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Setting up your environment

Goals

  • Setup a development environment for CD4ML including:
  • Fork Git Code Repository into Personal GitHub
  • Configuring a python code development environment

Github Setup

Navigate to the Github Personal Access Tokens page.

Click "Generate new token" on the top right. You may need to enter your Github password again.

Enter a Note for your personal access token and select the "repo" and "user:email" permissions. Click "Generate Token".

Your personal access token will be created and displayed to you. Make sure you save this token safely because it will not be shown again.

Fork the following repository into your personal github account.

Clone the repo to your machine

git clone https://github.com/<Your User Name>/CD4ML-Scenarios

Setting your Jenkins Administrator Password

Create a file called jenkins-admin-password.txt in the jenkins\ folder. On the first line of the file type in a secure password. Save and close the file

Docker Settings Adjustments

Open Docker Desktop by clicking on the docker icon in your Mac or Windows taskbar and selecting Dashboard. Click the gear and select "Resources" and then "Advanced". Increase the RAM allocated to docker to 4 Gigabytes. Click "Apply and Restart".

DockerSettings

If you are running the environment on a MS Windows 10 machine, make sure to switch Docker to Linux-containers. You can do this by clicking on the little Docker icon in your Windows taskbar. You also need to make sure that your PC can handle virtual environments. This can be switched on or off in your systems BIOS.

Select your development environment

In the next section you can choose from one of two different application development environments either using a local machine based development environment or a JupyterLab based development environment. When completing the scenario there is no difference between the two environments. We recommend that if you are more comfortable with the JupyterLab development IDE and environment then select JupyterLab otherwise use your already existing development environment.

Local machine environment

For this workshop we are going to use python3 as our python, pip3 as our dependency manager, and virtualenv for python environment management.

First you need to fork this repo to your github account and then clone this environment to your local machine

After you install python run the following commands to start your environment

git clone https://github.com/<Your User Name>/CD4ML-Scenarios
cd <cloned repo>
docker-compose up -d --build --remove-orphans
pip3 install virtualenv
virtualenv --python=python3 .venv

# On Mac/Linux run the following
source .venv/bin/activate
pip3 install -r requirements.txt

# On Windows Powershell Run:
Set-ExecutionPolicy RemoteSigned
.venv/Scripts/activate.ps1
pip3 install -r requirements.txt

Note, if using PyCharm and virtualenv, be sure to select the right python interpreter for PyCharm. This is done by clicking on interpreter in the bottom right on the PyCharm window and navigating to the python3 executable in the .venv/bin/ directory. That will allow PyCharm to highlight missing libraries and incorrect syntax correctly. If you don't configure this it will show red lines everywhere because it can't find the installed libraries.

JupyterLab Development Environment

  1. Clone the environment to your local machine using git clone https://github.com/<Your User Name>/CD4ML-Scenarios
  2. Run docker-compose up -d --build --remove-orphans to download the images and start the environment
  3. Run the following command to retrieve the URL for docker.
docker logs dev

You will see a URL such as http://127.0.0.1:8888?token=<token>. Open that url in your web browser

  1. From the JupyterLab environment you can open a terminal window by clicking the terminal tile on the home page. Run the following commands to setup the environment. Please fill in the git email and name to your name
git config --global user.email "<your email>"
git config --global user.name "<your name>"
pip install -r requirements.txt

Next Steps

At this step you can start setting up the different components of CD4ML. Continue to the next section.