ibm-watson-studio-pipelines 0.1.2

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)
  

• implicitly:

  APIKEY=...
  export APIKEY
  

  from ibm_watson_studio_pipelines import WSPipelines
  
  client = WSPipelines()
  # or
  client = WSPipelines.new_instance()
  # or
  client = WSPipelines.from_apikey()
  

All of the above may also define service_name and url.

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")

Contribution

See a separate document on contribution.