Customizing with pip

You can add custom packages from the Python Package Index, PyPI without access to the public network.

You can use these methods to add custom packages:

Configuring pip to use a proxy server
Using pip from IBM Cloud Pak for Data storage
Using pip from Project storage

Required roles:

You must be a Cloud Pak for Data cluster administrator to create a pip configuration file pip.conf.
You need Admin or Editor permissions on the analytics project to create an environment template and add a software configuration.

Configuring pip to use a proxy server

You can configure pip for use behind a proxy server by creating your own clusterwide pip configuration file called pip.conf in which you can specify your own package index or a proxy user.

Create a new pip configuration file or open the existing one at /user-home/_global_/config/conda/pip.conf. The names of the settings in pip.conf are derived from the long command-line option. Use the following options to configure your corporate proxy and an internal package index:
```
[global]
proxy=http://user:password@proxy_name:port
index-url=http://internal-pip-index.internal.com/root/pypi/+simple/
trusted-host=internal-pip-index.internal.com
```

Copy the pip.conf file to the shared file system:

ibm_nginx_pod=`oc get pods -l component=ibm-nginx -o jsonpath='{.items[0].metadata.name}'`
oc cp pip.conf ${ibm_nginx_pod}:/user-home/_global_/config/conda/pip.conf

Configuring pip from IBM Cloud Pak for Data storage

You can add a custom Python distribution package to IBM Cloud Pak for Data storage and then access these packages directly from within a notebook. Alternatively, you can add a configuration to your environment runtime with the file path to the package where it can be picked up by the runtime builds.

Create a Python project with a setup.py build script.
Generate a distribution package.

Upload the zipped archive file to /user-home/_global_/config/conda.

 ibm_nginx_pod=`oc get pods -l component=ibm-nginx -o jsonpath='{.items[0].metadata.name}'`
 oc exec ${ibm_nginx_pod} -- chmod a+r "/user-home/_global_/config/conda/<package_name>"

Create an environment template in the Watson Studio project and add a customization for pip:

# Add conda channels below defaults, indented by two spaces and a hyphen.
channels:
  - nodefaults

# Add conda packages here, indented by two spaces and a hyphen.
dependencies:

# Add pip packages here, indented by four spaces and a hyphen.
# Remove the comments on the following lines and replace sample package name with your package name.
  - pip:
    - file:///user-home/_global_/config/conda/Archive.zip

Configuring pip from project storage

You can add a custom Python distribution package to your analytics project in Watson Studio and then add a configuration to your environment template with the file path to the data assets folder in the project.

Create a Python project with a setup.py build script.
Generate a distribution package.
Upload the zipped distribution file to your project as a data asset.

Create an environment template in your Watson Studio project and add a customization:

# Add conda channels below defaults, indented by two spaces and a hyphen.
channels:
  - nodefaults

# Add conda packages here, indented by two spaces and a hyphen.
dependencies:

# Add pip packages here, indented by four spaces and a hyphen.
# Remove the comments on the following lines and replace sample package name with your package name.
  - pip:
    - file:///project_data/data_asset/Archive.zip

If your environment is air-gapped and you do not plan to use any conda packages from external sources, create a .condarc file in the same folder with the following content:

offline: True

This ensures that conda filters out all channels that do not use the file:// protocol.

Parent topic: Customization options for conda and pip