You can configure Data Science & AI Workbench to use a proxy for conda packages, if your organization’s network security policy requires it.

​
Obtain your proxy variables

In order to complete the following sections, you need an appropriate configuration for your proxy. This usually takes the form of environment variables:
  • http_proxy/HTTP_PROXY
  • https_proxy/HTTPS_PROXY
  • no_proxy/NO_PROXY
Ask your system administrator to confirm which variables to use.

​
Verify the proxy works

After you’ve configured the platform, you can test your changes to verify that it’s using the proxy.
  1. Log in to Workbench.
  2. Click Projects, and open the project you want to use to test the proxy.
    If the project already has an open session, you’ll need to stop the current session and start a new session.
  3. Open a terminal window within JupyterLab.
  4. Set and export your proxy variables.
  5. Verify the proxy works by running the following command: 
    conda create -n testenv python

​
Configure Workbench

Once you’ve confirmed your proxy works, go to Setting global config variables and follow the steps there to apply those variables to all future sessions, deployments, and jobs. The lines you add to the global config should look something like the following, with your specific proxy URLs and domains substituted in: 
https_proxy: http://proxy.example.com:1245/
https_proxy: https://proxy.example.com:1245/
no_proxy: *.example.com
HTTP_PROXY: http://proxy.example.com:1245/
HTTPS_PROXY: https://proxy.example.com:1245/
NO_PROXY: *.example.com