ibm-watson-studio-pipelines 0.2.1

Python utilities for IBM Watson Studio Pipelines

IBM Watson Studio Pipelines Python Client

This package provides various utilities for working with IBM Watson Studio Pipelines. Its primary usage is to enable users to store artifact results of a notebook run.

Usage

Construction

WSPipelines client is constructed from IAM APIKEY, which can be provided in a few ways:

• explicitly:

  from ibm_watson_studio_pipelines import WSPipelines
  
  client = WSPipelines(apikey)
  # or
  client = WSPipelines.from_apikey(apikey)
  # or
  client = WSPipelines.from_token(token)
  

• implicitly:

  APIKEY=...
  export APIKEY
  

  or

  USER_ACCESS_TOKEN=...
  export USER_ACCESS_TOKEN
  

  from ibm_watson_studio_pipelines import WSPipelines
  
  # use APIKEY
  client = WSPipelines.from_apikey()
  
  # use USER_ACCESS_TOKEN
  client = WSPipelines.from_token()
  
  # try APIKEY, if absent then USER_ACCESS_TOKEN:
  client = WSPipelines()
  # or
  client = WSPipelines.new_instance()
  

All of the above may also define service_name and url.

The exact procedure of deciding which authentication method to use:

1. If from_apikey or from_token is used, the method is forced.
2. If constructor is used but either apikey or bearer_token argument was provided, that method will be forced (if both are present, an overloading error will be raised). Note that providing a nameless argument is equivalent to providing apikey.
3. If constructor or new_instance is used, APIKEY env-var is used.
4. If constructor or new_instance is used, but APIKEY env-var is not present, USER_ACCESS_TOKEN env-var is used.
5. If none of the above matches, an error is returned.

Usage in Python notebooks

Notebooks run in IBM Watson Studio Pipelines get inputs and expose outputs as a node:

{
  "id": ...,
  "type": "execution_node",
  "op": "run_container",
  "app_data": {
    "pipeline_data": {
      "name": ...,
      "config": {
        "link": {
          "component_id_ref": "run-notebook"
        }
      },
      "inputs": [
        ...,
        {
          "name": "model_name",
          "group": "env_variables",
          "type": "String",
          "value_from": ...
        }
      ],
      "outputs": [
        {
          "name": "trained_model",
          "group": "output_variables",
          "type": {
            "CPDPath": {
              "path_type": "resource",
              "resource_type": "asset",
              "asset_type": "wml_model"
            }
          }
        }
      ]
    }
  },
  ...
}

Inside of the notebook, inputs are available as environmental variables:

model_name = os.environ['model_name']

Outputs are exposed using sdk method, store_results:

client = WSPipelines.from_apikey(...)
client.store_results({
  "trained_model": ... // cpd path to the trained model
})

Other features

Client also provides a method to get WML instance credentials:

client.get_wml_credentials() # the scope passed in notebook
# or
client.get_wml_credentials("cpd://projects/123456789")

Note how the result will vary depending on the authentication method used to create the client.

Contribution

See a separate document on contribution.