From 870655ca280742de79ddb9545aae5da7f04a9c9a Mon Sep 17 00:00:00 2001 From: Olivia Jain Date: Mon, 28 Mar 2022 10:17:44 -0700 Subject: [PATCH] Update Broken OpenVINO Links (#11009) --- docs/execution-providers/OpenVINO-ExecutionProvider.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/docs/execution-providers/OpenVINO-ExecutionProvider.md b/docs/execution-providers/OpenVINO-ExecutionProvider.md index e7a100d010..400d6f84e1 100644 --- a/docs/execution-providers/OpenVINO-ExecutionProvider.md +++ b/docs/execution-providers/OpenVINO-ExecutionProvider.md @@ -54,7 +54,7 @@ To utilize accelerators power and calculate heaviest parts of network on acceler 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](https://docs.openvino.ai/latest/openvino_docs_IE_DG_supported_plugins_HETERO.html). +[Intel OpenVINO Heterogeneous Plugin](https://docs.openvino.ai/latest/openvino_docs_OV_UG_Hetero_execution.html). ### Multi-Device Execution for OpenVINO EP @@ -64,7 +64,7 @@ Multi-Device plugin automatically assigns inference requests to available comput * 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](https://docs.openvino.ai/latest/openvino_docs_IE_DG_supported_plugins_MULTI.html). +[Intel OpenVINO Multi Device Plugin](https://docs.openvino.ai/latest/openvino_docs_OV_UG_Running_on_multiple_devices.html). ### Auto-Device Execution for OpenVINO EP @@ -80,7 +80,6 @@ For more information on Auto-Device plugin of OpenVINO, please refer to the The model caching setting enables blobs with Myriadx(VPU) and as cl_cache files with iGPU. #### Save/Load blob capability for Myriadx(VPU) - 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. #### CL Cache capability for iGPU @@ -132,7 +131,7 @@ However, int8 support won't be available for VPU. Starting from the OpenVINO EP 2021.4 Release, support for external weights is added. OpenVINO™ EP 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](https://docs.openvino.ai/latest/openvino_docs_IE_DG_ONNX_Support.html). +See the [OpenVINO ONNX Support documentation](https://docs.openvino.ai/latest/classov_1_1Core.html). Converting and Saving an ONNX Model to External Data: Use the ONNX API's.[documentation](https://github.com/onnx/onnx/blob/master/docs/ExternalData.md#converting-and-saving-an-onnx-model-to-external-data). @@ -212,7 +211,7 @@ The following table lists all the available configuration options and the Key-Va | **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](https://docs.openvino.ai/latest/classInferenceEngine_1_1ICore.html#doxid-class-inference-engine-1-1-i-core). If this option is not explicitly set, an arbitrary free device will be automatically selected by OpenVINO runtime.| +| 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](https://docs.openvino.ai/latest/classInferenceEngine_1_1Core.html). 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. | @@ -468,4 +467,3 @@ In order to showcase what you can do with the OpenVINO Execution Provider for ON ### Tutorial on how to use OpenVINO™ Execution Provider for ONNX Runtime python wheel packages [Python Pip Wheel Packages](https://www.intel.com/content/www/us/en/artificial-intelligence/posts/openvino-execution-provider-for-onnx-runtime.html) -