bootstrap.md

Set Up OpenVINO DL Workbench

• If you are a developer and want to configure your system for DL Workbench development, refer to Configure for Development section.

Configure for Development

0. Install prerequisites:

   • git
   • python3.6

1. Clone the DL Workbench repository:

   git clone https://github.com/openvinotoolkit/workbench.git

2. Run the script to bootstrap os from the root folder of the DL Workbench repository:

   bash automation/bootstrap/bootstrap_os.sh

3. (Optional) Set up the workspace in PyCharm

   1. Open the workbench project in PyCharm
   2. Open the automation/bootstrap/bootstrap_pycharm.py
   3. Right click inside the script and choose Run 'bootstrap_pycharm.py'
   4. Restart PyCharm

Steps if you want to set up the environment manually

0. Install prerequisites:

   • git
   • python3.6

1. Download the OpenVINO package and install it (follow the instruction) The full link to the current package you can find in openvino_version.yml in the private CI repository.

2. Open the terminal and clone the DL Workbench repository:

   git clone https://github.com/openvinotoolkit/workbench.git
   cd workbench
   export OPENVINO_WORKBENCH_ROOT=$(pwd)

3. Initialize and clone submodules of the repository:

   git submodule update --init --recursive

4. Install the virtualenv python package:

   python3 -m pip install virtualenv

5. Create a python virtual environment:

   python3 -m virtualenv ${OPENVINO_WORKBENCH_ROOT}/.venv

6. Initialize the OpenVINO environment:

   source ~/intel/openvino_2022.4.653/setupvars.sh

7. Install OpenVINO console tools (Accuracy Checker, POT, Benchmark App, Model Optimizer) from wheels:

   source ${OPENVINO_WORKBENCH_ROOT}/.venv/bin/activate
   python -m pip install ${INTEL_OPENVINO_DIR}/tools/wheels/openvino-2022.1.0-5583-cp36-cp36m-manylinux2014_x86_64.whl
   python -m pip install ${INTEL_OPENVINO_DIR}/tools/wheels/openvino_dev-2022.1.0-5583-py3-none-any.whl
   deactivate

   Make sure to substitute cp36-cp36m in openvino runtime wheel with your Python version.

8. Install requirements of DL Workbench:

   source ${OPENVINO_WORKBENCH_ROOT}/.venv/bin/activate
   python -m pip install -r ${OPENVINO_WORKBENCH_ROOT}/requirements/requirements.txt
   python -m pip install -r ${OPENVINO_WORKBENCH_ROOT}/requirements/requirements_dev.txt
   python -m pip install -r ${OPENVINO_WORKBENCH_ROOT}/requirements/requirements_jupyter.txt
   python -m pip install -r ${OPENVINO_WORKBENCH_ROOT}/client/automation/requirements_dev.txt
   python -m pip install -r ${OPENVINO_WORKBENCH_ROOT}/model_analyzer/requirements.txt
   deactivate

9. Create a environment for models conversion:

   python3 -m virtualenv ${OPENVINO_WORKBENCH_ROOT}/.unified_venv
   source ${OPENVINO_WORKBENCH_ROOT}/.unified_venv/bin/activate
   python3 -m pip install -r ${INTEL_OPENVINO_DIR}/tools/model_optimizer/requirements_tf2.txt
   python3 -m pip install -r ${INTEL_OPENVINO_DIR}/extras/open_model_zoo/tools/model_tools/requirements.in
   python3 -m pip install -r ${INTEL_OPENVINO_DIR}/extras/open_model_zoo/tools/model_tools/requirements-pytorch.in
   python3 -m pip install -r ${INTEL_OPENVINO_DIR}/extras/open_model_zoo/tools/model_tools/requirements-caffe2.in
   deactivate

10. Install Node.js and install packages for the client:

    wget -qO- https://raw.githubusercontent.com/nvm-sh/nvm/v0.38.0/install.sh | bash
    export NVM_DIR="$([ -z "${XDG_CONFIG_HOME-}" ] && printf %s "${HOME}/.nvm" || printf %s "${XDG_CONFIG_HOME}/nvm")"
    [ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh" # This loads nvm
    
    pushd ${OPENVINO_WORKBENCH_ROOT}/client
        nvm use --delete-prefix v14 --silent
        npm ci
        npm run init-netron
    popd

11. Install Git hooks:

    source ${OPENVINO_WORKBENCH_ROOT}/.venv/bin/activate
    pre-commit install
    pre-commit install --hook-type pre-push
    deactivate

12. Install Docker

    1. Update the apt package index and install packages to allow apt to use a repository over HTTPS

    sudo -E apt-get update
    
    sudo -E apt-get install \
        apt-transport-https \
        ca-certificates \
        curl \
        gnupg \
        lsb-release

    2. Add Docker’s official GPG key

    curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg

    3. Use the following command to set up the stable repository

    echo \
    "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \
    $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

    4. Install Docker engine

    sudo -E apt-get update
    
    sudo -E apt-get install docker-ce docker-ce-cli containerd.io

    5. Verify that Docker Engine is installed correctly by running the hello-world image

    sudo docker run hello-world

    6. To manage Docker as a non-root user create the docker group

    sudo groupadd docker

    7. Add your user to the docker group

    sudo usermod -aG docker $USER

    8. Log out and log back in so that your group membership is re-evaluated

13. Install Docker Compose

    1. Run this command to download the current stable release of Docker Compose

    sudo -E curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose

    2. Apply executable permissions to the binary

    sudo chmod +x /usr/local/bin/docker-compose

    3. Install command completion for the bash and zsh shell

    sudo -E curl \
    -L https://raw.githubusercontent.com/docker/compose/1.29.2/contrib/completion/bash/docker-compose \
    -o /etc/bash_completion.d/docker-compose

    4. Reload your terminal. You can close and then open a new terminal, or reload your setting with source ~/.bashrc command in your current terminal.

14. Install PyCharm

15. Setup PyCharm environment

    1. Open 'Edit Configurations' window and add new configuration(press plus button)

    2. Select Docker->Docker-compose from the list

    3. Setup settings as on the image

       Property	Value
       Compose files	./docker/docker-compose.local.yml;
       Environment variables	LOCAL_ADDR=172.17.0.1
       Services	nginx, postgres, rabbitmq,

    4. Add another configuration, select 'npm' from the list

    5. Setup settings as on the image

       Property	Value
       package.json	OPENVINO_WORKBENCH_ROOT/client/package.json
       Command	start
       Arguments	--scripts-prepend-node-path

    6. Create Python configuration for Gunicorn

    7. Setup settings as on the image

       Property	Value
       Script path	OPENVINO_WORKBENCH_ROOT/.venv/bin/gunicorn
       Parameters	--worker-class eventlet -w 1 -b 0.0.0.0:5676 workbench:APP --log-level DEBUG --no-sendfile --timeout 500
       Working configuration	OPENVINO_WORKBENCH_ROOT/.venv/bin

    8. Add these user environment variables for the configuration:

       Property	Value
       PYTHONUNBUFFERED	1
       PYTHONPATH
       LD_LIBRARY_PATH
       INTEL_OPENVINO_DIR
       VENV_TF2_PYTHON	OPENVINO_WORKBENCH_ROOT/.unified_venv
       SERVER_MODE	development
       PUBLIC_PORT	4200

    9. In terminal activate OpenVINO environment

       source ~/intel/openvino_2022/setupvars.sh

    10. Copy output of commands as values to the same named environment variables in PyCharm

        echo $PYTHONPATH
        echo $LD_LIBRARY_PATH
        echo $INTEL_OPENVINO_DIR

    11. Copy this configuration(this one will be for Celery)

    12. Setup settings as on the image

        Property	Value
        Script path	OPENVINO_WORKBENCH_ROOT/.venv/bin/celery
        Parameters	-A wb.main.tasks.task worker --loglevel=DEBUG
        Working configuration	OPENVINO_WORKBENCH_ROOT/

    13. Set to user environment variable VENV_TF2_PYTHON this value OPENVINO_WORKBENCH_ROOT/.unified_venv/bin/python

    14. Copy this configuration(this one will be for database upgrading)

    15. Setup settings as on the image

        Property	Value
        Script path	OPENVINO_WORKBENCH_ROOT/.venv/bin/flask
        Parameters	db upgrade
        Working configuration	OPENVINO_WORKBENCH_ROOT/

    16. Set to user environment variable FLASK_APP this value OPENVINO_WORKBENCH_ROOT/migrations/migration:APP

    17. Remove PUBLIC_PORT user environment variable

16. Run all of these configurations

17. Check http://127.0.0.1:4200 in your browser, you should see DL Workbench app

How to download bundles for setuping remote targets and creating deployment package

1. Copy the value of the field openvino_package from the openvino_version.yml from the private CI repository file
2. Run the following command to download bundles from root of the repository:

python ./wb/main/utils/bundle_creator/bundle_downloader.py \
    --link ${PACKAGE_LINK}/deployment_archives \
    -os ubuntu18 ubuntu20 \
    --output-path ./bundles \
    --targets cpu gpu vpu hddl opencv python3.6 python3.7 python3.8 

Setup VM for working with remote targets

1. Create new virtual machine with Ubuntu 18.04 OS
2. Configure SSH server in guest Ubuntu OS
   1. Install openssh-server:

      sudo apt update 
      sudo apt install openssh-server

   2. Check SSH service is running:

      sudo systemctl status ssh 

   3. Update firewall rules:

      sudo ufw allow ssh

   4. Configure network in virtual machine as bridged network. Your VM's IP address should be in the same network as your host machine (e.g 192.168.*.*), not in the virtual subnet.
   5. Test SSH connection:

      ssh -p $user $user@$machine_ip_address 

      Example:

      ssh -p 22 parallels@192.168.1.100

3. Generate and setup SSH keys:
   1. Generate SSH public and private key pair:

      ssh-keygen -t rsa -f ~/destination_file_path

   2. Copy public key to virtual machine:

      ssh-copy-id -i ~/destination_file_path -p $user $user@$machine_ip_address

   3. Test SSH connection with private key:

      ssh -i ~/destination_file_path -p $user $user@$machine_ip_address

4. Make sure that your VM satisfy requirement:
   • Installed python (v3) and pip (as a python module:python3 -m pip --version)
   • Configured Internet connection (ping -c 1 google.com)