Metadata-Version: 2.1
Name: ibm-watson-studio-pipelines
Version: 0.1.3
Summary: Python utilities for IBM Watson Studio Pipelines
Home-page: https://github.ibm.com/AILifecycle/ibm-watson-studio-pipelines
Author: Michalina Kotwica
Author-email: michalina.kotwica@ibm.com
License: UNKNOWN
Description: # 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:
        
          ```python
          from ibm_watson_studio_pipelines import WSPipelines
          
          client = WSPipelines(apikey)
          # or
          client = WSPipelines.from_apikey(apikey)
          ```
        
        * implicitly:
        
          ```bash
          APIKEY=...
          export APIKEY
          ```
        
          ```python
          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:
        
        ```python
        model_name = os.environ['model_name']
        ```
        
        Outputs are exposed using sdk method, `store_results`:
        
        ```python
        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:
        
        ```python
        client.get_wml_credentials() # the scope passed in notebook
        # or
        client.get_wml_credentials("cpd://projects/123456789")
        ```
        
        
        ## Contribution
        
        See a separate [document on contribution](CONTRIBUTING.md).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
