Propagating software specifications and package extensions from projects to deployment spaces

To export custom software specifications and package extensions that were created in a watsonx.ai Studio project to a deployment space:

1. From your project interface, click the Manage tab.
2. Select Environments.
3. Click the Templates tab.
4. From your custom environment's Options menu, select Promote to space.

Alternatively, when you promote any model or Python function that is associated with a custom environment from a watsonx.ai Studio project to a deployment space, the associated custom software specification and package extension is also promoted to the deployment space.

If you want to update software specifications and package extensions after you promote them to deployment space, follow these steps:

1. In the deployment space, delete the software specifications, package extensions, and associated models (optional) by using the watsonx.ai Python client.
2. In a project, promote the model, function, or script that is associated with the changed custom software specification and package extension to the space.

Software specifications are also included when you import a project or space that includes one.

Saving a conda YAML file that contains a list of third-party libraries

To save a conda YAML file that contains a list of third-party libraries as a package extension and create a custom software specification that is linked to the package extension:

1. Authenticate and create the client.

2. Create and set the default deployment space, then list available software specifications.

    metadata = {
        wml_client.spaces.ConfigurationMetaNames.NAME:
            'examples-create-software-spec',
        wml_client.spaces.ConfigurationMetaNames.DESCRIPTION:
            'For my models'
    }
    space_details = wml_client.spaces.store(meta_props=metadata)
    space_uid = wml_client.spaces.get_id(space_details)
   
    # set the default space
    wml_client.set.default_space(space_uid)
   
    # see available meta names for software specs
    print('Available software specs configuration:', wml_client.software_specifications.ConfigurationMetaNames.get())
    wml_client.software_specifications.list()
   
    asset_id = 'undefined'
    pe_asset_id = 'undefined'
   

3. Create the metadata for package extensions to add to the base specification.

   pe_metadata = {
       wml_client.package_extensions.ConfigurationMetaNames.NAME:
           'My custom library',
       # optional:
       # wml_client.software_specifications.ConfigurationMetaNames.DESCRIPTION:
       wml_client.package_extensions.ConfigurationMetaNames.TYPE:
           'conda_yml'
   }
   

4. Create a yaml file that contains the list of packages and then save it as customlibrary.yaml.

   Example yaml file:

   name: add-regex-package
   dependencies:
       - regex
   

   For more information, see Examples of customizations.

5. Store package extension information.

   pe_asset_details = wml_client.package_extensions.store(
       meta_props=pe_metadata,
       file_path='customlibrary.yaml'
   )
   pe_asset_id = wml_client.package_extensions.get_id(pe_asset_details)
   

6. Create the metadata for the software specification and store the software specification.

   # Get the id of the base software specification
   base_id = wml_client.software_specifications.get_id_by_name('default_py3.9')
   
   # create the metadata for software specs
   ss_metadata = {
       wml_client.software_specifications.ConfigurationMetaNames.NAME:
           'Python 3.9 with pre-installed ML package',
       wml_client.software_specifications.ConfigurationMetaNames.DESCRIPTION:
           'Adding some custom libraries like regex', # optional
       wml_client.software_specifications.ConfigurationMetaNames.BASE_SOFTWARE_SPECIFICATION:
           {'guid': base_id},
       wml_client.software_specifications.ConfigurationMetaNames.PACKAGE_EXTENSIONS:
           [{'guid': pe_asset_id}]
   }
   
   # store the software spec
   ss_asset_details = wml_client.software_specifications.store(meta_props=ss_metadata)
   
   # get the id of the new asset
   asset_id = wml_client.software_specifications.get_id(ss_asset_details)
   
   # view new software specification details
   import pprint as pp
   
   ss_asset_details = wml_client.software_specifications.get_details(asset_id)
   print('Package extensions', pp.pformat(
       ss_asset_details['entity']['software_specification']['package_extensions']
   ))
   

Saving a user-created Python library and creating a package extension

For more information, see Requirements for using custom components in models.

To save a user-created Python package as a package extension and create a custom software specification that is linked to the package extension:

1. Authenticate and create the client.

2. Create and set the default deployment space, then list available software specifications.

    metadata = {
        wml_client.spaces.ConfigurationMetaNames.NAME:
            'examples-create-software-spec',
        wml_client.spaces.ConfigurationMetaNames.DESCRIPTION:
            'For my models'
    }
    space_details = wml_client.spaces.store(meta_props=metadata)
    space_uid = wml_client.spaces.get_id(space_details)
   
    # set the default space
    wml_client.set.default_space(space_uid)
   
    # see available meta names for software specs
    print('Available software specs configuration:', wml_client.software_specifications.ConfigurationMetaNames.get())
    wml_client.software_specifications.list()
   
    asset_id = 'undefined'
    pe_asset_id = 'undefined'
   

3. Create the metadata for package extensions to add to the base specification.

   Note:

   You can specify pip_zip only as a value for the wml_client.package_extensions.ConfigurationMetaNames.TYPE metadata property.

   pe_metadata = {
       wml_client.package_extensions.ConfigurationMetaNames.NAME:
           'My Python library',
       # optional:
       # wml_client.software_specifications.ConfigurationMetaNames.DESCRIPTION:
       wml_client.package_extensions.ConfigurationMetaNames.TYPE:
           'pip.zip'
   }
   

4. Specify the path of the user-created Python library.

   python_lib_file_path="my-python-library-0.1.zip"
   

   For more information, see Requirements for using custom components in models.

5. Store package extension information.

   pe_asset_details = wml_client.package_extensions.store(
       meta_props=pe_metadata,
       file_path=python_lib_file_path
   )
   pe_asset_id = wml_client.package_extensions.get_id(pe_asset_details)
   

6. Create the metadata for the software specification and store the software specification.

   # Get the id of the base software specification
   base_id = wml_client.software_specifications.get_id_by_name('default_py3.9')
   
   # create the metadata for software specs
   ss_metadata = {
       wml_client.software_specifications.ConfigurationMetaNames.NAME:
           'Python 3.9 with pre-installed ML package',
       wml_client.software_specifications.ConfigurationMetaNames.DESCRIPTION:
           'Adding some custom libraries like regex', # optional
       wml_client.software_specifications.ConfigurationMetaNames.BASE_SOFTWARE_SPECIFICATION:
           {'guid': base_id},
       wml_client.software_specifications.ConfigurationMetaNames.PACKAGE_EXTENSIONS:
           [{'guid': pe_asset_id}]
   }
   
   # store the software spec
   ss_asset_details = wml_client.software_specifications.store(meta_props=ss_metadata)
   
   # get the id of the new asset
   asset_id = wml_client.software_specifications.get_id(ss_asset_details)
   
   # view new software specification details
   import pprint as pp
   
   ss_asset_details = wml_client.software_specifications.get_details(asset_id)
   print('Package extensions', pp.pformat(
       ss_asset_details['entity']['software_specification']['package_extensions']
   ))
   

Troubleshooting

When a conda yml-based custom library installation fails with this error: Encountered error while installing custom library, try these alternatives:

• Use a different version of the same package that is available in Anaconda for the concerned Python version.

• Install the library from the pypi repository, by using pip. Edit the conda yml installation file contents:

  name: <conda yml file name>
  dependencies:
  - numpy
  - pip:
      - pandas==1.2.5
  

Parent topic: Customizing deployment runtimes