doc: fix Docker install guide (#7004)
* doc: fix 58710 issue (#6911) * doc: refactor docker install guide (#6988) * doc: refactor docker install guide * doc: refactor docker install guide windows * Update installing-openvino-docker-linux.md * Update installing-openvino-docker-windows.md Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> * doc fixes (#6438) * doc fixes * doc fix * doc fix Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> Co-authored-by: Nikolay Tyukaev <nikolay.tyukaev@intel.com>
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@@ -2,7 +2,7 @@
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## Introduction
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As described in [Inference Engine Introduction](inference_engine_intro.md), common application flow consists of the following steps:
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As described in [Inference Engine Developer Guide](Deep_Learning_Inference_Engine_DevGuide.md), common application flow consists of the following steps:
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1. **Create Inference Engine Core object**
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@@ -5,7 +5,7 @@
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This Guide provides an overview of the Inference Engine describing the typical workflow for performing
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inference of a pre-trained and optimized deep learning model and a set of sample applications.
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> **NOTE:** Before you perform inference with the Inference Engine, your models should be converted to the Inference Engine format using the Model Optimizer or built directly in run-time using nGraph API. To learn about how to use Model Optimizer, refer to the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). To learn about the pre-trained and optimized models delivered with the OpenVINO™ toolkit, refer to [Pre-Trained Models](@ref omz_models_intel_index).
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> **NOTE:** Before you perform inference with the Inference Engine, your models should be converted to the Inference Engine format using the Model Optimizer or built directly in run-time using nGraph API. To learn about how to use Model Optimizer, refer to the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). To learn about the pre-trained and optimized models delivered with the OpenVINO™ toolkit, refer to [Pre-Trained Models](@ref omz_models_group_intel).
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After you have used the Model Optimizer to create an Intermediate Representation (IR), use the Inference Engine to infer the result for a given input data.
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@@ -101,6 +101,9 @@ limitations under the License.
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<tab type="user" title="opset2 Specification" url="@ref openvino_docs_ops_opset2"/>
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<tab type="user" title="opset1 Specification" url="@ref openvino_docs_ops_opset1"/>
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</tab>
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<tab type="usergroup" title="Broadcast Rules For Elementwise Operations" url="@ref openvino_docs_ops_broadcast_rules">
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<tab type="usergroup" title="Broadcast Rules For Elementwise Operations" url="@ref openvino_docs_ops_broadcast_rules"/>
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</tab>
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<tab type="usergroup" title="Operations Specifications" url="">
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<tab type="user" title="Abs-1" url="@ref openvino_docs_ops_arithmetic_Abs_1"/>
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<tab type="user" title="Acos-1" url="@ref openvino_docs_ops_arithmetic_Acos_1"/>
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@@ -158,6 +158,9 @@ limitations under the License.
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<xi:include href="omz_docs.xml" xpointer="omz_models">
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<xi:fallback/>
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</xi:include>
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<tab type="user" title="Dataset Preparation Guide" url="@ref omz_data_datasets"/>
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<tab type="user" title="Intel's Pre-Trained Models Device Support" url="@ref omz_models_intel_device_support"/>
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<tab type="user" title="Public Pre-Trained Models Device Support" url="@ref omz_models_public_device_support"/>
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<xi:include href="omz_docs.xml" xpointer="omz_demos">
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<xi:fallback/>
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</xi:include>
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@@ -205,6 +208,8 @@ limitations under the License.
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<tab type="user" title="MetaPublish Listeners" url="@ref gst_samples_gst_launch_metapublish_listener"/>
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</tab>
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<tab type="user" title="gvapython Sample" url="@ref gst_samples_gst_launch_gvapython_face_detection_and_classification_README"/>
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<tab type="user" title="Action Recognition Sample" url="@ref gst_samples_gst_launch_action_recognition_README"/>
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<tab type="user" title="Human Pose Estimation Sample" url="@ref gst_samples_gst_launch_human_pose_estimation_README"/>
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</tab>
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<tab type="user" title="Draw Face Attributes C++ Sample" url="@ref gst_samples_cpp_draw_face_attributes_README"/>
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<tab type="user" title="Draw Face Attributes Python Sample" url="@ref gst_samples_python_draw_face_attributes_README"/>
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@@ -522,7 +522,7 @@ source /opt/intel/openvino_2021/bin/setupvars.sh
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## <a name="syntax-examples"></a> Typical Code Sample and Demo Application Syntax Examples
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This section explains how to build and use the sample and demo applications provided with the toolkit. You will need CMake 3.10 or later installed. Build details are on the [Inference Engine Samples](../IE_DG/Samples_Overview.md) and [Demo Applications](@ref omz_demos_README) pages.
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This section explains how to build and use the sample and demo applications provided with the toolkit. You will need CMake 3.10 or later installed. Build details are on the [Inference Engine Samples](../IE_DG/Samples_Overview.md) and [Demo Applications](@ref omz_demos) pages.
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To build all the demos and samples:
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@@ -476,7 +476,7 @@ source /opt/intel/openvino_2021/bin/setupvars.sh
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## <a name="syntax-examples"></a> Typical Code Sample and Demo Application Syntax Examples
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|
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This section explains how to build and use the sample and demo applications provided with the toolkit. You will need CMake 3.13 or later installed. Build details are on the [Inference Engine Samples](../IE_DG/Samples_Overview.md) and [Demo Applications](@ref omz_demos_README) pages.
|
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This section explains how to build and use the sample and demo applications provided with the toolkit. You will need CMake 3.13 or later installed. Build details are on the [Inference Engine Samples](../IE_DG/Samples_Overview.md) and [Demo Applications](@ref omz_demos) pages.
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|
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To build all the demos and samples:
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|
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@@ -484,7 +484,7 @@ Below you can find basic guidelines for executing the OpenVINO™ workflow using
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## <a name="syntax-examples"></a> Typical Code Sample and Demo Application Syntax Examples
|
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|
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This section explains how to build and use the sample and demo applications provided with the toolkit. You will need CMake 3.10 or later and Microsoft Visual Studio 2017 or 2019 installed. Build details are on the [Inference Engine Samples](../IE_DG/Samples_Overview.md) and [Demo Applications](@ref omz_demos_README) pages.
|
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This section explains how to build and use the sample and demo applications provided with the toolkit. You will need CMake 3.10 or later and Microsoft Visual Studio 2017 or 2019 installed. Build details are on the [Inference Engine Samples](../IE_DG/Samples_Overview.md) and [Demo Applications](@ref omz_demos) pages.
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To build all the demos and samples:
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+20
-44
@@ -16,71 +16,50 @@ OpenVINO™ toolkit:
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The following diagram illustrates the typical OpenVINO™ workflow (click to see the full-size image):
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### Model Preparation
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#### Components: [Open Model Zoo](https://github.com/opencv/open_model_zoo), [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction), [OpenVINO Training Extentions](https://github.com/openvinotoolkit/training_extensions)
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### Model Preparation, Conversion and Optimization
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You can use your framework of choice to prepare and train a deep learning model or just download a pre-trained model from the Open Model Zoo. The Open Model Zoo includes deep learning solutions to a variety of vision problems, including object recognition, face recognition, pose estimation, text detection, and action recognition, at a range of measured complexities.
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Several of these pre-trained models are also used in the [code samples](IE_DG/Samples_Overview.md) and [application demos](@ref omz_demos_README). To download models from the Open Model Zoo, the [Model Downloader](@ref omz_tools_downloader_README) tool is used.
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Several of these pre-trained models are used also in the [code samples](IE_DG/Samples_Overview.md) and [application demos](@ref omz_demos). To download models from the Open Model Zoo, the [Model Downloader](@ref omz_tools_downloader) tool is used.
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[Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) provides a web-based interface to download a pre-trained model and enables you to visualize, fine-tune, and compare performance of deep learning models.
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If you cannot find the needed model in Open Model Zoo or want to train your own model, use [OpenVINO Training Extentions](https://github.com/openvinotoolkit/training_extensions) which provide a convenient environment to train deep learning models.
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Useful documents for model preparation:
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* [Model Downloader](@ref omz_tools_downloader) utility
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* [Intel's Pretrained Models (Open Model Zoo)](@ref omz_models_group_intel)
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* [Public Pretrained Models (Open Model Zoo)](@ref omz_models_group_public)
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* [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction)
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* [OpenVINO Training Extentions](https://github.com/openvinotoolkit/training_extensions)
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### Model Conversion
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#### Components: [Model Optimizer](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
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The [Model Optimizer](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) is a cross-platform command-line
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One of the core component of the OpenVINO™ toolkit is the [Model Optimizer](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) a cross-platform command-line
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tool that converts a trained neural network from its source framework to an open-source, nGraph-compatible [Intermediate Representation (IR)](MO_DG/IR_and_opsets.md) for use in inference operations. The Model Optimizer imports models trained in popular frameworks such as Caffe*, TensorFlow*, MXNet*, Kaldi*, and ONNX* and performs a few optimizations to remove excess layers and group operations when possible into simpler, faster graphs.
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If your neural network contains layers that are not in the list of known layers for supported frameworks, you can adjust the conversion and optimization process using [Custom Layers](HOWTO/Custom_Layers_Guide.md).
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Run the [Accuracy Checker utility](@ref omz_tools_accuracy_checker) either against source topologies or against the output representation to evaluate the accuracy of inference. You can also use the Accuracy Checker as a part of the [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction), an official OpenVINO™ graphical interface.
|
||||
|
||||
|
||||
|
||||
Useful documents for model conversion and optimization:
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||||
If your neural network model contains layers that are not in the list of known layers for supported frameworks, you can adjust the conversion and optimization process through use of [Custom Layers](HOWTO/Custom_Layers_Guide.md).
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||||
|
||||
Run the [Accuracy Checker utility](@ref omz_tools_accuracy_checker) either against source topologies or against the output representation to evaluate the accuracy of inference. The Accuracy Checker is also part of the [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction), an integrated web-based performance analysis studio.
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Use the [Post-training Optimization Tool](@ref pot_README) to accelerate the inference of a deep learning model by quantizing it to INT8.
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||||
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Useful documents for model optimization:
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||||
* [Model Optimizer Developer Guide](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
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||||
* [Intermediate Representation and Opsets](MO_DG/IR_and_opsets.md)
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||||
* [Custom Layers Guide](HOWTO/Custom_Layers_Guide.md)
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||||
* [Accuracy Checker utility](@ref omz_tools_accuracy_checker)
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* [Deep Learning Workbench Guide](@ref workbench_docs_Workbench_DG_Introduction)
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||||
* [Post-training Optimization Tool](@ref pot_README)
|
||||
* [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction)
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||||
* [Model Downloader](@ref omz_tools_downloader) utility
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||||
* [Intel's Pretrained Models (Open Model Zoo)](@ref omz_models_group_intel)
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||||
* [Public Pretrained Models (Open Model Zoo)](@ref omz_models_group_public)
|
||||
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||||
### Running and Tuning Inference
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||||
#### Components: [Inference Engine](IE_DG/Deep_Learning_Inference_Engine_DevGuide.md), [Post-training Optimization Tool](@ref pot_README), [Neural Network Compression Framework](https://github.com/openvinotoolkit/nncf)
|
||||
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The other core component of OpenVINO™ is the [Inference Engine](IE_DG/Deep_Learning_Inference_Engine_DevGuide.md), which manages the loading and compiling of the optimized neural network model, runs inference operations on input data, and outputs the results. Inference Engine can execute synchronously or asynchronously, and its plugin architecture manages the appropriate compilations for execution on multiple Intel® devices, including both workhorse CPUs and specialized graphics and video processing platforms (see below, Packaging and Deployment).
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You can use OpenVINO™ Tuning Utilities with the Inference Engine for trial and test inference on your model. The [Benchmark utility](../inference-engine/tools/benchmark_tool/README.md) uses an input model to run iterative tests for throughput or latency measures, and the [Cross Check Utility](../inference-engine/tools/cross_check_tool/README.md) compares performance of differently configured inferences.
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You can use OpenVINO™ Tuning Utilities with the Inference Engine to trial and test inference on your model. The Benchmark utility uses an input model to run iterative tests for throughput or latency measures, and the [Cross Check Utility](../inference-engine/tools/cross_check_tool/README.md) compares performance of differently configured inferences.
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For a full browser-based studio integrating these other key tuning utilities, try the [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction).
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OpenVINO™ toolkit includes a set of [inference code samples](IE_DG/Samples_Overview.md) and [application demos](@ref omz_demos) showing how inference is run and output processed for use in retail environments, classrooms, smart camera applications, and other solutions.
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Use the [Post-training Optimization Tool](@ref pot_README) to accelerate the inference of a deep learning model by quantizing it to INT8. Models from Open Model Zoo can be quantized using the [Model Quantizer utility](https://github.com/openvinotoolkit/open_model_zoo/tree/master/tools/downloader#model-quantizer-usage).
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Besides the [Post-training Optimization Tool](@ref pot_README), the [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf) can be used for model fine-tuning INT8 quantization or even for applying more aggressive compression methods, such as filter pruning, sparsity, and binarization to further speed up model inference and reduce the footprint. In that case the compression algorithms are integrated into your model training pipeline.
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OpenVINO also makes use of open-source and Intel™ tools for traditional graphics processing and performance management. Intel® Media SDK supports accelerated rich-media processing, including transcoding. OpenVINO™ optimizes calls to the rich OpenCV and OpenVX libraries for processing computer vision workloads. And the new DL Streamer integration further accelerates video pipelining and performance.
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Try these key tuning tools in your browser with the [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) intuitive graphical interface.
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Useful documents for inference tuning:
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* [Inference Engine Developer Guide](IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
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* [Inference Engine API References](./api_references.html)
|
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* [Inference Code Samples](IE_DG/Samples_Overview.md)
|
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* [Application Demos](@ref omz_demos)
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* [Post-training Optimization Tool](@ref pot_README)
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* [Low Precision Optimization Guide] (@ref pot_docs_LowPrecisionOptimizationGuide)
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* [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf)
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* [Deep Learning Workbench Guide](@ref workbench_docs_Workbench_DG_Introduction)
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* [Intel Media SDK](https://github.com/Intel-Media-SDK/MediaSDK)
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* [DL Streamer Samples](@ref gst_samples_README)
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@@ -88,8 +67,6 @@ Useful documents for inference tuning:
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* [OpenVX](https://software.intel.com/en-us/openvino-ovx-guide)
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### Packaging and Deployment
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#### Components: [Deployment Manager](./install_guides/deployment-manager-tool.md)
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The Intel Distribution of OpenVINO™ toolkit outputs optimized inference runtimes for the following devices:
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* Intel® CPUs
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* Intel® Processor Graphics
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@@ -98,13 +75,12 @@ The Intel Distribution of OpenVINO™ toolkit outputs optimized inference runtim
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The Inference Engine's plug-in architecture can be extended to meet other specialized needs. [Deployment Manager](./install_guides/deployment-manager-tool.md) is a Python* command-line tool that assembles the tuned model, IR files, your application, and required dependencies into a runtime package for your target device. It outputs packages for CPU, GPU, and VPU on Linux* and Windows*, and Neural Compute Stick-optimized packages with Linux.
|
||||
|
||||
|
||||
|
||||
* [Inference Engine Integration Workflow](IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
* [Inference Engine API References](./api_references.html)
|
||||
* [Inference Engine Plug-in Developer Guide](./ie_plugin_api/index.html)
|
||||
* [Deployment Manager Guide](./install_guides/deployment-manager-tool.md)
|
||||
|
||||
|
||||
## OpenVINO™ Toolkit Components
|
||||
|
||||
Intel® Distribution of OpenVINO™ toolkit includes the following components:
|
||||
|
||||
@@ -27,8 +27,9 @@ Prebuilt images are available on:
|
||||
|
||||
## Build a Docker* Image
|
||||
|
||||
You can use [available Dockerfiles](https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles) or generate a Dockerfile with your setting via [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci). The Framework can generate a Dockerfile, build, test, and deploy an image with the Intel® Distribution of OpenVINO™ toolkit.
|
||||
You can also try our [Tutorials](https://github.com/openvinotoolkit/docker_ci/tree/master/docs/tutorials) which demonstrate the usage of OpenVINO™ Docker containers.
|
||||
You can use [available Dockerfiles](https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles) or generate a Dockerfile with your setting via [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci).
|
||||
The Framework can generate a Dockerfile, build, test, and deploy an image with the Intel® Distribution of OpenVINO™ toolkit.
|
||||
You can also try our [Tutorials](https://github.com/openvinotoolkit/docker_ci/tree/master/docs/tutorials) which demonstrate the usage of Docker containers with Intel® Distribution of OpenVINO™ toolkit. You can find device specific steps to configure an Intel® Distribution of OpenVINO™ toolkit Dockerfile below.
|
||||
|
||||
## Use Docker* Image for CPU
|
||||
|
||||
@@ -36,34 +37,40 @@ You can also try our [Tutorials](https://github.com/openvinotoolkit/docker_ci/tr
|
||||
- All instructions that are available to host process available for process in container, including, for example, AVX2, AVX512. No restrictions.
|
||||
- Docker\* does not use virtualization or emulation. The process in Docker* is just a regular Linux process, but it is isolated from external world on kernel level. Performance penalty is small.
|
||||
|
||||
### <a name="building-for-cpu"></a>Build a Docker* Image for CPU
|
||||
### <a name="configuring-for-cpu"></a>Configure a Docker* Image for CPU
|
||||
|
||||
You can use [available Dockerfiles](https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles) or generate a Dockerfile with your setting via [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci) for Intel® Distribution of OpenVINO™ toolkit.
|
||||
The Framework can generate a Dockerfile, build, test, and deploy an image with the Intel® Distribution of OpenVINO™ toolkit.
|
||||
You don't need to do specific steps to configure an Intel® Distribution of OpenVINO™ toolkit Dockerfile for CPU. You can use [available Dockerfiles](https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles) or generate a Dockerfile with your setting via [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci).
|
||||
|
||||
### Run the Docker* Image for CPU
|
||||
|
||||
Run the image with the following command:
|
||||
|
||||
```sh
|
||||
docker run -it --rm <image_name>
|
||||
```
|
||||
|
||||
## Use a Docker* Image for GPU
|
||||
### Build a Docker* Image for GPU
|
||||
|
||||
### Configure a Docker* Image for GPU
|
||||
|
||||
> **NOTE**: Only Intel® integrated graphics are supported.
|
||||
|
||||
**Prerequisites:**
|
||||
|
||||
- GPU is not available in container by default, you must attach it to the container.
|
||||
- Kernel driver must be installed on the host.
|
||||
- Intel® OpenCL™ runtime package must be included into the container.
|
||||
- In the container, non-root user must be in the `video` and `render` groups. To add a user to the render group, follow the [Configuration Guide for the Intel® Graphics Compute Runtime for OpenCL™ on Ubuntu* 20.04](https://github.com/openvinotoolkit/docker_ci/blob/master/configure_gpu_ubuntu20.md).
|
||||
- In the container, non-root user must be in the `video` and `render` groups. To add a user to the render group, follow the [Configuration Guide for the Intel® Graphics Compute Runtime for OpenCL™ on Ubuntu* 20.04](https://github.com/openvinotoolkit/docker_ci/blob/master/configure_gpu_ubuntu20.md).
|
||||
|
||||
|
||||
Before building a Docker* image on GPU, add the following commands to a Dockerfile:
|
||||
To configure a OpenVINO Docker* image with access to GPU, add the following commands to a Dockerfile:
|
||||
|
||||
**Ubuntu 18.04/20.04**:
|
||||
|
||||
```sh
|
||||
WORKDIR /tmp/opencl
|
||||
RUN useradd -ms /bin/bash -G video,users openvino && \
|
||||
chown openvino -R /home/openvino
|
||||
|
||||
RUN apt-get update && \
|
||||
apt-get install -y --no-install-recommends ocl-icd-libopencl1 && \
|
||||
rm -rf /var/lib/apt/lists/* && \
|
||||
@@ -76,7 +83,24 @@ RUN apt-get update && \
|
||||
ldconfig && \
|
||||
rm /tmp/opencl
|
||||
```
|
||||
|
||||
or you can use the installation script `install_NEO_OCL_driver.sh` if you previously installed OpenVINO in the Dockerfile, where `INTEL_OPENCL` is the variable to store the default version of Intel® Graphics Compute Runtime for OpenCL™ Driver:
|
||||
|
||||
```sh
|
||||
WORKDIR /tmp/opencl
|
||||
RUN useradd -ms /bin/bash -G video,users openvino && \
|
||||
chown openvino -R /home/openvino
|
||||
|
||||
# Please use `20.35.17767` for 10th generation Intel® Core™ processor (formerly Ice Lake) or 11th generation Intel® Core™ processor (formerly Tiger Lake)
|
||||
ARG INTEL_OPENCL=19.41.14441
|
||||
|
||||
WORKDIR ${INTEL_OPENVINO_DIR}/install_dependencies
|
||||
RUN ./install_NEO_OCL_driver.sh --no_numa -y --install_driver ${INTEL_OPENCL} && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
```
|
||||
|
||||
**CentOS 7/RHEL 8**:
|
||||
|
||||
```sh
|
||||
WORKDIR /tmp/opencl
|
||||
RUN useradd -ms /bin/bash -G video,users openvino && \
|
||||
@@ -98,9 +122,27 @@ RUN yum update -y && yum install -y https://dl.fedoraproject.org/pub/epel/epel-r
|
||||
yum remove -y epel-release
|
||||
```
|
||||
|
||||
or you can use the installation script `install_NEO_OCL_driver.sh` if you previously installed OpenVINO in the Dockerfile, where `INTEL_OPENCL` is the variable to store the default version of Intel® Graphics Compute Runtime for OpenCL™ Driver:
|
||||
|
||||
```sh
|
||||
WORKDIR /tmp/opencl
|
||||
RUN useradd -ms /bin/bash -G video,users openvino && \
|
||||
chown openvino -R /home/openvino
|
||||
RUN groupmod -g 44 video
|
||||
|
||||
# Please use `20.35.17767` for 10th generation Intel® Core™ processor (formerly Ice Lake) or 11th generation Intel® Core™ processor (formerly Tiger Lake)
|
||||
ARG INTEL_OPENCL=19.41.14441
|
||||
|
||||
WORKDIR ${INTEL_OPENVINO_DIR}/install_dependencies
|
||||
RUN ./install_NEO_OCL_driver.sh --no_numa -y --install_driver ${INTEL_OPENCL} && \
|
||||
yum clean all && rm -rf /var/cache/yum && \
|
||||
yum remove -y epel-release
|
||||
```
|
||||
|
||||
### Run the Docker* Image for GPU
|
||||
|
||||
To make GPU available in the container, attach the GPU to the container using `--device /dev/dri` option and run the container:
|
||||
|
||||
```sh
|
||||
docker run -it --rm --device /dev/dri <image_name>
|
||||
```
|
||||
@@ -108,7 +150,7 @@ docker run -it --rm --device /dev/dri <image_name>
|
||||
|
||||
## Use a Docker* Image for Intel® Neural Compute Stick 2
|
||||
|
||||
### Build and Run the Docker* Image for Intel® Neural Compute Stick 2
|
||||
### Configure and Run the Docker* Image for Intel® Neural Compute Stick 2
|
||||
|
||||
**Known limitations:**
|
||||
|
||||
@@ -118,7 +160,8 @@ docker run -it --rm --device /dev/dri <image_name>
|
||||
|
||||
Use one of the following options as **Possible solutions for Intel® Neural Compute Stick 2:**
|
||||
|
||||
#### Option #1
|
||||
#### Option 1
|
||||
|
||||
1. Get rid of UDEV by rebuilding `libusb` without UDEV support in the Docker* image (add the following commands to a `Dockerfile`):
|
||||
- **Ubuntu 18.04/20.04**:
|
||||
```sh
|
||||
@@ -192,22 +235,23 @@ RUN /usr/bin/install -c -m 644 libusb-1.0.pc '/usr/local/lib/pkgconfig' && \
|
||||
docker run -it --rm --device-cgroup-rule='c 189:* rmw' -v /dev/bus/usb:/dev/bus/usb <image_name>
|
||||
```
|
||||
|
||||
#### Option #2
|
||||
#### Option 2
|
||||
Run container in the privileged mode, enable the Docker network configuration as host, and mount all devices to the container:
|
||||
```sh
|
||||
docker run -it --rm --privileged -v /dev:/dev --network=host <image_name>
|
||||
```
|
||||
> **NOTES**:
|
||||
>
|
||||
> - It is not secure.
|
||||
> - Conflicts with Kubernetes* and other tools that use orchestration and private networks may occur.
|
||||
|
||||
## Use a Docker* Image for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
|
||||
|
||||
### Build Docker* Image for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
|
||||
### Configure Docker* Image for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
|
||||
To use the Docker container for inference on Intel® Vision Accelerator Design with Intel® Movidius™ VPUs:
|
||||
|
||||
1. Set up the environment on the host machine, that is going to be used for running Docker*.
|
||||
It is required to execute `hddldaemon`, which is responsible for communication between the HDDL plugin and the board.
|
||||
1. Set up the environment on the host machine, that is going to be used for running Docker*.
|
||||
It is required to execute `hddldaemon`, which is responsible for communication between the HDDL plugin and the board.
|
||||
To learn how to set up the environment (the OpenVINO package or HDDL package must be pre-installed), see [Configuration guide for HDDL device](https://github.com/openvinotoolkit/docker_ci/blob/master/install_guide_vpu_hddl.md) or [Configuration Guide for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs](installing-openvino-linux-ivad-vpu.md).
|
||||
2. Prepare the Docker* image (add the following commands to a Dockerfile).
|
||||
- **Ubuntu 18.04**:
|
||||
@@ -255,50 +299,57 @@ $HDDL_INSTALL_DIR/hddldaemon
|
||||
### Run the Docker* Image for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
|
||||
|
||||
To run the built Docker* image for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, use the following command:
|
||||
|
||||
```sh
|
||||
docker run -it --rm --device=/dev/ion:/dev/ion -v /var/tmp:/var/tmp <image_name>
|
||||
```
|
||||
|
||||
> **NOTES**:
|
||||
>
|
||||
> - The device `/dev/ion` need to be shared to be able to use ion buffers among the plugin, `hddldaemon` and the kernel.
|
||||
>
|
||||
> - The device `/dev/ion` needs to be shared to be able to use ion buffers among the plugin, `hddldaemon` and the kernel.
|
||||
> - Since separate inference tasks share the same HDDL service communication interface (the service creates mutexes and a socket file in `/var/tmp`), `/var/tmp` needs to be mounted and shared among them.
|
||||
|
||||
In some cases, the ion driver is not enabled (for example, due to a newer kernel version or iommu incompatibility). `lsmod | grep myd_ion` returns empty output. To resolve, use the following command:
|
||||
In some cases, the ion driver is not enabled (for example, due to a newer kernel version or iommu (Input-Output Memory Management Unit) incompatibility). `lsmod | grep myd_ion` returns empty output. To resolve, use the following command:
|
||||
|
||||
```sh
|
||||
docker run -it --rm --net=host -v /var/tmp:/var/tmp –ipc=host <image_name>
|
||||
docker run -it --rm --net=host -v /var/tmp:/var/tmp –-ipc=host <image_name>
|
||||
```
|
||||
|
||||
> **NOTES**:
|
||||
>
|
||||
> - When building docker images, create a user in the docker file that has the same UID and GID as the user which runs hddldaemon on the host.
|
||||
> - Run the application in the docker with this user.
|
||||
>
|
||||
> - When building Docker images, create a user in the Dockerfile that has the same UID(User Identifier) and GID(Group Identifier) as the user which runs hddldaemon on the host.
|
||||
> - Run the application in the Docker image with this user.
|
||||
> - Alternatively, you can start hddldaemon with the root user on host, but this approach is not recommended.
|
||||
|
||||
### Run Demos in the Docker* Image
|
||||
### Run Demos in the Docker* Image
|
||||
|
||||
To run the Security Barrier Camera Demo on a specific inference device, run the following commands with the root privileges (additional third-party dependencies will be installed):
|
||||
|
||||
**CPU**:
|
||||
|
||||
```sh
|
||||
docker run -itu root:root --rm --device=/dev/ion:/dev/ion -v /var/tmp:/var/tmp --device /dev/dri:/dev/dri --device-cgroup-rule='c 189:* rmw' -v /dev/bus/usb:/dev/bus/usb <image_name>
|
||||
docker run -itu root:root --rm <image_name>
|
||||
/bin/bash -c "apt update && apt install sudo && deployment_tools/demo/demo_security_barrier_camera.sh -d CPU -sample-options -no_show"
|
||||
```
|
||||
|
||||
**GPU**:
|
||||
|
||||
```sh
|
||||
docker run -itu root:root --rm --device=/dev/ion:/dev/ion -v /var/tmp:/var/tmp --device /dev/dri:/dev/dri --device-cgroup-rule='c 189:* rmw' -v /dev/bus/usb:/dev/bus/usb <image_name>
|
||||
docker run -itu root:root --rm --device /dev/dri:/dev/dri <image_name>
|
||||
/bin/bash -c "apt update && apt install sudo && deployment_tools/demo/demo_security_barrier_camera.sh -d GPU -sample-options -no_show"
|
||||
```
|
||||
|
||||
**MYRIAD**:
|
||||
|
||||
```sh
|
||||
docker run -itu root:root --rm --device=/dev/ion:/dev/ion -v /var/tmp:/var/tmp --device /dev/dri:/dev/dri --device-cgroup-rule='c 189:* rmw' -v /dev/bus/usb:/dev/bus/usb <image_name>
|
||||
docker run -itu root:root --rm --device-cgroup-rule='c 189:* rmw' -v /dev/bus/usb:/dev/bus/usb <image_name>
|
||||
/bin/bash -c "apt update && apt install sudo && deployment_tools/demo/demo_security_barrier_camera.sh -d MYRIAD -sample-options -no_show"
|
||||
```
|
||||
|
||||
**HDDL**:
|
||||
|
||||
```sh
|
||||
docker run -itu root:root --rm --device=/dev/ion:/dev/ion -v /var/tmp:/var/tmp --device /dev/dri:/dev/dri --device-cgroup-rule='c 189:* rmw' -v /dev/bus/usb:/dev/bus/usb <image_name>
|
||||
docker run -itu root:root --rm --device=/dev/ion:/dev/ion -v /var/tmp:/var/tmp <image_name>
|
||||
/bin/bash -c "apt update && apt install sudo && deployment_tools/demo/demo_security_barrier_camera.sh -d HDDL -sample-options -no_show"
|
||||
```
|
||||
|
||||
@@ -312,12 +363,12 @@ For instructions for previous releases with FPGA Support, see documentation for
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
If you got proxy issues, please setup proxy settings for Docker. See the Proxy section in the [Install the DL Workbench from Docker Hub* ](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) topic.
|
||||
If you got proxy issues, please setup proxy settings for Docker. See the Proxy section in the [Install the DL Workbench from Docker Hub* ](@ref workbench_docs_Workbench_DG_Run_Locally) topic.
|
||||
|
||||
## Additional Resources
|
||||
|
||||
* [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci) for Intel® Distribution of OpenVINO™ toolkit. The Framework can generate a Dockerfile, build, test, and deploy an image with the Intel® Distribution of OpenVINO™ toolkit. You can reuse available Dockerfiles, add your layer and customize the image of OpenVINO™ for your needs.
|
||||
- [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci) for Intel® Distribution of OpenVINO™ toolkit. The Framework can generate a Dockerfile, build, test, and deploy an image with the Intel® Distribution of OpenVINO™ toolkit. You can reuse available Dockerfiles, add your layer and customize the image of OpenVINO™ for your needs.
|
||||
|
||||
* Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
|
||||
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
|
||||
|
||||
* Intel® Neural Compute Stick 2 Get Started: [https://software.intel.com/en-us/neural-compute-stick/get-started](https://software.intel.com/en-us/neural-compute-stick/get-started)
|
||||
- Intel® Neural Compute Stick 2 Get Started: [https://software.intel.com/en-us/neural-compute-stick/get-started](https://software.intel.com/en-us/neural-compute-stick/get-started)
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
The Intel® Distribution of OpenVINO™ toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNN), the toolkit extends computer vision (CV) workloads across Intel® hardware, maximizing performance. The Intel® Distribution of OpenVINO™ toolkit includes the Intel® Deep Learning Deployment Toolkit.
|
||||
|
||||
This guide provides device specifics for a Docker* image creation with Intel® Distribution of OpenVINO™ toolkit for Linux* and its further usage.
|
||||
This guide provides device specifics for a Docker* image creation with Intel® Distribution of OpenVINO™ toolkit for Windows* and its further usage.
|
||||
|
||||
## System Requirements
|
||||
|
||||
@@ -21,43 +21,46 @@ Prebuilt images are available on [Docker Hub](https://hub.docker.com/u/openvino)
|
||||
|
||||
## Build a Docker* Image
|
||||
|
||||
You can use [available Dockerfiles](https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles) or generate a Dockerfile with your setting via [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci). The Framework can generate a Dockerfile, build, test, and deploy an image with the Intel® Distribution of OpenVINO™ toolkit.
|
||||
You can use [available Dockerfiles](https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles) or generate a Dockerfile with your setting via [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci).
|
||||
The Framework can generate a Dockerfile, build, test, and deploy an image with the Intel® Distribution of OpenVINO™ toolkit. You can find device specific steps to configure an Intel® Distribution of OpenVINO™ toolkit Dockerfile below.
|
||||
|
||||
## Build and Run the Docker* Image for CPU
|
||||
## Configure and Run the Docker* Image for CPU
|
||||
|
||||
## Install Additional Dependencies
|
||||
|
||||
### Install CMake
|
||||
|
||||
To add CMake to the image, add the following commands to the Dockerfile:
|
||||
~~~
|
||||
|
||||
```bat
|
||||
RUN powershell.exe -Command `
|
||||
Invoke-WebRequest -URI https://cmake.org/files/v3.14/cmake-3.14.7-win64-x64.msi -OutFile %TMP%\\cmake-3.14.7-win64-x64.msi ; `
|
||||
Start-Process %TMP%\\cmake-3.14.7-win64-x64.msi -ArgumentList '/quiet /norestart' -Wait ; `
|
||||
Remove-Item %TMP%\\cmake-3.14.7-win64-x64.msi -Force
|
||||
|
||||
RUN SETX /M PATH "C:\Program Files\CMake\Bin;%PATH%"
|
||||
~~~
|
||||
In case of proxy issues, please add the `ARG HTTPS_PROXY` and `-Proxy %%HTTPS_PROXY%` settings to the `powershell.exe` command to the Dockerfile. Then build a docker image:
|
||||
~~~
|
||||
```
|
||||
|
||||
In case of proxy issues, please add the `ARG HTTPS_PROXY` and `-Proxy %%HTTPS_PROXY%` settings to the `powershell.exe` command to the Dockerfile. Then build a Docker image:
|
||||
|
||||
```bat
|
||||
docker build . -t <image_name> `
|
||||
--build-arg HTTPS_PROXY=<https://your_proxy_server:port>
|
||||
~~~
|
||||
```
|
||||
|
||||
### Install Microsoft Visual Studio* Build Tools
|
||||
|
||||
You can add Microsoft Visual Studio Build Tools* to a Windows* OS Docker image. Available options are to use offline installer for Build Tools
|
||||
(follow the [Instruction for the offline installer](https://docs.microsoft.com/en-us/visualstudio/install/create-an-offline-installation-of-visual-studio?view=vs-2019)) or
|
||||
to use the online installer for Build Tools (follow [Instruction for the online installer](https://docs.microsoft.com/en-us/visualstudio/install/build-tools-container?view=vs-2019)).
|
||||
Microsoft Visual Studio Build Tools* are licensed as a supplement your existing Microsoft Visual Studio* license.
|
||||
You can add Microsoft Visual Studio Build Tools* to a Windows* OS Docker image using the [offline](https://docs.microsoft.com/en-us/visualstudio/install/create-an-offline-installation-of-visual-studio?view=vs-2019) or [online](https://docs.microsoft.com/en-us/visualstudio/install/build-tools-container?view=vs-2019) installers for Build Tools.
|
||||
Microsoft Visual Studio Build Tools* are licensed as a supplement your existing Microsoft Visual Studio* license.
|
||||
Any images built with these tools should be for your personal use or for use in your organization in accordance with your existing Visual Studio* and Windows* licenses.
|
||||
|
||||
To add MSBuild 2019 to the image, add the following commands to the Dockerfile:
|
||||
~~~
|
||||
|
||||
```bat
|
||||
RUN powershell.exe -Command Invoke-WebRequest -URI https://aka.ms/vs/16/release/vs_buildtools.exe -OutFile %TMP%\\vs_buildtools.exe
|
||||
|
||||
RUN %TMP%\\vs_buildtools.exe --quiet --norestart --wait --nocache `
|
||||
--installPath "C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools" `
|
||||
--installPath "C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools" `
|
||||
--add Microsoft.VisualStudio.Workload.MSBuildTools `
|
||||
--add Microsoft.VisualStudio.Workload.UniversalBuildTools `
|
||||
--add Microsoft.VisualStudio.Workload.VCTools --includeRecommended `
|
||||
@@ -65,35 +68,38 @@ RUN %TMP%\\vs_buildtools.exe --quiet --norestart --wait --nocache `
|
||||
--remove Microsoft.VisualStudio.Component.Windows10SDK.10586 `
|
||||
--remove Microsoft.VisualStudio.Component.Windows10SDK.14393 `
|
||||
--remove Microsoft.VisualStudio.Component.Windows81SDK || IF "%ERRORLEVEL%"=="3010" EXIT 0 && powershell set-executionpolicy remotesigned
|
||||
~~~
|
||||
In case of proxy issues, please use an offline installer for Build Tools (follow [Instruction for the offline installer](https://docs.microsoft.com/en-us/visualstudio/install/create-an-offline-installation-of-visual-studio?view=vs-2019).
|
||||
```
|
||||
|
||||
In case of proxy issues, please use the [offline installer for Build Tools](https://docs.microsoft.com/en-us/visualstudio/install/create-an-offline-installation-of-visual-studio?view=vs-2019).
|
||||
|
||||
## Run the Docker* Image for CPU
|
||||
|
||||
To install the OpenVINO toolkit from the prepared Docker image, run the image with the following command:
|
||||
~~~
|
||||
To start the interactive session, run the following command allows inference on the CPU:
|
||||
|
||||
```bat
|
||||
docker run -it --rm <image_name>
|
||||
~~~
|
||||
```
|
||||
|
||||
If you want to try some demos then run image with the root privileges (some additional 3-rd party dependencies will be installed):
|
||||
~~~
|
||||
docker run -itu ContainerAdministrator --rm <image_name> cmd /S /C "cd deployment_tools\demo && demo_security_barrier_camera.bat -d CPU -sample-options -no_show"
|
||||
~~~
|
||||
|
||||
## Build and Run the Docker* Image for GPU
|
||||
```bat
|
||||
docker run -itu ContainerAdministrator --rm <image_name> cmd /S /C "cd deployment_tools\demo && demo_security_barrier_camera.bat -d CPU -sample-options -no_show"
|
||||
```
|
||||
|
||||
## Configure and Run the Docker* Image for GPU
|
||||
|
||||
GPU Acceleration in Windows containers feature requires to meet Windows host, OpenVINO toolkit and Docker* requirements:
|
||||
|
||||
* [Windows requirements](https://docs.microsoft.com/en-us/virtualization/windowscontainers/deploy-containers/gpu-acceleration):
|
||||
* The container host must be running Windows Server 2019 or Windows 10 of version 1809 or higher.
|
||||
* The container base image must be `mcr.microsoft.com/windows:1809` or higher. Windows Server Core and Nano Server container images are not currently supported.
|
||||
* The container host must be running Docker Engine 19.03 or higher.
|
||||
* The container host must have GPU running display drivers of version WDDM 2.5 or higher.
|
||||
* [OpenVINO™ GPU requirement](https://docs.openvinotoolkit.org/latest/openvino_docs_install_guides_installing_openvino_windows.html#Install-GPU):
|
||||
* Intel Graphics Driver for Windows of version 15.65 or higher.
|
||||
* [Docker isolation mode requirement](https://docs.microsoft.com/en-us/virtualization/windowscontainers/manage-containers/hyperv-container):
|
||||
* Windows host and container version tags must match.
|
||||
* [Windows host and container isolation process support](https://docs.microsoft.com/en-us/virtualization/windowscontainers/deploy-containers/version-compatibility)
|
||||
- [Windows requirements](https://docs.microsoft.com/en-us/virtualization/windowscontainers/deploy-containers/gpu-acceleration):
|
||||
- The container host must be running Windows Server 2019 or Windows 10 of version 1809 or higher.
|
||||
- The container base image must be `mcr.microsoft.com/windows:1809` or higher. Windows Server Core and Nano Server container images are not currently supported.
|
||||
- The container host must be running Docker Engine 19.03 or higher.
|
||||
- The container host must have GPU running display drivers of version WDDM 2.5 or higher.
|
||||
- [OpenVINO™ GPU requirement](https://docs.openvinotoolkit.org/latest/openvino_docs_install_guides_installing_openvino_windows.html#Install-GPU):
|
||||
- Intel Graphics Driver for Windows of version 15.65 or higher.
|
||||
- [Docker isolation mode requirement](https://docs.microsoft.com/en-us/virtualization/windowscontainers/manage-containers/hyperv-container):
|
||||
- Windows host and container version tags must match.
|
||||
- [Windows host and container isolation process support](https://docs.microsoft.com/en-us/virtualization/windowscontainers/deploy-containers/version-compatibility)
|
||||
|
||||
## Build a Docker* Image for Your Host System
|
||||
|
||||
@@ -101,50 +107,49 @@ GPU Acceleration in Windows containers feature requires to meet Windows host, Op
|
||||
2. Check your [Windows host and container isolation process compatibility](https://docs.microsoft.com/en-us/virtualization/windowscontainers/deploy-containers/version-compatibility).
|
||||
3. Find the appropriate Windows container base image on [DockerHub*](https://hub.docker.com/_/microsoft-windows) and set up your host/container version in the `FROM` Dockerfile instruction.
|
||||
For example, in [openvino_c_dev_2021.dockerfile](https://github.com/openvinotoolkit/docker_ci/blob/master/dockerfiles/winserver2019/openvino_c_dev_2021.dockerfile), change:
|
||||
~~~
|
||||
```bat
|
||||
FROM mcr.microsoft.com/windows/servercore:ltsc2019 AS ov_base
|
||||
~~~
|
||||
to
|
||||
~~~
|
||||
```
|
||||
to:
|
||||
```bat
|
||||
FROM mcr.microsoft.com/windows:20H2
|
||||
~~~
|
||||
4. Build the Docker image
|
||||
~~~
|
||||
```
|
||||
4. Build the Docker image
|
||||
```bat
|
||||
docker build --build-arg package_url=<OpenVINO pkg> -f <Dockerfile> -t <image_name> .
|
||||
~~~
|
||||
```
|
||||
5. Copy `OpenCL.dll` from your `C:\Windows\System32` host folder to any `temp` directory:
|
||||
~~~
|
||||
```bat
|
||||
mkdir C:\tmp
|
||||
copy C:\Windows\System32\OpenCL.dll C:\tmp
|
||||
~~~
|
||||
|
||||
```
|
||||
|
||||
## Run the Docker* Image for GPU
|
||||
|
||||
1. To try inference on a GPU, run the image with the following command:
|
||||
~~~
|
||||
1. To try inference on a GPU, run the image with the following command:
|
||||
```bat
|
||||
docker run -it --rm -u ContainerAdministrator --isolation process --device class/5B45201D-F2F2-4F3B-85BB-30FF1F953599 -v C:\Windows\System32\DriverStore\FileRepository\iigd_dch.inf_amd64_518f2921ba495409:C:\Windows\System32\DriverStore\FileRepository\iigd_dch.inf_amd64_518f2921ba495409 -v C:\tmp:C:\tmp <image_name>
|
||||
~~~
|
||||
where
|
||||
* `--device class/5B45201D-F2F2-4F3B-85BB-30FF1F953599` is a reserved interface class GUID for a GPU device.
|
||||
* `C:\Windows\System32\DriverStore\FileRepository\iigd_dch.inf_amd64_518f2921ba495409` is the path to OpenCL driver home directory. To find it on your PC, run the `C:\Windows\System32\DriverStore\FileRepository\iigd_dch.inf_amd64_*` regular expression.
|
||||
* `C:\tmp` is the folder with the copy of `OpenCL.dll` from your `C:\Windows\System32` host folder.
|
||||
|
||||
```
|
||||
where
|
||||
- `--device class/5B45201D-F2F2-4F3B-85BB-30FF1F953599` is a reserved interface class GUID for a GPU device.
|
||||
- `C:\Windows\System32\DriverStore\FileRepository\iigd_dch.inf_amd64_518f2921ba495409` is the path to OpenCL driver home directory. To find it on your PC, run the `C:\Windows\System32\DriverStore\FileRepository\iigd_dch.inf_amd64_*` regular expression.
|
||||
- `C:\tmp` is the folder with the copy of `OpenCL.dll` from your `C:\Windows\System32` host folder.
|
||||
2. Copy `OpenCL.dll` to the `C:\Windows\System32` folder inside the container and set appropriate registry entry. Now you can run inference on a GPU device:
|
||||
~~~
|
||||
```bat
|
||||
copy C:\tmp\OpenCL.dll C:\Windows\System32\ && reg add "HKLM\SOFTWARE\Khronos\OpenCL\Vendors" /v "C:\Windows\System32\DriverStore\FileRepository\iigd_dch.inf_amd64_518f2921ba495409\ocl\bin\x64\intelocl64.dll" /t REG_DWORD /d 0
|
||||
~~~
|
||||
3. For example, run the `demo_security_barrier_camera` demo with the command below:
|
||||
~~~
|
||||
```
|
||||
3. For example, run the `demo_security_barrier_camera` demo with the command below:
|
||||
```bat
|
||||
cd bin && setupvars.bat && cd ../ && cd deployment_tools\demo && demo_security_barrier_camera.bat -d GPU -sample-options -no_show
|
||||
~~~
|
||||
```
|
||||
> **NOTE**: Addittional third-party dependencies will be installed.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
If you got proxy issues, please setup proxy settings for Docker. See the Proxy section in the [Install the DL Workbench from Docker Hub* ](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) topic.
|
||||
If you got proxy issues, please setup proxy settings for Docker. See the Proxy section in the [Install the DL Workbench from Docker Hub* ](@ref workbench_docs_Workbench_DG_Run_Locally) topic.
|
||||
|
||||
## Additional Resources
|
||||
|
||||
* [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci) for Intel® Distribution of OpenVINO™ toolkit. The Framework can generate a Dockerfile, build, test, and deploy an image with the Intel® Distribution of OpenVINO™ toolkit. You can reuse available Dockerfiles, add your layer and customize the image of OpenVINO™ for your needs.
|
||||
- [DockerHub CI Framework](https://github.com/openvinotoolkit/docker_ci) for Intel® Distribution of OpenVINO™ toolkit. The Framework can generate a Dockerfile, build, test, and deploy an image with the Intel® Distribution of OpenVINO™ toolkit. You can reuse available Dockerfiles, add your layer and customize the image of OpenVINO™ for your needs.
|
||||
|
||||
* Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
|
||||
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
|
||||
@@ -12,7 +12,7 @@ is only accessible from the machine the Docker container is built on:
|
||||
application are accessible only from the `localhost` by default.
|
||||
|
||||
* When using `docker run` to [start the DL Workbench from Docker
|
||||
Hub](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub), limit connections for the host IP 127.0.0.1.
|
||||
Hub](@ref workbench_docs_Workbench_DG_Run_Locally), limit connections for the host IP 127.0.0.1.
|
||||
For example, limit the connections for the host IP to the port `5665` with the `-p
|
||||
127.0.0.1:5665:5665` command . Refer to [Container
|
||||
networking](https://docs.docker.com/config/containers/container-networking/#published-ports) for
|
||||
|
||||
@@ -36,7 +36,7 @@ To build the sample, please use instructions available at [Build the Sample Appl
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
> **NOTES**:
|
||||
@@ -94,7 +94,7 @@ This sample is an API example, for any performance measurements please use the d
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
|
||||
[ie_core_create]:https://docs.openvinotoolkit.org/latest/ie_c_api/group__Core.html#gaab73c7ee3704c742eaac457636259541
|
||||
|
||||
@@ -35,7 +35,7 @@ To build the sample, please use instructions available at [Build the Sample Appl
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
The sample accepts an uncompressed image in the NV12 color format. To run the sample, you need to
|
||||
@@ -109,7 +109,7 @@ This sample is an API example, for any performance measurements please use the d
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
|
||||
[ie_network_set_color_format]:https://docs.openvinotoolkit.org/latest/ie_c_api/group__Network.html#ga85f3251f1f7b08507c297e73baa58969
|
||||
|
||||
@@ -42,7 +42,7 @@ To build the sample, please use instructions available at [Build the Sample Appl
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
Running the application with the `-h` option yields the following usage message:
|
||||
@@ -153,7 +153,7 @@ This sample is an API example, for any performance measurements please use the d
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
|
||||
[ie_infer_request_infer_async]:https://docs.openvinotoolkit.org/latest/ie_c_api/group__InferRequest.html#gad2351010e292b6faec959a3d5a8fb60e
|
||||
|
||||
@@ -68,7 +68,7 @@ Options:
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
> **NOTES**:
|
||||
@@ -148,7 +148,7 @@ The sample application logs each step in a standard output stream and outputs to
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
|
||||
[IECore]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html
|
||||
|
||||
@@ -57,7 +57,7 @@ Options:
|
||||
```
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
> **NOTES**:
|
||||
@@ -119,7 +119,7 @@ The sample application logs each step in a standard output stream and outputs to
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
|
||||
[IECore]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html
|
||||
|
||||
@@ -65,7 +65,7 @@ Options:
|
||||
```
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
> **NOTES**:
|
||||
@@ -116,7 +116,7 @@ The sample application logs each step in a standard output stream and creates an
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
|
||||
[IECore]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html
|
||||
|
||||
@@ -130,7 +130,7 @@ The sample application logs each step in a standard output stream and outputs to
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
|
||||
[IECore]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html
|
||||
|
||||
@@ -67,7 +67,7 @@ Options:
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
> **NOTES**:
|
||||
@@ -117,7 +117,7 @@ The sample application logs each step in a standard output stream and creates an
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
|
||||
[IECore]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html
|
||||
|
||||
@@ -193,7 +193,7 @@ The sample application logs each step in a standard output stream.
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
|
||||
[IENetwork.batch_size]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IENetwork.html#a79a647cb1b49645616eaeb2ca255ef2e
|
||||
|
||||
@@ -79,7 +79,7 @@ Options:
|
||||
```
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
> **NOTES**:
|
||||
@@ -129,7 +129,7 @@ The sample application logs each step in a standard output stream and creates an
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
|
||||
[IECore]:https://docs.openvinotoolkit.org/latest/ie_python_api/classie__api_1_1IECore.html
|
||||
|
||||
@@ -47,7 +47,7 @@ To build the sample, please use instructions available at [Build the Sample Appl
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
> **NOTES**:
|
||||
@@ -166,5 +166,5 @@ classid probability
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
@@ -36,7 +36,7 @@ To build the sample, please use instructions available at [Build the Sample Appl
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
> **NOTES**:
|
||||
@@ -94,5 +94,5 @@ This sample is an API example, for any performance measurements please use the d
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
@@ -37,7 +37,7 @@ To build the sample, please use instructions available at [Build the Sample Appl
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
The sample accepts an uncompressed image in the NV12 color format. To run the sample, you need to
|
||||
@@ -115,5 +115,5 @@ This sample is an API example, for any performance measurements please use the d
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
@@ -38,7 +38,7 @@ To build the sample, please use instructions available at [Build the Sample Appl
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
> **NOTES**:
|
||||
@@ -93,5 +93,5 @@ This sample is an API example, for any performance measurements please use the d
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
@@ -38,7 +38,7 @@ To build the sample, please use instructions available at [Build the Sample Appl
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
Running the application with the `-h` option yields the following usage message:
|
||||
@@ -146,5 +146,5 @@ Parsing input parameters
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
@@ -246,5 +246,5 @@ All of mentioned files can be downloaded from [https://storage.openvinotoolkit.o
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
@@ -37,7 +37,7 @@ To build the sample, please use instructions available at [Build the Sample Appl
|
||||
|
||||
To run the sample, you need specify a model and image:
|
||||
|
||||
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
|
||||
- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
|
||||
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
|
||||
|
||||
Running the application with the `-h` option yields the following usage message:
|
||||
@@ -134,5 +134,5 @@ The sample application logs each step in a standard output stream and creates an
|
||||
|
||||
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
|
||||
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
|
||||
- [Model Downloader](@ref omz_tools_downloader_README)
|
||||
- [Model Downloader](@ref omz_tools_downloader)
|
||||
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
|
||||
Reference in new issue
Block a user