the support for audio analytics. Added a new element gvaaudiodetect for audio event detection using the AclNet model. Added an end-to-end sample of the pipeline to the samples folder.
  • Added a new element gvametaaggregate to combine the results from multiple branches of a pipeline. This enables the creation of complex pipelines by splitting a pipeline into multiple branches for parallel processing and then combining the results from various branches. 
  • Enabled GPU memory surface sharing, namely zero-copy of data, between VAAPI decode, resize, CSC, and DL Streamer inference elements on GPU to improve the overall pipeline performance.
  • Enabled GPU memory at input and output of gvatrack and gvawatermark elements, thereby removing the need to explicitly convert the memory from GPU to CPU using vaapipostproc when inference is performed on GPU. This not only makes the pipelines portable between the devices with and without GPU but also improves the performance due to the removal of a memory copy step.
  • [Preview] Extended the DL Streamer OS support to Ubuntu 20.04. On Ubuntu 20.04, the DL Streamer will use the GStreamer and its plugins provided by the OS, and thus you have access to all the elements provided by the GStreamer default installation on Ubuntu 20.04.

For more information on DL Streamer, see the DL Streamer tutorial, API reference, and samples documentation at OpenVINO™ Inference Engine Samples and a new home for the DL Streamer open source project located at OpenVINO™ Toolkit - DL Streamer repository on GitHub.

OpenVINO™ Model Server

Model Server

Model Server is a scalable high-performance tool for serving models optimized with OpenVINO™. It provides an inference service via a gRPC or HTTP/REST endpoint, enabling you to bring your models to production quicker without writing custom code.

Key Features and Enhancements

  • Improved scalability in a single server instance. With the new C++ implementation, you can use the full capacity of available hardware with linear scalability while avoiding any bottleneck on the frontend.
  • Reduced the latency between the client and the server. This is especially noticeable with high-performance accelerators or CPUs.
  • Reduced footprint. By switching to C++ and reducing dependencies, the Docker image is reduced to ~450MB.
  • Added the support for online model updates. The server monitors configuration file changes and reloads models as needed without restarting the service.

For more information about the Model Server, see the open source repo and the Model Server Release Notes. Prebuilt Docker images are available at openvino/model_server

Preview Features Terminology

A preview feature is a functionality that is being introduced to gain early feedback from developers. You are encouraged to submit your comments, questions, and suggestions related to preview features to the forum.

The key properties of a preview feature are:

  • High-quality implementation
  • No guarantee of future existence, compatibility, or security confidence.

Note A preview feature/support is subject to change in the future. It may be removed or radically altered in future releases. Changes to a preview feature do NOT require the usual deprecation and deletion process. Using a preview feature in a production code base is therefore strongly discouraged.

Known Issues


Jira ID




  #1 A number of issues were not addressed yet, see the Known Issues section in the Release Notes for Intel® Distribution of OpenVINO™ toolkit v.2020 All N/A
1 21670 FC layers with bimodal weights distribution are not quantized accurately by the Intel® GNA Plugin when 8-bit quantization is specified. Weights with values near to zero are set to zero. IE GNA plugin For now, use 16-bit weights in these cases.
2 25358 Some performance degradations are possible in the GPU plugin on GT3e/GT4e/ICL NUC platforms. IE GPU Plugin N/A
3 24709 Retrained TensorFlow Object Detection API RFCN model has significant accuracy degradation. Only the pretrained model produces correct inference results. All Use Faster-RCNN models instead of an RFCN model if retraining of a model is required.


Low latency (batch size 1) graphs with LSTMCell do not infer properly due to missing state handling. All Use deprecated IRv7 and manually insert memory layers into the IR graph. Alternatively, add state tensors as extra input and output nodes and associate their blobs given the IR node IDs after loading the graph.
5 24101 Performance and memory consumption may be bad if layers are not 64-bytes aligned. IE GNA plugin Try to avoid the layers which are not 64-bytes aligned to make a model GNA-friendly.
6 33146 Some models fail with batch greater than 1 on Intel® Neural Compute Stick 2 and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs. IE MyriadX plugin, IE HDDL plugin Use batch 1 or use other hardware. like CPU or GPU.



In the FSMN model MO output, the tensor iterate number is wrong. MO Currently, the MO does not support dynamic tensor iterator loop control. Manually modify the FSMN IR XML, change all end="1" to end="76".
8 30271 Performance degradation with the Python benchmark_app. IE Tools N/A
9 30571 The benchmark_app (CPP sample app) does not support models with the NHWC layout. IE Tools N/A
10 32927 OpenVINO™ benchmark_app is not interpreting the nthreads parameter as expected. IE Tools N/A
11 30580 When running accuracy checkout on the entire dataset for UNET2D, it throws an error. IE Tools N/A
12 28259 Slow BERT inference in the Python interface. IE Python This is only visible when importing PyTorch. Do not import the PyTorch module.
13 34660 Model initialization in OS X fails. IE C API N/A
14 34065 Spin time (tbb scheduler) becomes the overhead when running inference for models. IE Common N/A
15 38623 Incorrect detection with VASurface sharing. IE clDNN Plugin N/A
16 36549 Requirements installation contains errors. Workbench N/A
17 35367 [IE][TF2] Several models failed on the last tensor check with FP32. IE MKL-DNN Plugin N/A
18 35363 Wait method timeout specified by the user is not taken into account by the GNA plugin. IE GNA Plugin N/A
19 39060 LoadNetwork crash on CentOS 7 with a large number of models. IE MKL-DNN Plugin N/A
20 35955 ReduceSum error on some reduction axes (nGraph function with Inference Engine) IE NG Conversion N/A
21 37418 [IE MKL-DNN][INT8] Memory consumption increased for several models. IE NG Conversion N/A
22 34087 [cIDNN] Performance degradation on several models due to upgrade of the OpenCL driver clDNN N/A
23 33132 [IE CLDNN] Accuracy and last-tensor checks regressions for FP32 models on ICLU GPU IE clDNN Plugin N/A
24 39128 Mounted directory contains nothing on Windows. Workbench N/A
25 36628 [MYRIAD] Crash when executing PyTorch-trained YoloV3 on NCS2/MyriadX IE MyriadX Plugin Reconvert model with adding "--output 446/Split" to the Model Optimizer command line.
26 25358 [cIDNN] Performance degradation on NUC and ICE_LAKE targets on R4 IE clDNN Plugin N/A
27 38605 [nGraph Python API] Debug build is compiled with incorrect options. IE NG Python N/A
28 33400 IR specification is rendered without comments in the public documentation. Documentation N/A
29 34060 [IE][PyTorch] ONNX_Runtime_GPT2 model failed with the "Cannot set batch for 1D/3D input on batch 2 only" error. IE Python N/A
30 38249 [HETERO] Hetero plugin does not support an INT8 network with a manual graph splitting into two devices. IE Hetero Plugin N/A
31 39150 MLPerf ONNX / Unet3D INT8 failed to score on CFL with segmentation fault. IE MKL-DNN Plugin N/A
32 39136 Calling LoadNetwork after a failed reshape throws an exception IE NG integration N/A
33 38816 Failure to build samples in C [Windows only] IE Samples N/A
34 39024 [MKLDNN] LoadTime increased for several models. IE NG Conversion N/A
35 39175 [nGraph Python API] Absent of documentation comments in .cpp files Documentation N/A
36 39231

LSTMSequence operation is not supported via ngraph and ONNX importer and mistakenly included in opset1, opset2, and opset3. The operation is excluded from opset4.

nGraph Core

LSTMSequence is supported via ModelOptimizer, which converts it into the TensorIterator op.

Included in This Release

The Intel® Distribution of OpenVINO™ toolkit is available in these versions:

  • OpenVINO™ toolkit for Windows*
  • OpenVINO™ toolkit for Linux*
  • OpenVINO™ toolkit for macOS*
Component License Location Windows Linux macOS

Deep Learning Model Optimizer

Model optimization tool for your trained models

Apache 2.0 <install_root>/deployment_tools/model_optimizer/* YES YES YES

Deep Learning Inference Engine

Unified API to integrate the inference with application logic

Inference Engine Headers




Apache 2.0






OpenCV* library

OpenCV Community version compiled for Intel® hardware

Apache 2.0 <install_root>/opencv/* YES YES YES

Intel® Media SDK libraries (open source version)

Eases the integration between the OpenVINO™ toolkit and the Intel® Media SDK.

MIT <install_root>/../mediasdk/* NO YES NO

OpenVINO™ toolkit documentation

Developer guides and other documentation

  Available from the OpenVINO™ toolkit product site, not part of the installer packages. NO NO NO

Open Model Zoo

Documentation for models from the Intel® Open Model Zoo. Use the Model Downloader to download models in a binary format.

Apache 2.0 <install_root>/deployment_tools/open_model_zoo/* YES YES YES

Inference Engine Samples

Samples that illustrate Inference Engine API usage and demos that demonstrate how you can use features of Intel® Distribution of OpenVINO™ toolkit in your application

Apache 2.0

<install_root>/deployment_tools/inference_engine/samples/* YES YES YES

Deep Learning Workbench

Enables you to run deep learning models through the OpenVINO™ Model Optimizer, convert models into INT8, fine-tune them, run inference, and measure accuracy.

EULA <install_root>/deployment_tools/tools/workbench/* YES YES YES

Post-Training Optimization Toolkit

Designed to convert a model into a more hardware-friendly representation by applying specific methods that do not require retraining, for example, post-training quantization.

EULA <install_root>/deployment_tools/tools/post_training_optimization_toolkit/* YES YES YES

Speech Libraries and End-to-End Speech Demos


GNA Software License Agreement <install_root>/data_processing/audio/speech_recognition/* YES YES NO
DL Streamer EULA <install_root>/data_processing/dl_streamer/* NO YES NO

Where to Download this Release

System Requirements

Intel® CPU Processors


  • Intel® Atom* processor with Intel® SSE4.1 support
  • Intel® Pentium® processor N4200/5, N3350/5, N3450/5 with Intel® HD Graphics
  • 6th - 11th generation Intel® Core™ processors
  • Intel® Xeon® processor E3, E5, and E7 family (formerly Sandy Bridge, Ivy Bridge, Haswell, and Broadwell)
  • 2nd Generation Intel® Xeon® Scalable Processors (formerly Skylake and Cascade Lake)
  • 3rd Generation Intel® Xeon® Scalable Processors (formerly Cooper Lake)

Operating Systems:

  • Ubuntu* 18.04 long-term support (LTS), 64-bit
  • Ubuntu* 20.04 long-term support (LTS), 64-bit - preview support
  • Windows* 10, 64-bit
  • macOS* 10.15, 64-bit

Intel® Processor Graphics


  • Intel® HD Graphics
  • Intel® UHD Graphics
  • Intel® Iris® Pro Graphics

Operating Systems:

  • Ubuntu* 18.04 long-term support (LTS), 64-bit
  • Windows* 10, 64-bit
  • Yocto* 3.0, 64-bit

Note This installation requires drivers that are not included in the Intel Distribution of OpenVINO toolkit package

Note A chipset that supports processor graphics is required for Intel®Xeon processors. Processor graphics are not included in all processors. See Product Specifications for information about your processor.

Intel® Gaussian & Neural Accelerator (Intel® GNA)

Operating Systems:

  • Ubuntu* 18.04 long-term support (LTS), 64-bit
  • Windows* 10, 64-bit

Intel® FPGA Processors

Note Only for the OpenVINO™ toolkit for Linux with FPGA Support download.


  • Intel® Vision Accelerator Design with an Intel® Arria 10 FPGA (Mustang-F100-A10) Speed Grade 1 and Speed Grade 2
  • Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA (Intel® PAC with Intel® Arria® 10 GX FPGA)

Operating Systems:

  • Ubuntu* 18.04 long-term support (LTS), 64-bit

Intel® VPU Processors

Intel® Vision Accelerator Design with Intel® Movidius™ Vision Processing Units (VPU)

Operating Systems:

  • Ubuntu* 18.04 long-term support (LTS), 64-bit (Linux Kernel 5.2 and below)
  • Windows* 10, 64-bit
  • CentOS* 7.6, 64-bit

Intel® Movidius™ Neural Compute Stick and Intel® Neural Compute Stick 2

Operating Systems:

  • Ubuntu* 18.04 long-term support (LTS), 64-bit
  • CentOS* 7.6, 64-bit
  • Windows* 10, 64-bit
  • Raspbian* (target only)

AI Edge Computing Board with Intel® Movidius™ Myriad™ X C0 VPU, MYDX x 1

Operating Systems:

  • Windows* 10, 64-bit

Components Used In Validation

Operating systems used in validation:

  • Linux* OS
    • Ubuntu 18.04.3 with Linux kernel 5.3
      •  Ubuntu 18.04.3 with Linux kernel 5.6 for 10th Generation Intel® Core™ Processors (formerly codenamed Ice Lake ) and 11th Generation Intel® Core™ Processor Family for Internet of Things (IoT) Applications  (formerly codenamed Tiger Lake)
    • Ubuntu 20.04 with Linux kernel 5.4
    • CentOS 7.6 with Linux kernel 5.3
    • A Linux* OS build environment needs these components:
      • GNU Compiler Collection (GCC)* 4.8 (CentOS 7), 7.5 (Ubuntu 18), 9.3 (Ubuntu 20)
      • CMake* 3.10 or higher
      • Python* 3.6-3.7, additionally 3.8 for Ubuntu 20
      • OpenCV 4.5
      • Intel® Graphics Compute Runtime. Required only for GPU.
        • 19.41
        • 20.35 for 10th Generation Intel® Core™ Processors (formerly codenamed Ice Lake ) and 11th Generation Intel® Core™ Processor Family for Internet of Things (IoT) Applications  (formerly codenamed Tiger Lake)
  • Windows 10 version 1809 (known as Redstone 5)
  • macOS 10.15

DL frameworks used for validation:

  • TensorFlow 1.15.2, 2.2.0 (limited support according to product features)
  • MxNet 1.5.1

Note Building samples and demos from the Intel® Distribution of OpenVINO™ toolkit package requires CMake* 3.10 or higher.

Helpful Links

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Legal Information

You may not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. You agree to grant Intel a non-exclusive, royalty-free license to any patent claim thereafter drafted which includes subject matter disclosed herein.

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All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest Intel product specifications and roadmaps.

The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request.

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

Copyright © 2020, Intel Corporation. All rights reserved.

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Product and Performance Information


Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804