diff --git a/docs/Documentation/dl_workbench_overview.md b/docs/Documentation/dl_workbench_overview.md deleted file mode 100644 index 41acb4a1364..00000000000 --- a/docs/Documentation/dl_workbench_overview.md +++ /dev/null @@ -1,15 +0,0 @@ -# OpenVINO™ Deep Learning Workbench Overview {#workbench_docs_Workbench_DG_Introduction} - -@sphinxdirective -.. toctree:: - :maxdepth: 1 - :hidden: - - workbench_docs_Workbench_DG_Install - workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets - Tutorials - User Guide - workbench_docs_Workbench_DG_Troubleshooting - -@endsphinxdirective - diff --git a/docs/Documentation/openvino_ecosystem.md b/docs/Documentation/openvino_ecosystem.md index 9c418693ca9..b60413e0a16 100644 --- a/docs/Documentation/openvino_ecosystem.md +++ b/docs/Documentation/openvino_ecosystem.md @@ -7,7 +7,6 @@ :hidden: ovtf_integration - ote_documentation ovsa_get_started openvino_inference_engine_tools_compile_tool_README openvino_docs_tuning_utilities @@ -16,7 +15,6 @@ @endsphinxdirective - 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. ### Neural Network Compression Framework (NNCF) @@ -51,14 +49,14 @@ More resources: * [installation Guide on GitHub](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Install-Guide) ### DL Workbench -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. +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. More resources: -* [documentation](dl_workbench_overview.md) +* [Documentation](https://docs.openvino.ai/2022.3/workbench_docs_Workbench_DG_Introduction.html) * [Docker Hub](https://hub.docker.com/r/openvino/workbench) * [PyPI](https://pypi.org/project/openvino-workbench/) -### OpenVINO™ Training Extensions (OTE) +### OpenVINO™ Training Extensions (OTX) A convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference. More resources: diff --git a/docs/OV_Runtime_UG/migration_ov_2_0/intro.md b/docs/OV_Runtime_UG/migration_ov_2_0/intro.md index fc897f947e4..905ac5ed36d 100644 --- a/docs/OV_Runtime_UG/migration_ov_2_0/intro.md +++ b/docs/OV_Runtime_UG/migration_ov_2_0/intro.md @@ -46,7 +46,7 @@ Some of the OpenVINO Development Tools also support both OpenVINO IR v10 and v11 - 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). - [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. -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. +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. > **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. diff --git a/docs/install_guides/troubleshooting-issues.md b/docs/install_guides/troubleshooting-issues.md index c59e1cb5bbb..fd539d6ea64 100644 --- a/docs/install_guides/troubleshooting-issues.md +++ b/docs/install_guides/troubleshooting-issues.md @@ -30,7 +30,7 @@ Users in China might encounter errors while downloading sources via PIP during O ### Proxy Issues -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. +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. @anchor yocto-install-issues diff --git a/docs/optimization_guide/dldt_deployment_optimization_tput_advanced.md b/docs/optimization_guide/dldt_deployment_optimization_tput_advanced.md index 1e01ec8b506..3465b3dd5e8 100644 --- a/docs/optimization_guide/dldt_deployment_optimization_tput_advanced.md +++ b/docs/optimization_guide/dldt_deployment_optimization_tput_advanced.md @@ -45,7 +45,6 @@ Similarly, different devices require a different number of execution streams to In some cases, combination of streams and batching may be required to maximize the throughput. 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)**. -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. > **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. diff --git a/docs/security_guide/introduction.md b/docs/security_guide/introduction.md index 2d4557da3b0..bc5c35a0f1f 100644 --- a/docs/security_guide/introduction.md +++ b/docs/security_guide/introduction.md @@ -6,7 +6,6 @@ :maxdepth: 1 :hidden: - openvino_docs_security_guide_workbench openvino_docs_OV_UG_protecting_model_guide @endsphinxdirective diff --git a/docs/security_guide/workbench.md b/docs/security_guide/workbench.md deleted file mode 100644 index 8aa5252d05d..00000000000 --- a/docs/security_guide/workbench.md +++ /dev/null @@ -1,27 +0,0 @@ -# Deep Learning Workbench Security {#openvino_docs_security_guide_workbench} - -Deep Learning Workbench (DL Workbench) is a web application running within a Docker\* container. - -## Run DL Workbench - -Unless necessary, limit the connections to the DL Workbench to `localhost` (127.0.0.1), so that it -is only accessible from the machine the Docker container is built on. - -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. -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. - -## Authentication Security - -DL Workbench uses [authentication tokens](@ref workbench_docs_Workbench_DG_Authentication) to access the -application. The script starting the DL Workbench creates an authentication token each time the DL -Workbench starts. Anyone who has the authentication token can use the DL Workbench. - -When you finish working with the DL Workbench, log out to prevent the use of the DL Workbench from -the same browser session without authentication. - -To invalidate the authentication token completely, [restart the DL Workbench](@ref workbench_docs_Workbench_DG_Docker_Container). - -## Use TLS to Protect Communications - -[Configure Transport Layer Security (TLS)](@ref workbench_docs_Workbench_DG_Configure_TLS) to keep the -authentication token encrypted.