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nGraph Execution Provider

Deprecation Notice

Deprecation Begins June 1, 2020
Removal Date December 1, 2020

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 ONNX RT Execution Provider for nGraph have been merged with ONNX RT Execution Provider for OpenVINO™ toolkit.

Therefore, ONNX RT Execution Provider for nGraph will be deprecated starting June 1, 2020 and will be completely removed on December 1, 2020. Users are recommended to migrate to the ONNX RT Execution Provider for OpenVINO™ toolkit as the unified solution for all AI inferencing on Intel® hardware.

nGraph is a deep learning compiler from Intel®. The integration of nGraph as an execution provider (EP) into ONNX Runtime accelerates performance of ONNX model workloads across wide range of hardware offerings. Microsoft and Intel worked closely to integrate the nGraph EP with ONNX Runtime to showcase the benefits of quantization (int8). The nGraph EP leverages Intel® DL Boost and delivers performance increase with minimal loss of accuracy relative to FP32 ONNX models. With the nGraph EP, the ONNX Runtime delivers better inference performance across range of Intel hardware including Intel® Xeon® Processors compared to a generic CPU execution provider.


For build instructions, please see the BUILD page.

Supported OS

While the nGraph Compiler stack supports various operating systems and backends (full list available here), the nGraph execution provider for ONNX Runtime is validated for the following:

  • Ubuntu 16.04
  • Windows 10 (DEX_ONLY mode is only one supported for the moment, codegen mode is work-in-progress.)
  • More to be added soon!

Supported backend

  • CPU

Using the nGraph execution provider


To use nGraph as execution provider for inferencing, please register it as below.

string log_id = "Foo";
auto logging_manager = std::make_unique<LoggingManager>
(std::unique_ptr<ISink>{new CLogSink{}},
Environment::Create(std::move(logging_manager), env)
InferenceSession session_object{so,env};
status = session_object.Load(model_file_name);

The C API details are here.


When using the python wheel from the ONNX Runtime built with nGraph execution provider, it will be automatically prioritized over the CPU execution provider. Python APIs details are here.

Performance Tuning

For performance tuning, please see guidance on this page: ONNX Runtime Perf Tuning

When/if using onnxruntime_perf_test, use the flag -e ngraph