DL Workbench is the official UI environment of the OpenVINO™ toolkit.
OpenVINO™ Deep Learning Workbench Python Starter
Copyright © 2018-2023 Intel Corporation
LEGAL NOTICE: Your use of this software and any required dependent software (the “Software Package”) is subject to the terms and conditions of the Apache 2.0 License.
Deep Learning Workbench is a web-based graphical environment with a convenient user-friendly interface and a wide range of customization options designed to make the development of deep learning models significantly easier.
The DL Workbench is an official UI environment of the OpenVINO™ toolkit that enables you to:
- Learn what neural networks are, how they work, and how to analyze their architectures and performance.
- Get familiar with the OpenVINO™ ecosystem and its main components without installing it on your system.
- Measure and interpret model performance.
- Analyze the quality of your model and visualize output.
- Optimize your model and prepare it for deployment on the target system.
In the DL Workbench, you can use the following OpenVINO™ toolkit components:
|Model Downloader and Model Converter||Model Downloader is a tool for getting access to the collection of high-quality pre-trained deep learning public and Intel-trained models. The tool downloads model files from online sources and, if necessary, patches them with Model Optimizer.
Model Converter is a tool for converting the models stored in a format other than the Intermediate Representation (IR) into that format using Model Optimizer.
|Model Optimizer||Model Optimizer imports, converts, and optimizes models that were trained in certain frameworks to the IR format used in OpenVINO tools.
Supported frameworks include TensorFlow*, Caffe*, Kaldi**, MXNet*, and ONNX*.
|Benchmark Tool||Benchmark Application allows you to estimate deep learning inference performance on supported devices for synchronous and asynchronous modes.|
|Accuracy Checker||Accuracy Checker is a deep learning accuracy validation tool that allows you to evaluate accuracy on the given dataset by collecting one or several metric values.|
|Post-Training Optimization Tool||Post-Training Optimization Tool allows you to optimize trained models with advanced capabilities, such as quantization and low-precision optimizations, without the need to retrain or fine-tune models.|
The complete list of recommended requirements is available in the documentation.
To successfully run the DL Workbench with Python Starter, install Python 3.6 or higher.
|Operating system||Ubuntu* 18.04||Windows* 10||macOS* 10.15 Catalina|
|Available RAM space||8 GB**||8 GB**||8 GB**|
|Available storage space||10 GB + space for imported artifacts||10 GB + space for imported artifacts||10 GB + space for imported artifacts|
|Docker*||Docker CE 18.06.1||Docker Desktop 184.108.40.206||Docker CE 18.06.1|
Windows*, Linux* and MacOS* support CPU targets. GPU, Intel® Neural Compute Stick 2 and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs are supported only for Linux*.
Install the DL Workbench Starter
Step 1. Set Up Python Virtual Environment
To avoid dependency conflicts, use a virtual environment. Skip this step only if you do want to install all dependencies globally.
Create virtual environment by executing the following commands in your terminal:
- On Linux and MacOS:
python3 -m pip install --user virtualenv python3 -m venv venv
- On Windows:
py -m pip install --user virtualenv py -m venv venv
Step 2. Activate Virtual Environment
- On Linux and MacOS:
- On Windows:
Step 3. Update PIP to the Latest Version
Run the command below:
python -m pip install --upgrade pip
Step 4. Install the Python Wrapper
pip install -U openvino-workbench
Step 5. Verify the Installation
To verify that the package is properly installed, run the command below:
You will see the help message for the starting package if installation finished successfully.
Use the DL Workbench Starter
To start the latest available version of the DL Workbench, execute the following command:
openvino-workbench --image openvino/workbench:latest --force-pull
You can see the list of available arguments with the following command:
Refer to the documentation for additional information.
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