[DOCS] Remove DL Workbench (#15733)
* remove dl wb docs * text correction * change ecosystem description * replace link
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# OpenVINO™ Deep Learning Workbench Overview {#workbench_docs_Workbench_DG_Introduction}
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@sphinxdirective
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.. toctree::
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:maxdepth: 1
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:hidden:
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workbench_docs_Workbench_DG_Install
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workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets
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Tutorials <workbench_docs_Workbench_DG_Tutorials>
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User Guide <workbench_docs_Workbench_DG_User_Guide>
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workbench_docs_Workbench_DG_Troubleshooting
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@endsphinxdirective
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@ -7,7 +7,6 @@
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:hidden:
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ovtf_integration
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ote_documentation
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ovsa_get_started
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openvino_inference_engine_tools_compile_tool_README
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openvino_docs_tuning_utilities
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@ -16,7 +15,6 @@
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@endsphinxdirective
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OpenVINO™ is not just one tool. It is an expansive ecosystem of utilities, providing a comprehensive workflow for deep learning solution development. Learn more about each of them to reach the full potential of OpenVINO™ Toolkit.
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### Neural Network Compression Framework (NNCF)
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@ -51,14 +49,14 @@ More resources:
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* [installation Guide on GitHub](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Install-Guide)
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### DL Workbench
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A web-based tool for deploying deep learning models. Built on the core of OpenVINO and equipped with a graphics user interface, DL Workbench is a great way to explore the possibilities of the OpenVINO workflow, import, analyze, optimize, and build your pre-trained models. You can do all that by visiting [Intel® DevCloud for the Edge](https://software.intel.com/content/www/us/en/develop/tools/devcloud.html) and launching DL Workbench on-line.
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A web-based tool for deploying deep learning models. Built on the core of OpenVINO and equipped with a graphics user interface, DL Workbench is a great way to explore the possibilities of the OpenVINO workflow, import, analyze, optimize, and build your pre-trained models. You can do all that by visiting [Intel® Developer Cloud](https://software.intel.com/content/www/us/en/develop/tools/devcloud.html) and launching DL Workbench online.
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More resources:
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* [documentation](dl_workbench_overview.md)
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* [Documentation](https://docs.openvino.ai/2022.3/workbench_docs_Workbench_DG_Introduction.html)
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* [Docker Hub](https://hub.docker.com/r/openvino/workbench)
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* [PyPI](https://pypi.org/project/openvino-workbench/)
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### OpenVINO™ Training Extensions (OTE)
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### OpenVINO™ Training Extensions (OTX)
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A convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference.
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More resources:
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@ -46,7 +46,7 @@ Some of the OpenVINO Development Tools also support both OpenVINO IR v10 and v11
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- Accuracy checker uses API 2.0 for model accuracy measurement by default. It also supports switching to the old API by using the `--use_new_api False` command-line parameter. Both launchers accept OpenVINO IR v10 and v11, but in some cases configuration files should be updated. For more details, see the [Accuracy Checker documentation](https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/openvino/tools/accuracy_checker/launcher/openvino_launcher_readme.md).
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- [Compile tool](../../../tools/compile_tool/README.md) compiles the model to be used in API 2.0 by default. To use the resulting compiled blob under the Inference Engine API, the additional `ov_api_1_0` option should be passed.
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However, Post-Training Optimization Tool and Deep Learning Workbench of OpenVINO 2022.1 do not support OpenVINO IR v10. They require the latest version of Model Optimizer to generate OpenVINO IR v11 files.
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However, Post-Training Optimization Tool of OpenVINO 2022.1 does not support OpenVINO IR v10. They require the latest version of Model Optimizer to generate OpenVINO IR v11 files.
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> **NOTE**: To quantize your OpenVINO IR v10 models to run with OpenVINO 2022.1, download and use Post-Training Optimization Tool of OpenVINO 2021.4.
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@ -30,7 +30,7 @@ Users in China might encounter errors while downloading sources via PIP during O
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### <a name="proxy-issues"></a>Proxy Issues
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If you met proxy issues during the installation with Docker, you need set up proxy settings for Docker. See the [Set Proxy section in DL Workbench Installation](https://docs.openvino.ai/latest/workbench_docs_Workbench_DG_Prerequisites.html#set-proxy) for more details.
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If you met proxy issues during the installation with Docker, you need set up proxy settings for Docker. See the [Docker guide](https://docs.docker.com/network/proxy/) for more details.
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@anchor yocto-install-issues
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@ -45,7 +45,6 @@ Similarly, different devices require a different number of execution streams to
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In some cases, combination of streams and batching may be required to maximize the throughput.
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One possible throughput optimization strategy is to **set an upper bound for latency and then increase the batch size and/or number of the streams until that tail latency is met (or the throughput is not growing anymore)**.
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Consider [OpenVINO Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) that builds handy latency vs throughput charts, iterating over possible values of the batch size and number of streams.
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> **NOTE**: When playing with [dynamically-shaped inputs](../OV_Runtime_UG/ov_dynamic_shapes.md), use only the streams (no batching), as they tolerate individual requests having different shapes.
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:maxdepth: 1
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:hidden:
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openvino_docs_security_guide_workbench
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openvino_docs_OV_UG_protecting_model_guide
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@endsphinxdirective
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# Deep Learning Workbench Security {#openvino_docs_security_guide_workbench}
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Deep Learning Workbench (DL Workbench) is a web application running within a Docker\* container.
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## Run DL Workbench
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Unless necessary, limit the connections to the DL Workbench to `localhost` (127.0.0.1), so that it
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is only accessible from the machine the Docker container is built on.
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When using `docker run` to [start the DL Workbench from Docker Hub](@ref workbench_docs_Workbench_DG_Run_Locally), limit connections for the host IP 127.0.0.1.
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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 details.
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## Authentication Security
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DL Workbench uses [authentication tokens](@ref workbench_docs_Workbench_DG_Authentication) to access the
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application. The script starting the DL Workbench creates an authentication token each time the DL
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Workbench starts. Anyone who has the authentication token can use the DL Workbench.
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When you finish working with the DL Workbench, log out to prevent the use of the DL Workbench from
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the same browser session without authentication.
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To invalidate the authentication token completely, [restart the DL Workbench](@ref workbench_docs_Workbench_DG_Docker_Container).
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## Use TLS to Protect Communications
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[Configure Transport Layer Security (TLS)](@ref workbench_docs_Workbench_DG_Configure_TLS) to keep the
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authentication token encrypted.
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