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OpenVINO™ Execution Provider

Accelerate ONNX models on Intel CPUs, GPUs and VPUs with Intel OpenVINO™ Execution Provider. Please refer to this page for details on the Intel hardware supported.



Pre-built packages and Docker images are published for OpenVINO™ Execution Provider for ONNX Runtime by Intel for each release.


ONNX Runtime OpenVINO™ Execution Provider is compatible with three lastest releases of OpenVINO™.

ONNX Runtime OpenVINO™ Notes
1.11.0 2022.1 Details
1.10.0 2021.4.2 Details
1.9.0 2021.4.1 Details
1.8.1 2021.4 Details
1.8.0 2021.3 Details


For build instructions, please see the BUILD page.

Announcement: OpenVINO™ Execution Provider for ONNX Runtime v4.0 Release with OpenVINO™ 2022.1 version:

  • The new OpenVINO™ Execution Provider for ONNX Runtime v4.0 Release introduces a new version of OpenVINO™ API (API 2.0). For more information on the changes and transition steps, see the transition guide

  • With this release, we have officially stopped supporting CentOS. We will start supporting RHEL OS from OpenVINO™ 2022.1.


OpenVINO™ Environment


Please download onnxruntime-openvino python packages from

pip install onnxruntime-openvino==1.11.0

To enable OpenVINO™ Execution Provider with ONNX Runtime on Windows we must install OpenVINO™ separately:

pip install openvino==2022.1

Code to be added in ONNX Runtime Windows Samples:

import openvino.utils as utils

OpenVINO™ Execution Provider with Onnx Runtime on Linux installed from come with prebuilt OpenVINO™ libs and supports flag CXX11_ABI=0. So there is no need to install OpenVINO™ separately.

To enable CX11_ABI=1 flag, build Onnx Runtime python wheel packages from source. For build instructions, please see the BUILD page. OpenVINO™ Execution Provider wheels on Linux built from source will not have prebuilt OpenVINO™ libs so we must set the OpenVINO™ Environment Variable using the full installer package of OpenVINO™:

C:\ <openvino_install_directory>\setupvars.bat


For Running C++/C# ORT Samples with the OpenVINO™ Execution Provider it is must to set up the OpenVINO™ Environment Variables using the full installer package of OpenVINO™. Initialize the OpenVINO™ environment by running the setupvars script as shown below. This is a required step:

  • For Linux run till OpenVINO™ 2021.4 version:
     $ source <openvino_install_directory>/bin/
  • For Linux run from OpenVINO™ 2022.1 version:
     $ source <openvino_install_directory>/
  • For Windows run till OpenVINO™ 2021.4 version:
     C:\ <openvino_install_directory>\bin\setupvars.bat
  • For Windows run from OpenVINO™ 2022.1 version:
     C:\ <openvino_install_directory>\setupvars.bat

    Note: If you are using a dockerfile to use OpenVINO™ Execution Provider, sourcing OpenVINO™ won’t be possible within the dockerfile. You would have to explicitly set the LD_LIBRARY_PATH to point to OpenVINO™ libraries location. Refer our [dockerfile].(


To use csharp api for openvino execution provider create a custom nuget package. Follow the instructions here to install prerequisites for nuget creation. Once prerequisites are installed follow the instructions to build openvino and add an extra flag --build_nuget to create nuget packages. Two nuget packages will be created Microsoft.ML.OnnxRuntime.Managed and Microsoft.ML.OnnxRuntime.Openvino.

Multi-threading for OpenVINO™ Execution Provider

OpenVINO™ Execution Provider for ONNX Runtime enables thread-safe deep learning inference

Heterogeneous Execution for OpenVINO™ Execution Provider

The heterogeneous execution enables computing for inference on one network on several devices. Purposes to execute networks in heterogeneous mode:

  • To utilize accelerator’s power and calculate the heaviest parts of the network on the accelerator and execute unsupported layers on fallback devices like the CPU to utilize all available hardware more efficiently during one inference.

For more information on Heterogeneous plugin of OpenVINO™, please refer to the Intel OpenVINO™ Heterogeneous Plugin.

Multi-Device Execution for OpenVINO EP

Multi-Device plugin automatically assigns inference requests to available computational devices to execute the requests in parallel. Potential gains are as follows:

  • Improved throughput that multiple devices can deliver (compared to single-device execution)
  • More consistent performance, since the devices can now share the inference burden (so that if one device is becoming too busy, another device can take more of the load)

For more information on Multi-Device plugin of OpenVINO™, please refer to the Intel OpenVINO™ Multi Device Plugin.

Auto-Device Execution for OpenVINO EP

Use AUTO:<device 1><device 2>.. as the device name to delegate selection of an actual accelerator to OpenVINO™. With the 2021.4 release, Auto-device internally recognizes and selects devices from CPU, integrated GPU and discrete Intel GPUs (when available) depending on the device capabilities and the characteristic of CNN models, for example, precisions. Then Auto-device assigns inference requests to the selected device.

From the application point of view, this is just another device that handles all accelerators in full system.

For more information on Auto-Device plugin of OpenVINO™, please refer to the Intel OpenVINO™ Auto Device Plugin.

Model caching feature for OpenVINO™ Execution Provider

The model caching setting enables blobs with Myriadx(VPU) and as cl_cache files with iGPU.

OpenCL queue throttling for GPU devices

Enables OpenCL queue throttling for GPU devices. Reduces CPU utilization when using GPUs with OpenVINO EP.

Save/Load blob capability for Myriadx(VPU) with OpenVINO™ 2021.3 version

This feature enables users to save and load the blobs directly. These pre-compiled blobs can be directly loaded on to the specific hardware device target and inferencing can be done. This feature is only supported on MyriadX(VPU) hardware device target.

Model caching

Starting from version 2021.4, OpenVINO™ supports model caching. With OpenVINO™ 2021.4, it is supported on Myriadx(VPU) and iGPU.

From OpenVINO™ 2022.1 version, this feature is supported on Myriadx(VPU), iGPU and CPU.

iGPU: This feature enables users to save and load the cl_cache files directly. These cl_cache files can be loaded directly onto the iGPU hardware device target and inferencing can be performed.

Myriadx(VPU) and CPU: This feature enables users to save and load the blob file directly. This file can be loaded directly on to the hardware device target and inferencing can be performed.

There are two different methods of exercising this feature:

option 1. Enabling via Runtime options using c++/python API’s.

This flow can be enabled by setting the runtime config option ‘use_compiled_network’ to True while using the c++/python API’S. This config option acts like a switch to on and off the feature.

The blobs are saved and loaded from a directory named ‘ov_compiled_blobs’ relative to the executable path by default. This path however can be overridden using the runtime config option ‘blob_dump_path’ which is used to explicitly specify the path where you would like to dump and load the blobs (VPU, CPU) or cl_cache(iGPU) files from when already using the use_compiled_network(model caching) setting.

Refer to Configuration Options for more information about using these runtime options.

option 2. Importing the pre-compiled blobs directly from the path set by the user.

This flow enables users to import/load the pre-compiled blob directly if available readily. This option is enabled by explicitly setting the path to the blob using environment variables and setting the OV_USE_COMPILED_NETWORK flag to true.

This flow only works for MyriadX(VPU) device.

For Linux:

export OV_BLOB_PATH=<path to the blob>
Example: export OV_BLOB_PATH=/home/blobs_dir/model.blob

For Windows:

set OV_BLOB_PATH=<path to the blob>
Example: set OV_BLOB_PATH=\home\blobs_dir\model.blob


The device specific Myriadx blobs can be generated using an offline tool called compile_tool from OpenVINO™ Toolkit.documentation.

Support for INT8 Quantized models

Starting from the OpenVINO™ Execution Provider 2021.4 Release, int8 models will be supported on CPU and GPU. However, int8 support won’t be available for VPU.

Support for Weights saved in external files

Starting from the OpenVINO™ Execution Provider 2021.4 Release, support for external weights is added. OpenVINO™ Execution Provider now supports ONNX models that store weights in external files. It is especially useful for models larger than 2GB because of protobuf limitations.

See the OpenVINO™ ONNX Support documentation.

Converting and Saving an ONNX Model to External Data: Use the ONNX API’s.documentation.


import onnx
onnx_model = onnx.load("model.onnx") # Your model in memory as ModelProto
onnx.save_model(onnx_model, 'saved_model.onnx', save_as_external_data=True, all_tensors_to_one_file=True, location='data/weights_data', size_threshold=1024, convert_attribute=False)


  1. In the above script, model.onnx is loaded and then gets saved into a file called ‘saved_model.onnx’ which won’t have the weights but this new onnx model now will have the relative path to where the weights file is located. The weights file ‘weights_data’ will now contain the weights of the model and the weights from the original model gets saved at /data/weights_data.

  2. Now, you can use this ‘saved_model.onnx’ file to infer using your sample. But remember, the weights file location can’t be changed. The weights have to be present at /data/weights_data

  3. Install the latest ONNX Python package using pip to run these ONNX Python API’s successfully.

Support for IO Buffer Optimization

To enable IO Buffer Optimization we have to set OPENCL_LIBS, OPENCL_INCS environment variables before build. For IO Buffer Optimization, the model must be fully supported on OpenVINO™ and we must provide in the remote context cl_context void pointer as C++ Configuration Option. We can provide cl::Buffer address as Input using GPU Memory Allocator for input and output.


//Set up a remote context
cl::Context _context;
// Set the context through openvino options
OrtOpenVINOProviderOptions options;
options.context = (void *) _context.get() ;
//Define the Memory area
Ort::MemoryInfo info_gpu("OpenVINO_GPU", OrtAllocatorType::OrtDeviceAllocator, 0, OrtMemTypeDefault);
//Create a shared buffer , fill in with data
cl::Buffer shared_buffer(_context, CL_MEM_READ_WRITE, imgSize, NULL, &err);
//Cast it to void*, and wrap it as device pointer for Ort::Value
void *shared_buffer_void = static_cast<void *>(&shared_buffer);
Ort::Value inputTensors = Ort::Value::CreateTensor(
        info_gpu, shared_buffer_void, imgSize,,
        inputDims.size(), ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT);

Configuration Options

OpenVINO™ Execution Provider can be configured with certain options at runtime that control the behavior of the EP. These options can be set as key-value pairs as below:-

Python API

Key-Value pairs for config options can be set using InferenceSession API as follow:-

session = onnxruntime.InferenceSession(<path_to_model_file>, providers=['OpenVINOExecutionProvider'], provider_options=[{Key1 : Value1, Key2 : Value2, ...}])

Note that the next release (ORT 1.10) will require explicitly setting the providers parameter if you want to use execution providers other than the default CPU provider (as opposed to the current behavior of providers getting set/registered by default based on the build flags) when instantiating InferenceSession.


All the options shown below are passed to SessionOptionsAppendExecutionProvider_OpenVINO() API and populated in the struct OrtOpenVINOProviderOptions in an example shown below, for example for CPU device type:

OrtOpenVINOProviderOptions options;
options.device_type = "CPU_FP32";
options.enable_vpu_fast_compile = 0;
options.device_id = "";
options.num_of_threads = 8;
options.use_compiled_network = false;
options.blob_dump_path = "";
options.context = 0x123456ff;
options.enable_opencl_throttling = false;
SessionOptionsAppendExecutionProvider_OpenVINO(session_options, &options);

Summary of options

The following table lists all the available configuration options and the Key-Value pairs to set them:

Key Key type Allowable Values Value type Description  
device_type string CPU_FP32, GPU_FP32, GPU_FP16, MYRIAD_FP16, VAD-M_FP16, VAD-F_FP32, Any valid Hetero combination, Any valid Multi or Auto devices combination string Overrides the accelerator hardware type and precision with these values at runtime. If this option is not explicitly set, default hardware and precision specified during build time is used. Overrides the accelerator hardware type and precision with these values at runtime. If this option is not explicitly set, default hardware and precision specified during build time is used.
device_id string Any valid OpenVINO device ID string Selects a particular hardware device for inference. The list of valid OpenVINO device ID’s available on a platform can be obtained either by Python API (onnxruntime.capi._pybind_state.get_available_openvino_device_ids()) or by OpenVINO C/C++ API. If this option is not explicitly set, an arbitrary free device will be automatically selected by OpenVINO runtime.  
enable_vpu_fast_compile string True/False boolean This option is only available for MYRIAD_FP16 VPU devices. During initialization of the VPU device with compiled model, Fast-compile may be optionally enabled to speeds up the model’s compilation to VPU device specific format. This in-turn speeds up model initialization time. However, enabling this option may slowdown inference due to some of the optimizations not being fully applied, so caution is to be exercised while enabling this option.  
num_of_threads string Any unsigned positive number other than 0 size_t Overrides the accelerator default value of number of threads with this value at runtime. If this option is not explicitly set, default value of 8 is used during build time.  
use_compiled_network string True/False boolean This option is only available for MYRIAD_FP16 VPU devices for both Linux and Windows and it enables save/load blob functionality. It can be used to directly import pre-compiled blobs if exists or dump a pre-compiled blob at the executable path.  
blob_dump_path string Any valid string path on the hardware target string Explicitly specify the path where you would like to dump and load the blobs for the save/load blob feature when use_compiled_network setting is enabled . This overrides the default path.  
context string OpenCL Context void* This option is only alvailable when OpenVINO EP is built with OpenCL flags enabled. It takes in the remote context i.e the cl_context address as a void pointer.  
enable_opencl_throttling string True/False boolean This option enables OpenCL queue throttling for GPU devices (reduces CPU utilization when using GPU).  
enable_dynamic_shapes string True/False boolean This option if enabled works for dynamic shaped models whose shape will be set dynamically based on the infer input image/data shape at run time in CPU. This gives best result for running multiple inferences with varied shaped images/data.  

Valid Hetero or Multi or Auto Device combinations: HETERO:,,... The can be any of these devices from this list ['CPU','GPU','MYRIAD','FPGA','HDDL']

A minimum of two DEVICE_TYPE’S should be specified for a valid HETERO or Multi-Device Build.


Other configuration settings

Onnxruntime Graph Optimization level

OpenVINO™ backend performs both hardware dependent as well as independent optimizations to the graph to infer it with on the target hardware with best possible performance. In most of the cases it has been observed that passing in the graph from the input model as is would lead to best possible optimizations by OpenVINO™. For this reason, it is advised to turn off high level optimizations performed by ONNX Runtime before handing the graph over to OpenVINO™ backend. This can be done using Session options as shown below:-

Python API

options = onnxruntime.SessionOptions()
options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL
sess = onnxruntime.InferenceSession(<path_to_model_file>, options)



Deprecated: Dynamic device type selection

Note: This API has been deprecated. Please use the mechanism mentioned above to set the ‘device-type’ option. When ONNX Runtime is built with OpenVINO™ Execution Provider, a target hardware option needs to be provided. This build time option becomes the default target harware the EP schedules inference on. However, this target may be overriden at runtime to schedule inference on a different hardware as shown below.

Note: This dynamic hardware selection is optional. The EP falls back to the build-time default selection if no dynamic hardware option value is specified.

Python API

import onnxruntime
# Create session after this

This property persists and gets applied to new sessions until it is explicity unset. To unset, assign a null string (“”).


Append the settings string “" to the EP settings string. Example shown below for the CPU_FP32 option:

std::string settings_str;
Ort::ThrowOnError(OrtSessionOptionsAppendExecutionProvider_OpenVINO(sf, settings_str.c_str()));

Support Coverage

ONNX Layers supported using OpenVINO

The table below shows the ONNX layers supported and validated using OpenVINO™ Execution Provider.The below table also lists the Intel hardware support for each of the layers. CPU refers to Intel® Atom, Core, and Xeon processors. GPU refers to the Intel Integrated Graphics. VPU refers to USB based Intel® MovidiusTM VPUs as well as Intel® Vision accelerator Design with Intel Movidius TM MyriadX VPU.

Abs Yes Yes No
Acos Yes Yes No
Acosh Yes Yes No
Add Yes Yes Yes
And Yes Yes Yes
ArgMax Yes Yes Yes
ArgMin Yes Yes Yes
Asin Yes Yes No
Asinh Yes Yes No
Atan Yes Yes No
Atanh Yes Yes No
AveragePool Yes Yes Yes
BatchNormalization Yes Yes Yes
BitShift Yes No No
Ceil Yes Yes Yes
Cast Yes Yes Yes
Clip Yes Yes Yes
Concat Yes Yes Yes
Constant Yes Yes Yes
ConstantOfShape Yes Yes Yes
Conv Yes Yes Yes
ConvInteger Yes Yes Yes
ConvTranspose Yes Yes Yes
Cos Yes Yes No
Cosh Yes Yes No
CumSum Yes Yes No
DepthToSpace Yes Yes Yes
DequantizeLinear Yes Yes No
Div Yes Yes Yes
Dropout Yes Yes Yes
Elu Yes Yes Yes
Equal Yes Yes Yes
Erf Yes Yes Yes
Exp Yes Yes Yes
Expand Yes Yes Yes
Flatten Yes Yes Yes
Floor Yes Yes Yes
Gather Yes Yes Yes
GatherElements No No Yes
GatherND Yes Yes Yes
Gemm Yes Yes Yes
GlobalAveragePool Yes Yes Yes
GlobalLpPool Yes Yes No
GlobalMaxPool Yes Yes No
Greater Yes Yes Yes
GreaterOrEqual Yes Yes Yes
HardMax Yes Yes No
HardSigmoid Yes Yes No
Identity Yes Yes Yes
ImageScaler Yes Yes Yes
InstanceNormalization Yes Yes Yes
LeakyRelu Yes Yes Yes
Less Yes Yes Yes
LessOrEqual Yes Yes Yes
Log Yes Yes Yes
LogSoftMax Yes Yes Yes
Loop Yes Yes Yes
LRN Yes Yes Yes
LSTM Yes Yes Yes
MatMul Yes Yes Yes
MatMulInteger Yes No No
Max Yes Yes Yes
MaxPool Yes Yes Yes
Mean Yes Yes Yes
MeanVarianceNormalization Yes Yes Yes
Min Yes Yes Yes
Mod Yes Yes No
Mul Yes Yes Yes
Neg Yes Yes Yes
NonMaxSuppression Yes Yes Yes
NonZero Yes No Yes
Not Yes Yes Yes
OneHot Yes Yes Yes
Or Yes Yes No
Pad Yes Yes Yes
Pow Yes Yes Yes
PRelu Yes Yes Yes
QuantizeLinear Yes Yes No
Range Yes Yes Yes
Reciprocal Yes Yes Yes
ReduceL1 Yes Yes No
ReduceL2 Yes Yes No
ReduceLogSum Yes Yes Yes
ReduceLogSumExp Yes Yes Yes
ReduceMax Yes Yes Yes
ReduceMean Yes Yes Yes
ReduceMin Yes Yes Yes
ReduceProd Yes Yes No
ReduceSum Yes Yes Yes
ReduceSumSquare Yes Yes Yes
Relu Yes Yes Yes
Reshape Yes Yes Yes
Resize Yes Yes Yes
ReverseSequence Yes Yes No
RoiAlign Yes Yes Yes
Round Yes Yes Yes
Scatter Yes Yes Yes
ScatterElements Yes Yes Yes
ScatterND Yes Yes No
Selu Yes Yes No
Shape Yes Yes Yes
Shrink Yes Yes No
Sigmoid Yes Yes Yes
Sign Yes Yes No
Sin Yes Yes No
Sinh Yes No No
SinFloat No No Yes
Size Yes Yes No
Slice Yes Yes Yes
Softmax Yes Yes Yes
Softplus Yes Yes Yes
Softsign Yes Yes Yes
SpaceToDepth Yes Yes Yes
Split Yes Yes Yes
Sqrt Yes Yes Yes
Squeeze Yes Yes Yes
Sub Yes Yes Yes
Sum Yes Yes Yes
Tan Yes Yes No
Tanh Yes Yes Yes
Tile Yes Yes Yes
TopK Yes Yes Yes
Transpose Yes Yes Yes
Unsqueeze Yes Yes Yes
Upsample Yes Yes Yes
Where Yes Yes Yes
Xor Yes Yes No

Topology Support

Below topologies from ONNX open model zoo are fully supported on OpenVINO™ Execution Provider and many more are supported through sub-graph partitioning

Image Classification Networks

bvlc_alexnet Yes Yes Yes
bvlc_googlenet Yes Yes Yes
bvlc_reference_caffenet Yes Yes Yes
bvlc_reference_rcnn_ilsvrc13 Yes Yes Yes
emotion ferplus Yes Yes Yes
densenet121 Yes Yes Yes
inception_v1 Yes Yes Yes
inception_v2 Yes Yes Yes
mobilenetv2 Yes Yes Yes
resnet18v1 Yes Yes Yes
resnet34v1 Yes Yes Yes
resnet101v1 Yes Yes Yes
resnet152v1 Yes Yes Yes
resnet18v2 Yes Yes Yes
resnet34v2 Yes Yes Yes
resnet101v2 Yes Yes Yes
resnet152v2 Yes Yes Yes
resnet50 Yes Yes Yes
resnet50v2 Yes Yes Yes
shufflenet Yes Yes Yes
squeezenet1.1 Yes Yes Yes
vgg19 Yes Yes Yes
vgg16 Yes Yes Yes
zfnet512 Yes Yes Yes
mxnet_arcface No Yes No

Image Recognition Networks

mnist Yes Yes Yes

Object Detection Networks

tiny_yolov2 Yes Yes Yes
yolov3 Yes Yes Yes
tiny_yolov3 Yes Yes Yes
mask_rcnn Yes Yes Yes
faster_rcnn Yes Yes Yes
yolov4 Yes Yes Yes
yolov5 Yes Yes Yes

Image Manipulation Networks

mosaic Yes Yes Yes
candy Yes Yes Yes
cgan Yes Yes Yes
rain_princess Yes yes Yes
pointilism Yes Yes Yes
udnie Yes Yes Yes

OpenVINO™ Execution Provider Samples Tutorials

In order to showcase what you can do with the OpenVINO™ Execution Provider for ONNX Runtime, we have created a few samples that shows how you can get that performance boost you’re looking for with just one additional line of code.

Python API

Object detection with tinyYOLOv2 in Python

Object detection with YOLOv4 in Python


Image classification with Squeezenet in CPP

Csharp API

Object detection with YOLOv3 in C#


Overview of OpenVINO Execution Provider for ONNX Runtime

OpenVINO Execution Provider

Tutorial on how to use OpenVINO™ Execution Provider for ONNX Runtime Docker Containers

Docker Containers

Tutorial on how to use OpenVINO™ Execution Provider for ONNX Runtime python wheel packages

Python Pip Wheel Packages

Accelerate inference for PyTorch models with OpenVINO Execution Provider (Preview)

ONNX Runtime for PyTorch is now extended to support PyTorch model inference using ONNX Runtime. It is available via the torch-ort-infer python package. This preview package enables OpenVINO™ Execution Provider for ONNX Runtime by default for accelerating inference on various Intel® CPUs, Intel® integrated GPUs, and Intel® Movidius™ Vision Processing Units - referred to as VPU. For more details, see torch-ort-infer.