ibm-watson-machine-learning (V4)

ibm-watson-machine-learning is a python library that allows you to work with Watson Machine Learning services. Train, store, deploy your models and score them using the APIs and integrate them with your application development. ibm-watson-machine-learning can be used for working with IBM Cloud v2 instance plans and IBM Cloud Pak® for Data version 3.5 and higher. For older versions of IBM Cloud Pak® for Data, use client from link

IMPORTANT: For cloud, refer to the documentation for the migration process to be able to migrate your assets to new instance plans and access new features if you are using Watson Machine Learning Python client V3 or Watson Machine Learning Python client V4 Beta.

NOTE: DEPRECATED!! Python 3.6 framework is deprecated. For Cloud, it will be removed on Jan 20th, 2021. It will be read-only mode starting Nov 20th, 2020. i.e you won’t be able to create new assets using this client. Use Python 3.7 instead. For details, see Supported Frameworks. For IBM Cloud Pak® for Data, Python 3.6 framework is supported only for python 3.6 users upgrading from 3.0.1 to 3.5 and will be removed from a future release. We recommend you migrate your assets and use a Python 3.7 framework instead. For details, see Supported Frameworks

NOTE: DEPRECATED!! The Watson Machine Learning Python client V3 and Watson Machine Learning Python client V4 Beta are being deprecated starting Sep 1st, 2020 on cloud and will be discontinued at the end of the migration period. Migrate to IBM Watson Machine Learning Client V4 GA packaged in the name of ibm-watson-machine-learning on cloud.

Contents

• ibm-watson-machine-learning (V4)

  • Installation

  • API for IBM Cloud

    • Requirements

    • Authentication

    • Supported machine learning frameworks

    • APIs to Migrate V4 Beta and V3 assets to V4

    • Connections

    • Data assets

    • Deployments

    • Hardware specifications

    • Model definitions

    • Package extensions

    • Repository

    • Script

    • Service instance

    • Set

    • Spaces

    • Software specifications

    • Training

    • AutoAI (BETA)

      • AutoAI experiment

        • AutoAI

      • Working with AutoAI class and optimizer

        • Configure optimizer with one data source

        • Configure optimizer with joined data

        • Get configuration parameters

        • Fit optimizer

        • Get run status, get run details

        • Get data connections

        • Summary

        • Get pipeline details

        • Get pipeline

      • Working with deployments

        • Web Service

        • Batch

      • Working with DataConnection

        • Cloud DataConnection Initialization

        • Upload your training dataset

        • Download your training dataset

      • AutoAI Helpers

        • DataConnection

        • S3Connection

        • S3Location

        • CloudAssetLocation

        • DeploymentOutputAssetLocation

        • get_credentials_from_config

        • Enums

      • Deployment Modules for AutoAI models

        • Batch

        • WebService

  • API for IBM Cloud Pak® for Data , IBM Watson Machine Learning Server

    • Authentication

    • Supported machine learning frameworks

    • Connections

    • Data assets

    • Deployments

    • Hardware specifications

    • Model definitions

    • Package extensions

    • Repository

    • Script

    • Set

    • Software specifications

    • Training

    • AutoAI (BETA)

      • AutoAI experiment

        • AutoAI

      • Working with AutoAI class and optimizer

        • Configure optimizer with one data source

        • Configure optimizer with joined data

        • Get configuration parameters

        • Fit optimizer

        • Get run status, get run details

        • Get data connections

        • Summary

        • Get pipeline details

        • Get pipeline

      • Working with deployments

        • Web Service

        • Batch

      • Working with DataConnection

        • Cloud DataConnection Initialization

        • Upload your training dataset

        • Download your training dataset

      • AutoAI Helpers

        • DataConnection

        • S3Connection

        • S3Location

        • CloudAssetLocation

        • DeploymentOutputAssetLocation

        • get_credentials_from_config

        • Enums

      • Deployment Modules for AutoAI models

        • Batch

        • WebService

  • Samples

  • Changelog

• Indices and tables

Installation

It comes integrated into the Watson Studio Jupyter notebook. Also, the package is available on pypi. Please use below command to install it to use with IBM cloud services.

$pip install ibm-watson-machine-learning

API for IBM Cloud

To use Watson Machine learning APIs, user must create an instance of APIClient with authentication details.

Requirements

To create a Watson Machine Learning service instance, refer to the documentation.

Authentication

Authentication for IBM Cloud

IBM Cloud users can create an instance of Watson Machine learning python client by providing IAM token or apikey.

Example of creating the client using apikey:

Note: There is no instance_id to be provided. instance_id will be picked up from associated space/project for the new(v2) instance plans. For accessing your assets from old(v1) instance plans, provide your old v1 instance_id in the wml_credentials below and create a client object

from ibm_watson_machine_learning import APIClient

wml_credentials = {
                   "url": "https://us-south.ml.cloud.ibm.com",
                   "apikey":"***********"
                  }

client = APIClient(wml_credentials)

Example of creating the client using token:

from ibm_watson_machine_learning import APIClient

wml_credentials = {
                   "url": "https://us-south.ml.cloud.ibm.com",
                   "token":"***********",
                  }

client = APIClient(wml_credentials)

Example of creating the client using v1 Lite instance plan:

from ibm_watson_machine_learning import APIClient

wml_credentials = {
                   "url": "https://example.ml.cloud.ibm.com",
                   "apikey":"**********",
                   "instance_id": "v1 lite instance_id"
                  }

client = APIClient(wml_credentials)

Note

• Setting default space/project id is mandatory. Refer to client.set.default_space() API in this document.

Supported machine learning frameworks

For the list of supported machine learning frameworks (models) on IBM cloud, please refer to Watson Machine Learning Documentation.

APIs to Migrate V4 Beta and V3 assets to V4

Migration APIs for v4 GA Cloud to migrate assets from v3 or v4 beta. For details and examples of migration process refer to the document.

class client.Migrationv4GACloud(client)

Migration APIs for v4 GA Cloud. This will be applicable only till the migration period Refer to the documentation at ‘https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/wml-ai.html’ for details on migration

cancel(migration_id, space_id=None, project_id=None)

Cancel a migration job. ‘migration_id’ and ‘space_id’( or ‘project_id’ has to be provided ) Note: To delete a migratin job, use delete() api

Parameters

Important

1. migration_id: Migration identifier

   type: str

2. space_id: Space identifier

   type: str

3. project_id: Project identifier

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.v4ga_cloud_migration.cancel(migration_id='6213cf1-252f-424b-b52d-5cdd9814956c',
>>>                                    space_id='3421cf1-252f-424b-b52d-5cdd981495fe')

delete(migration_id, space_id=None, project_id=None)

Deletes a migration job. ‘migration_id’ and ‘space_id’( or ‘project_id’ has to be provided )

Parameters

Important

1. migration_id: Migration identifier

   type: str

2. space_id: Space identifier

   type: str

3. project_id: Project identifier

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.v4ga_cloud_migration.delete(migration_id='6213cf1-252f-424b-b52d-5cdd9814956c',
>>>                                    space_id='3421cf1-252f-424b-b52d-5cdd981495fe')

get_details(migration_id=None, space_id=None, project_id=None, limit=None)

Get metadata of of the migration job function(s). If no migration_id is specified all migration jobs metadata is returned.

Parameters

Important

1. migration_id: Migration identifier

   type: str

2. space_id: Space identifier

   type: str

3. project_id: Project identifier

   type: str

4. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: migration(s) metadata

return type: dict

dict (if migration_id is not None) or {“resources”: [dict]} (if migration_id is None)

Example

>>> migration_details = client.v4ga_cloud_migration.get_details(migration_id='6213cf1-252f-424b-b52d-5cdd9814956c',
>>>                                                             space_id='3421cf1-252f-424b-b52d-5cdd981495fe')

list(space_id=None, project_id=None, limit=None)

List the migration jobs. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all migration jobs in a table format.

return type: None

Example

>>> client.v4ga_cloud_migration.list()

start(meta_props)

Migration APIs for v4 GA Cloud to migrate assets from v3 or v4 beta. This will be applicable only till the migration period. Refer to the documentation at ‘https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/wml-ai.html’ for details on migration.

You will need to have a space or project created before you start migration of assets to that space or project You should have read access to the instance from which migration is required You should have ‘editor’ or ‘admin’ access on the target ‘space’/’project’

SKIP_MIGRATED_ASSETS meta prop: If this is True (the default) and if the target assets still exist and if there are completed jobs (that were not deleted) then any assets that were already migrated will be skipped and the details will be returned in the skipped collection in the response. If this is False then it is possible that duplicate assets will be created in the target space or project if the asset was already migrated

Parameters

Important

1. meta_props: meta data. To see available meta names use client.v4ga_cloud_migration.ConfigurationMetaNames.get()

   type: str or dict

Output

Important

returns: Initial state of migration

return type: dict

Example

>>> metadata = {
>>>    client.v4ga_cloud_migration.MigrationMetaNames.DESCRIPTION: "Migration of assets from v3 to v4ga",
>>>    client.v4ga_cloud_migration.MigrationMetaNames.OLD_INSTANCE_ID: "df40cf1-252f-424b-b52d-5cdd98143aec",
>>>    client.v4ga_cloud_migration.MigrationMetaNames.SPACE_ID: "3fc54cf1-252f-424b-b52d-5cdd9814987f",
>>>    client.v4ga_cloud_migration.MigrationMetaNames.FUNCTION_IDS: ["all"],
>>>    client.v4ga_cloud_migration.MigrationMetaNames.MODEL_IDS: ["afaecb4-254f-689f-4548-9b4298243291"],
>>>    client.v4ga_cloud_migration.MigrationMetaNames.MAPPING: {"dfaecf1-252f-424b-b52d-5cdd98143481": "4fbc211-252f-424b-b52d-5cdd98df310a"}
>>>    client.v4ga_cloud_migration.MigrationMetaNames.SKIP_MIGRATED_ASSETS: True
>>> }
>>> details = client.v4ga_cloud_migration.start(meta_props=metadata)

class metanames.Migrationv4GACloudMetaNames

Set of MetaNames for v4ga Cloud migration.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
DESCRIPTION	str	N	Testing migration	string
OLD_INSTANCE_ID	str	Y	df40cf1-252f-424b-b52d-5cdd98143aec	string
SPACE_ID	str	N	3fc54cf1-252f-424b-b52d-5cdd9814987f	string
PROJECT_ID	str	N	4fc54cf1-252f-424b-b52d-5cdd9814987f	string
MODEL_IDS	list	N	['afaecb4-254f-689f-4548-9b4298243291']	['string']
FUNCTION_IDS	list	N	['all']	['string']
EXPERIMENT_IDS	list	N	['ba2ecb4-4542-689a-2548-ab4232b43291']	['string']
PIPELINE_IDS	list	N	['4fabcb4-654f-489b-9548-9b4298243292']	['string']
SKIP_MIGRATED_ASSETS	bool	N	False
MAPPING	dict	N	{'dfaecf1-252f-424b-b52d-5cdd98143481': '4fbc211-252f-424b-b52d-5cdd98df310a'}

Connections

class client.Connections(client)

Store and manage your Connections.

ConfigurationMetaNames = <ibm_watson_machine_learning.metanames.ConnectionMetaNames object>

MetaNames for Connection creation.

create(meta_props)

Creates a connection. Input to PROPERTIES field examples

1. MySQL

   >>> client.connections.ConfigurationMetaNames.PROPERTIES: {
   >>>   "database": "database",
   >>>   "password": "password",
   >>>   "port": "3306",
   >>>   "host": "host url",
   >>>   "ssl": "false",
   >>>   "username": "username"
   >>> }
   

2. Google Big query

   1. Method1: Use service account json. The service account json generated can be provided as

      input as-is. Provide actual values in json. Example is only indicative to show the fields. Refer to Google big query documents how to generate the service account json

      >>> client.connections.ConfigurationMetaNames.PROPERTIES: {
      >>>     "type": "service_account",
      >>>     "project_id": "project_id",
      >>>     "private_key_id": "private_key_id",
      >>>     "private_key": "private key contents",
      >>>     "client_email": "client_email",
      >>>     "client_id": "client_id",
      >>>     "auth_uri": "https://accounts.google.com/o/oauth2/auth",
      >>>     "token_uri": "https://oauth2.googleapis.com/token",
      >>>     "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
      >>>     "client_x509_cert_url": "client_x509_cert_url"
      >>> }
      

   2. Method2: Using OAuth Method. Refer to Google big query documents how to generate OAuth token

      >>> client.connections.ConfigurationMetaNames.PROPERTIES: {
      >>>    "access_token": "access token generated for big query",
      >>>    "refresh_token": "refresh token",
      >>>    "project_id": "project_id",
      >>>    "client_secret": "This is your gmail account password",
      >>>   "client_id": "client_id"
      >>> }
      

3. MS SQL

   >>> client.connections.ConfigurationMetaNames.PROPERTIES: {
   >>>      "database": "database",
   >>>      "password": "password",
   >>>      "port": "1433",
   >>>      "host": "host",
   >>>      "username": "username"
   >>> }
   

4. Tera data

   >>> client.connections.ConfigurationMetaNames.PROPERTIES: {
   >>>      "database": "database",
   >>>      "password": "password",
   >>>      "port": "1433",
   >>>      "host": "host",
   >>>      "username": "username"
   >>> }
   

Parameters

Important

1. meta_props: meta data of the connection configuration. To see available meta names use:

   >>> client.connections.ConfigurationMetaNames.get()
   

   type: dict

Output

Important

returns: metadata of the stored connection

return type: dict

Example

>>> sqlserver_data_source_type_id = client.connections.get_datasource_type_uid_by_name('sqlserver')
>>> connections_details = client.connections.create({
>>> client.connections.ConfigurationMetaNames.NAME: "sqlserver connection",
>>> client.connections.ConfigurationMetaNames.DESCRIPTION: "connection description",
>>> client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: sqlserver_data_source_type_id,
>>> client.connections.ConfigurationMetaNames.PROPERTIES: { "database": "database",
>>>                                                         "password": "password",
>>>                                                         "port": "1433",
>>>                                                         "host": "host",
>>>                                                         "username": "username"}
>>> })

delete(connection_id)

Delete a stored Connection.

Parameters

Important

1. connection_id: Unique id of the connection to be deleted.

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.connections.delete(connection_id)

get_datasource_type_uid_by_name(name)

Get stored datasource types id for the given datasource type name.

Parameters

Important

1. name: name of datasource type

   type: int

Output

Important

This method only prints the id of given datasource type name.

return type: Str

Example

>>> client.connections.get_datasource_type_uid_by_name('cloudobjectstorage')

get_details(connection_id)

Get connection details for the given unique Connection id.

Parameters

Important

1. connection_id: Unique id of Connection

   type: str

Output

Important

returns: Metadata of the stored Connection

return type: dict

Example

>>> connection_details = client.connections.get_details(connection_id)

static get_uid(connection_details)

Get Unique Id of stored connection.

Parameters

Important

1. connection_details: Metadata of the stored connection

   type: dict

Output

Important

returns: Unique Id of stored connection

return type: str

Example

>>> connection_uid = client.connection.get_uid(connection_details)

list()

List all stored connections.

Output

Important

This method only prints the list of all connections in a table format.

return type: None

Example

>>> client.connections.list()

list_datasource_types()

List stored datasource types assets.

Output

Important

This method only prints the list of datasources type in a table format.

return type: None

Example

>>> client.connections.list_datasource_types()

class metanames.ConnectionMetaNames

Set of MetaNames for Spaces Specs.

Available MetaNames:

MetaName	Type	Required	Schema
NAME	str	Y	my_space
DESCRIPTION	str	N	my_description
DATASOURCE_TYPE	str	Y	1e3363a5-7ccf-4fff-8022-4850a8024b68
PROPERTIES	dict	Y	{'database': 'BLUDB', 'host': 'dashdb-txn-sbox-yp-dal09-04.services.dal.bluemix.net', 'password': 'a1b2c3d4#', 'username': 'usr21370'}

Data assets

class client.Assets(client)

Store and manage your data assets.

ConfigurationMetaNames = <ibm_watson_machine_learning.metanames.AssetsMetaNames object>

MetaNames for Data Assets creation.

create(name, file_path)

Creates a data asset and uploads content to it.

Parameters

Important

1. name: Name to be given to the data asset

   type: str

2. file_path: Path to the content file to be uploaded

   type: str

Output

Important

returns: metadata of the stored data asset

return type: dict

Example

>>> asset_details = client.data_assets.create(name="sample_asset",file_path="/path/to/file")

delete(asset_uid)

Delete a stored data asset.

Parameters

Important

1. asset_uid: Unique Id of data asset

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.data_assets.delete(asset_uid)

download(asset_uid, filename)

Download the content of a data asset.

Parameters

Important

1. asset_uid: The Unique Id of the data asset to be downloaded

   type: str

2. filename: filename to be used for the downloaded file

   type: str

Output

Important

returns: Path to the downloaded asset content

return type: str

Example

>>> client.data_assets.download(asset_uid,"sample_asset.csv")

get_details(asset_uid)

Get data asset details.

Parameters

Important

1. asset_details: Metadata of the stored data asset

   type: dict

Output

Important

returns: Unique id of asset

return type: str

Example

>>> asset_details = client.data_assets.get_details(asset_uid)

static get_href(asset_details)

Get url of stored data asset.

Parameters

Important

1. asset_details: stored data asset details

   type: dict

Output

Important

returns: href of stored data asset

return type: str

Example

>>> asset_details = client.data_assets.get_details(asset_uid)
>>> asset_href = client.data_assets.get_href(asset_details)

static get_id(asset_details)

Get Unique Id of stored script asset.

Parameters

Important

1. asset_details: Metadata of the stored script asset

   type: dict

   type: dict

Output

Important

returns: Unique Id of stored data asset

return type: str

Example

>>> asset_id = client.data_assets.get_id(asset_details)

static get_uid(asset_details)

Get Unique Id of stored data asset. Deprecated!! Use get_id(details) instead

Parameters

Important

1. asset_details: Metadata of the stored data asset

   type: dict

   type: dict

Output

Important

returns: Unique Id of stored asset

return type: str

Example

>>> asset_uid = client.data_assets.get_uid(asset_details)

list(limit=None)

List stored data assets. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all data assets in a table format.

return type: None

Example

>>> client.data_assets.list()

store(meta_props)

Creates a data asset and uploads content to it.

Parameters

Important

1. meta_props: meta data of the space configuration. To see available meta names use:

   >>> client.data_assets.ConfigurationMetaNames.get()
   

   type: dict

Example

Example for data asset creation for files :

>>> metadata = {
>>>  client.data_assets.ConfigurationMetaNames.NAME: 'my data assets',
>>>  client.data_assets.ConfigurationMetaNames.DESCRIPTION: 'sample description',
>>>  client.data_assets.ConfigurationMetaNames.DATA_CONTENT_NAME: 'sample.csv'
>>> }
>>> asset_details = client.data_assets.store(meta_props=metadata)

Example of data asset creation using connection:

>>> metadata = {
>>>  client.data_assets.ConfigurationMetaNames.NAME: 'my data assets',
>>>  client.data_assets.ConfigurationMetaNames.DESCRIPTION: 'sample description',
>>>  client.data_assets.ConfigurationMetaNames.CONNECTION_ID: '39eaa1ee-9aa4-4651-b8fe-95d3ddae',
>>>  client.data_assets.ConfigurationMetaNames.DATA_CONTENT_NAME: 't1/sample.csv'
>>> }
>>> asset_details = client.data_assets.store(meta_props=metadata)

Example for data asset creation with database sources type connection:

>>> metadata = {
>>>  client.data_assets.ConfigurationMetaNames.NAME: 'my data assets',
>>>  client.data_assets.ConfigurationMetaNames.DESCRIPTION: 'sample description',
>>>  client.data_assets.ConfigurationMetaNames.CONNECTION_ID: '23eaf1ee-96a4-4651-b8fe-95d3dadfe',
>>>  client.data_assets.ConfigurationMetaNames.DATA_CONTENT_NAME: 't1'
>>> }
>>> asset_details = client.data_assets.store(meta_props=metadata)

class metanames.AssetsMetaNames

Set of MetaNames for Data Asset Specs.

Available MetaNames:

MetaName	Type	Required	Schema
NAME	str	Y	my_data_asset
DATA_CONTENT_NAME	str	Y	/test/sample.csv
CONNECTION_ID	str	N	39eaa1ee-9aa4-4651-b8fe-95d3ddae
DESCRIPTION	str	N	my_description

Deployments

class client.Deployments(client)

Deploy and score published artifacts (models and functions).

create(artifact_uid=None, meta_props=None, rev_id=None, **kwargs)

Create a deployment from an artifact. As artifact we understand model or function which may be deployed.

Parameters

Important

1. artifact_uid: Published artifact UID (model or function uid)

   type: str

2. meta_props: metaprops. To see the available list of metanames use:

   >>> client.deployments.ConfigurationMetaNames.get()
   

   type: dict

Output

Important

returns: metadata of the created deployment

return type: dict

Example

>>> meta_props = {
>>> wml_client.deployments.ConfigurationMetaNames.NAME: "SAMPLE DEPLOYMENT NAME",
>>> wml_client.deployments.ConfigurationMetaNames.ONLINE: {},
>>> wml_client.deployments.ConfigurationMetaNames.HARDWARE_SPEC : { "id":  "e7ed1d6c-2e89-42d7-aed5-8sb972c1d2b"}
>>> }
>>> deployment_details = client.deployments.create(artifact_uid, meta_props)

create_job(deployment_id, meta_props)

Create an asynchronous deployment job.

Parameters

Important

1. deployment_id: Unique Id of Deployment

   type: str

2. meta_props: metaprops. To see the available list of metanames use:

   >>> client.deployments.ScoringMetaNames.get() or client.deployments.DecisionOptimizationmetaNames.get()
   

   type: dict

Output

Important

returns: metadata of the created async deployment job

return type: dict

Note

• The valid payloads for scoring input are either list of values, pandas or numpy dataframes.

Example

>>> scoring_payload = {wml_client.deployments.ScoringMetaNames.INPUT_DATA: [{'fields': ['GENDER','AGE','MARITAL_STATUS','PROFESSION'], 'values': [['M',23,'Single','Student'],['M',55,'Single','Executive']]}]}
>>> async_job = client.deployments.create_job(deployment_id, scoring_payload)

delete(deployment_uid)

Delete deployment.

Parameters

Important

1. deployment uid: Unique Id of Deployment

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.deployments.delete(deployment_uid)

delete_job(job_uid, hard_delete=False)

Cancels a deployment job that is currenlty running. This method is also be used to delete metadata details of the completed or canceled jobs when hard_delete parameter is set to True.

Parameters

Important

1. job_uid: Unique Id of deployment job which should be canceled

   type: str

2. hard_delete: specify True or False.

   True - To delete the completed or canceled job. False - To cancel the currently running deployment job. Default value is False.

   type: Boolean

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.deployments.delete_job(job_uid)

download(virtual_deployment_uid, filename=None)

Downloads file deployment of specified deployment Id. Currently supported format is Core ML.

Parameters

Important

1. virtual_deployment_uid: Unique Id of virtual deployment.

   type: str

2. filename: filename of downloaded archive (optional).

   type: str

Output

Important

returns: Path to downloaded file.

return type: str

Example

>>> client.deployments.download(virtual_deployment_uid)

get_details(deployment_uid=None, limit=None)

Get information about your deployment(s). If deployment_uid is not passed, all deployment details are fetched.

Parameters

Important

1. deployment_uid: Unique Id of Deployment (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: metadata of deployment(s)

return type: dict

dict (if deployment_uid is not None) or {“resources”: [dict]} (if deployment_uid is None)

Note

If deployment_uid is not specified, all deployments metadata is fetched

Example

>>> deployment_details = client.deployments.get_details(deployment_uid)
>>> deployment_details = client.deployments.get_details(deployment_uid=deployment_uid)
>>> deployments_details = client.deployments.get_details()

static get_download_url(deployment_details)

Get deployment_download_url from deployment details.

Parameters

Important

1. deployment_details: Created deployment details.

   type: dict

Output

Important

returns: Deployment download URL that is used to get file deployment (for example: Core ML).

return type: str

Example

>>> deployment_url = client.deployments.get_download_url(deployment)

static get_href(deployment_details)

Get deployment_href from deployment details.

Parameters

Important

1. deployment_details: Metadata of the deployment.

   type: dict

Output

Important

returns: Deployment href that is used to manage the deployment.

return type: str

Example

>>> deployment_href = client.deployments.get_href(deployment)

static get_id(deployment_details)

Get deployment id from deployment details.

Parameters

Important

1. deployment_details: Metadata of the deployment

   type: dict

Output

Important

returns: deployment ID that is used to manage the deployment

return type: str

Example

>>> deployment_id = client.deployments.get_id(deployment)

get_job_details(job_uid=None, limit=None)

Get information about your deployment job(s). If deployment job_uid is not passed, all deployment jobs details are fetched.

Parameters

Important

1. job_uid: Unique Job ID (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: metadata of deployment job(s)

return type: dict

dict (if job_uid is not None) or {“resources”: [dict]} (if job_uid is None)

Note

If job_uid is not specified, all deployment jobs metadata associated with the deployment Id is fetched

Example

>>> deployment_details = client.deployments.get_job_details()
>>> deployments_details = client.deployments.get_job_details(job_uid=job_uid)

get_job_href(job_details)

Get the href of the deployment job.

Parameters

Important

1. job_details: metadata of the deployment job

   type: dict

Output

Important

returns: href of the deployment job

return type: str

Example

>>> job_details = client.deployments.get_job_details(job_uid=job_uid)
>>> job_status = client.deployments.get_job_href(job_details)

get_job_status(job_id)

Get the status of the deployment job.

Parameters

Important

1. job_id: Unique Id of the deployment job

   type: str

Output

Important

returns: status of the deployment job

return type: dict

Example

>>> job_status = client.deployments.get_job_status(job_uid)

get_job_uid(job_details)

Get the Unique Id of the deployment job.

Parameters

Important

1. job_details: metadata of the deployment job

   type: dict

Output

Important

returns: Unique Id of the deployment job

return type: str

Example

>>> job_details = client.deployments.get_job_details(job_uid=job_uid)
>>> job_status = client.deployments.get_job_uid(job_details)

static get_scoring_href(deployment_details)

Get scoring url from deployment details.

Parameters

Important

1. deployment_details: Metadata of the deployment

   type: dict

Output

Important

returns: scoring endpoint url that is used for making scoring requests

return type: str

Example

>>> scoring_href = client.deployments.get_scoring_href(deployment)

static get_uid(deployment_details)

Get deployment_uid from deployment details. Deprecated!! Use get_id(deployment_details) instead

Parameters

Important

1. deployment_details: Metadata of the deployment

   type: dict

Output

Important

returns: deployment UID that is used to manage the deployment

return type: str

Example

>>> deployment_uid = client.deployments.get_uid(deployment)

list(limit=None)

List deployments. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all deployments in a table format.

return type: None

Example

>>> client.deployments.list()

list_jobs(limit=None)

List the async deployment jobs. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all async jobs in a table format.

return type: None

Note

• This method list only async deployment jobs created for WML deployment.

Example

>>> client.deployments.list_jobs()

score(deployment_id, meta_props)

Make scoring requests against deployed artifact.

Parameters

Important

1. deployment_id: Unique Id of the deployment to be scored

   type: str

2. meta_props: Meta props for scoring

   >>> Use client.deployments.ScoringMetaNames.show() to view the list of ScoringMetaNames.
   

   type: dict

3. transaction_id: transaction id to be passed with records during payload logging (optional)

   type: str

Output

Important

returns: scoring result containing prediction and probability

return type: dict

Note

• client.deployments.ScoringMetaNames.INPUT_DATA is the only metaname valid for sync scoring.

• The valid payloads for scoring input are either list of values, pandas or numpy dataframes.

Example

>>> scoring_payload = {wml_client.deployments.ScoringMetaNames.INPUT_DATA:
>>>        [{'fields':
>>>            ['GENDER','AGE','MARITAL_STATUS','PROFESSION'],
>>>            'values': [
>>>                ['M',23,'Single','Student'],
>>>                ['M',55,'Single','Executive']
>>>                ]
>>>         }
>>>       ]}
>>> predictions = client.deployments.score(deployment_id, scoring_payload)
>>> predictions = client.deployments.score(deployment_id, scoring_payload,async=True)

update(deployment_uid, changes)

Updates existing deployment metadata. If ASSET is patched, then ‘id’ field is mandatory and it starts a deployment with the provided asset id/rev. Deployment id remains same

Parameters

Important

1. deployment_uid: Unique Id of deployment which should be updated

   type: str

2. changes: elements which should be changed, where keys are ConfigurationMetaNames

   type: dict

Output

Important

returns: metadata of updated deployment

return type: dict

Example

>>> metadata = {
>>> client.deployments.ConfigurationMetaNames.NAME:"updated_Deployment",
>>> client.deployments.ConfigurationMetaNames.ASSET: { "id": "ca0cd864-4582-4732-b365-3165598dc945", "rev":"2" }
>>> }
>>>
>>> deployment_details = client.deployments.update(deployment_uid, changes=metadata)

class metanames.DeploymentNewMetaNames

Set of MetaNames for Deployments Specs.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
TAGS	list	N	['string1', 'string2']	['string']
NAME	str	N	my_deployment
DESCRIPTION	str	N	my_deployment
CUSTOM	dict	N	{}
ASSET	dict	N	{'id': '4cedab6d-e8e4-4214-b81a-2ddb122db2ab', 'rev': '1'}
HARDWARE_SPEC	dict	N	{'id': '3342-1ce536-20dc-4444-aac7-7284cf3befc'}
HYBRID_PIPELINE_HARDWARE_SPECS	list	N	[{'node_runtime_id': 'autoai.kb', 'hardware_spec': {'id': '3342-1ce536-20dc-4444-aac7-7284cf3befc', 'num_nodes': '2'}}]
ONLINE	dict	N	{}
BATCH	dict	N	{}
R_SHINY	dict	N	{'authentication': 'anyone_with_url'}
VIRTUAL	dict	N	{}

class metanames.ScoringMetaNames

Set of MetaNames for Scoring.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
NAME	str	N	jobs test
INPUT_DATA	list	N	[{'fields': ['name', 'age', 'occupation'], 'values': [['john', 23, 'student']]}]	[{'name(optional)': 'string', 'id(optional)': 'string', 'fields(optional)': 'array[string]', 'values': 'array[array[string]]'}]
INPUT_DATA_REFERENCES	list	N		[{'id(optional)': 'string', 'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}, 'schema(optional)': {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}}]
OUTPUT_DATA_REFERENCE	dict	N		{'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}, 'schema(optional)': {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}}
EVALUATIONS_SPEC	list	N	[{'id': 'string', 'input_target': 'string', 'metrics_names': ['auroc', 'accuracy']}]	[{'id(optional)': 'string', 'input_target(optional)': 'string', 'metrics_names(optional)': 'array[string]'}]
ENVIRONMENT_VARIABLES	dict	N	{'my_env_var1': 'env_var_value1', 'my_env_var2': 'env_var_value2'}

class metanames.DecisionOptimizationMetaNames

Set of MetaNames for Decision Optimization.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
INPUT_DATA	list	N	[{'fields': ['name', 'age', 'occupation'], 'values': [['john', 23, 'student']]}]	[{'name(optional)': 'string', 'id(optional)': 'string', 'fields(optional)': 'array[string]', 'values': 'array[array[string]]'}]
INPUT_DATA_REFERENCES	list	N	[{'fields': ['name', 'age', 'occupation'], 'values': [['john', 23, 'student']]}]	[{'name(optional)': 'string', 'id(optional)': 'string', 'fields(optional)': 'array[string]', 'values': 'array[array[string]]'}]
OUTPUT_DATA	list	N		[{'name(optional)': 'string'}]
OUTPUT_DATA_REFERENCES	list	N		{'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}, 'schema(optional)': {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}}
SOLVE_PARAMETERS	dict	N

Hardware specifications

class client.HwSpec(client)

Store and manage your hardware specs.

ConfigurationMetaNames = <ibm_watson_machine_learning.metanames.HwSpecMetaNames object>

MetaNames for Hardware Specification.

get_details(hw_spec_uid)

Get hardware specification details.

Parameters

Important

hw_spec_uid : Unique id of the hardware spec ** type**: str

Output

Important

returns: Metadata of the hardware specifications

return type: dict

Example

>>> hw_spec_details = client.hardware_specifications.get_details(hw_spec_uid)

static get_href(hw_spec_details)

Get url of hardware specifications.

Parameters

Important

1. hw_spec_details: hardware specifications details

   type: dict

Output

Important

returns: href of hardware specifications

return type: str

Example

>>> hw_spec_details = client.hw_spec.get_details(hw_spec_uid)
>>> hw_spec_href = client.hw_spec.get_href(hw_spec_details)

static get_id(hw_spec_details)

Get ID of hardware specifications asset.

Parameters

Important

1. asset_details: Metadata of the hardware specifications

   type: dict

Output

Important

returns: Unique Id of hardware specifications

return type: str

Example

>>> asset_uid = client.hardware_specifications.get_id(hw_spec_details)

get_id_by_name(hw_spec_name)

Get Unique Id of hardware specification for the given name.

Parameters

Important

1. hw_spec_name: name of the hardware spec

   type: str

Output

Important

returns: Unique Id of hardware specification

return type: str

Example

>>> asset_uid = client.hardware_specifications.get_id_by_name(hw_spec_name)

static get_uid(hw_spec_details)

Get UID of hardware specifications asset. Deprecated!! Use get_id(hw_spec_details) instead

Parameters

Important

1. asset_details: Metadata of the hardware specifications

   type: dict

Output

Important

returns: Unique Id of hardware specifications

return type: str

Example

>>> asset_uid = client.hardware_specifications.get_uid(hw_spec_details)

get_uid_by_name(hw_spec_name)

Get Unique Id of hardware specification for the given name. Deprecated!! Use get_id_by_name(self, hw_spec_name) instead

Parameters

Important

1. hw_spec_name: name of the hardware spec

   type: str

Output

Important

returns: Unique Id of hardware specification

return type: str

Example

>>> asset_uid = client.hardware_specifications.get_uid_by_name(hw_spec_name)

list(name=None)

List hardware specifications.

Parameters

Important

name : Unique id of the hardware spec ** type**: str

Output

Important

This method only prints the list of all assets in a table format.

return type: None

Example

>>> client.hardware_specifications.list()

class metanames.HwSpecMetaNames

Set of MetaNames for Software Specifications Specs.

Available MetaNames:

MetaName	Type	Required	Schema
NAME	str	Y	Python 3.6 with pre-installed ML package
DESCRIPTION	str	N	my_description
HARDWARE_CONFIGURATION	dict	N	{}

Model definitions

class client.ModelDefinition(client)

Store and manage your model_definitions.

ConfigurationMetaNames = <ibm_watson_machine_learning.metanames.ModelDefinitionMetaNames object>

MetaNames for model_definition creation.

create_revision(model_definition_uid)

Creates revision for the given model_definition. Revisions are immutable once created. The metadata and attachment at model_definition is taken and a revision is created out of it.

Parameters

Important

1. model_definition: model_definition ID.

   type: str

Output

Important

returns: Stored model_definition revisions metadata.

return type: dict

Example

>>> model_definition_revision = client.model_definitions.create_revision(model_definition_id)

delete(model_definition_uid)

Delete a stored model_definition.

Parameters

Important

1. model_definition_uid: Unique Id of stored Model definition

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.model_definitions.delete(model_definition_uid)

download(model_definition_uid, filename, rev_id=None)

Download the content of a script asset.

Parameters

Important

1. model_definition_uid: The Unique Id of the model_definition asset to be downloaded

   type: str

2. filename: filename to be used for the downloaded file

   type: str

3. rev_id: Revision id

   type: str

Output

Important

returns: Path to the downloaded asset content

return type: str

Example

>>> client.script.download(asset_uid,"script_file.zip")

get_details(model_definition_uid)

Get metadata of stored model_definition.

Parameters

Important

1. model_definition_uid: Unique Id of model_definition

   type: str

Output

Important

returns: metadata of model definition

return type: dict (if model_definition_uid is not None)

Example

>>> model_definition_details = client.model_definitions.get_details(model_definition_uid)

get_href(model_definition_details)

Get href of stored model_definition.

Parameters

Important

1. model_definition_details: Stored model_definition details.

   type: dict

Output

Important

returns: href of stored model_definition.

return type: str

Example

>>> model_definition_uid = client.model_definitions.get_href(model_definition_details)

get_id(model_definition_details)

Get Unique Id of stored model_definition asset.

Parameters

Important

1. model_definition_details: Metadata of the stored model_definition asset

   type: dict

Output

Important

returns: Unique Id of stored model_definition asset

return type: str

Example

>>> asset_uid = client.model_definition.get_id(asset_details)

get_revision_details(model_definition_uid, rev_uid=None)

Get metadata of model_definit

Parameters

Important

1. model_definition_uid: model_definition ID.

   type: str

2. rev_uid: Revision ID. If this parameter is not provided, returns latest revision if existing else error.

   type: int

Output

Important

returns: Stored model definitions metadata.

return type: dict

Example

>>> script_details = client.model_definitions.get_revision_details(model_definition_uid, rev_uid)

get_uid(model_definition_details)

Get uid of stored model. Deprecated!! Use get_id(model_definition_details) instead

Parameters

Important

1. param model_definition_details: stored model_definition details

   type: dict

Output

Important

returns: uid of stored model_definition

return type: str

Example

>>> model_definition_uid = client.model_definitions.get_uid(model_definition_details)

list(limit=None)

List stored model_definition assets. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all model_definition assets in a table format.

return type: None

Example

>>> client.model_definitions.list()

list_revisions(model_definition_uid, limit=None)

List stored model_definition assets. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. model_definition_uid: Unique id of model_definition

   type: str

2. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all model_definition revision in a table format.

return type: None

Example

>>> client.model_definitions.list_revisions()

store(model_definition, meta_props)

Create a model_definitions.

Parameters

Important

1. meta_props: meta data of the model_definition configuration. To see available meta names use:

   >>> client.model_definitions.ConfigurationMetaNames.get()
   

   type: dict

2. model_definition: Path to the content file to be uploaded

   type: str

Output

Important

returns: Metadata of the model_defintion created

return type: dict

Example

>>>  client.model_definitions.store(model_definition, meta_props)

update(model_definition_id, meta_props=None, file_path=None)

Update model_definition with either metadata or attachment or both.

Parameters

Important

1. model_definition_id: model_definition ID.

   type: str

2. file_path: Path to the content file to be uploaded.

   type: str

** Output**

Important

returns: Updated metadata of model_definition.

return type: dict

Example

>>> model_definition_details = client.model_definition.update(model_definition_id, meta_props, file_path)

class metanames.ModelDefinitionMetaNames

Set of MetaNames for Model Definition.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
NAME	str	Y	my_model_definition
DESCRIPTION	str	N	my model_definition
PLATFORM	dict	Y	{'name': 'python', 'versions': ['3.5']}	{'name(required)': 'string', 'versions(required)': ['versions']}
VERSION	str	Y	1.0
COMMAND	str	N	python3 convolutional_network.py
CUSTOM	dict	N	{'field1': 'value1'}
SPACE_UID	str	N	3c1ce536-20dc-426e-aac7-7284cf3befc6

Package extensions

class client.PkgExtn(client)

Store and manage your software Packages Extension specs.

ConfigurationMetaNames = <ibm_watson_machine_learning.metanames.PkgExtnMetaNames object>

MetaNames for Package Extensions creation.

delete(pkg_extn_uid)

Delete a package extension.

Parameters

Important

1. pkg_extn_uid: Unique Id of package extension

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.package_extensions.delete(pkg_extn_uid)

download(pkg_extn_id, filename)

Download a package extension.

Parameters

Important

1. pkg_extn_uid: The Unique Id of the package extension to be downloaded

   type: str

2. filename: filename to be used for the downloaded file

   type: str

Output

Important

returns: Path to the downloaded package extension content

return type: str

Example

>>> client.assets.download(asset_uid,"sample_conda.yml/custom_library.zip")

get_details(pkg_extn_uid)

Get package extensions details.

Parameters

Important

1. pkg_extn_details: Metadata of the package extensions

   type: dict

Output

Important

returns: pkg_extn UID

return type: str

Example

>>> pkg_extn_details = client.pkg_extn.get_details(pkg_extn_uid)

static get_href(pkg_extn_details)

Get url of stored package extensions.

Parameters

Important

1. asset_details: package extensions details

   type: dict

Output

Important

returns: href of package extensions details

return type: str

Example

>>> pkg_extn_details = client.package_extensions.get_details(pkg_extn_uid)
>>> pkg_extn_href = client.package_extensions.get_href(pkg_extn_details)

static get_id(pkg_extn_details)

Get Unique Id of package extensions.

Parameters

Important

1. asset_details: Metadata of the package extensions

   type: dict

Output

Important

returns: Unique Id of package extension

return type: str

Example

>>> asset_id = client.package_extensions.get_id(pkg_extn_details)

get_id_by_name(pkg_extn_name)

Get ID of package extensions.

Parameters

Important

1. asset_details: Metadata of the package extension

   type: dict

Output

Important

returns: Unique Id of package extension

return type: str

Example

>>> asset_id = client.package_extensions.get_id_by_name(pkg_extn_name)

static get_uid(pkg_extn_details)

Get Unique Id of package extensions. Deprecated!! use get_id(pkg_extn_details) instead

Parameters

Important

1. asset_details: Metadata of the package extensions

   type: dict

Output

Important

returns: Unique Id of package extension

return type: str

Example

>>> asset_uid = client.package_extensions.get_uid(pkg_extn_details)

get_uid_by_name(pkg_extn_name)

Get UID of package extensions. Deprecated!! Use get_id_by_name(pkg_extn_name) instead

Parameters

Important

1. asset_details: Metadata of the package extension

   type: dict

Output

Important

returns: Unique Id of package extension

return type: str

Example

>>> asset_uid = client.package_extensions.get_uid_by_name(pkg_extn_name)

list()

List package extensions.

Output

Important

This method only prints the list of all package extensionss in a table format.

return type: None

Example

>>> client.package_extensions.list()

store(meta_props, file_path)

Create a package extensions.

Parameters

Important

1. meta_props: meta data of the pacakge extension. To see available meta names use:

   >>> client.package_extensions.ConfigurationMetaNames.get()
   

   type: dict

Output

Important

returns: metadata of the package extensions

return type: dict

Example

>>> meta_props = {
>>>    client.package_extensions.ConfigurationMetaNames.NAME: "skl_pipeline_heart_problem_prediction",
>>>    client.package_extensions.ConfigurationMetaNames.DESCRIPTION: "description scikit-learn_0.20",
>>>    client.package_extensions.ConfigurationMetaNames.TYPE: "conda_yml",
>>> }

Example

>>> pkg_extn_details = client.package_extensions.store(meta_props=meta_props,file_path="/path/to/file")

class metanames.PkgExtnMetaNames

Set of MetaNames for Package Extensions Specs.

Available MetaNames:

MetaName	Type	Required	Schema
NAME	str	Y	Python 3.6 with pre-installed ML package
DESCRIPTION	str	N	my_description
TYPE	str	Y	conda_yml/custom_library

Repository

class client.Repository(client)

Store and manage your models, functions, spaces, pipelines and experiments using Watson Machine Learning Repository.

Important

1. To view ModelMetaNames, use:

   >>> client.repository.ModelMetaNames.show()
   

2. To view ExperimentMetaNames, use:

   >>> client.repository.ExperimentMetaNames.show()
   

3. To view FunctionMetaNames, use:

   >>> client.repository.FunctionMetaNames.show()
   

4. To view PipelineMetaNames, use:

   >>> client.repository.PipelineMetaNames.show()
   

clone(artifact_id, space_id=None, action='copy', rev_id=None)

Creates a new resource(models, runtimes, libraries, experiments, functions, pipelines) identical with the model either in the same space or in a new space. All dependent assets will be cloned too.

Parameters

Important

1. model_id: Guid of the artifact to be cloned:

   type: str

2. space_id: Guid of the space to which the model needs to be cloned. (optional)

   type: str

3. action: Action specifying “copy” or “move”. (optional)

   type: str

4. rev_id: Revision ID of the artifact. (optional)

   type: str

Output

Important

returns: Metadata of the model cloned.

return type: dict

Example

>>> client.repository.clone(artifact_id=artifact_id,space_id=space_uid,action="copy")

Note

• If revision id is not specified, all revisions of the artifact are cloned

• Default value of the parameter action is copy

• Space guid is mandatory for move action

create_experiment_revision(experiment_uid)

Create a new version for a experiment.

Parameters

Important

1. experiment_uid: Unique ID of the experiment.

   type: str

Output

Important

returns: experiment version details.

return type: dict

Example

>>> stored_experiment_revision_details = client.repository.create_experiment_revision(experiment_uid)

create_function_revision(function_uid)

Create a new version for a function.

Parameters

Important

1. function_uid: Unique ID of the function.

   type: str

Output

Important

returns: Function version details.

return type: dict

Example

>>> stored_function_revision_details = client.repository.create_function_revision( function_uid)

create_member(space_uid, meta_props)

Create a member within a space.

Parameters

Important

1. meta_props: meta data of the member configuration. To see available meta names use:

   >>> client.spaces.ConfigurationMetaNames.get()
   

   type: dict

Output

Important

returns: metadata of the stored member

return type: dict

Note

• client.spaces.MemberMetaNames.ROLE can be any one of the following “viewer, editor, admin”

• client.spaces.MemberMetaNames.IDENTITY_TYPE can be any one of the following “user,service”

• client.spaces.MemberMetaNames.IDENTITY can be either service-ID or IAM-userID

Example

>>> metadata = {
>>>  client.spaces.MemberMetaNames.ROLE:"Admin",
>>>  client.spaces.MemberMetaNames.IDENTITY:"iam-ServiceId-5a216e59-6592-43b9-8669-625d341aca71",
>>>  client.spaces.MemberMetaNames.IDENTITY_TYPE:"service"
>>> }
>>> members_details = client.repository.create_member(space_uid=space_id, meta_props=metadata)

create_model_revision(model_uid)

Create a new version for a model.

Parameters

Important

1. model_uid: Model ID.

   type: str

Output

Important

returns: Model version details.

return type: dict

Example

>>> stored_model_revision_details = client.repository.create_model_revision( model_uid="MODELID")

create_pipeline_revision(pipeline_uid)

Create a new version for a model.

Parameters

Important

1. pipeline_uid: Unique ID of the Pipeline.

   type: str

Output

Important

returns: Pipeline version details.

return type: dict

Example

>>> stored_pipeline_revision_details = client.repository.create_pipeline_revision( pipeline_uid)

create_revision(artifact_uid)

Create revision for passed artifact_uid.

Parameters

Important

1. artifact_uid: Unique id of stored model, experiment, function or pipelines.

   type: str

Output

Important

returns: Artifact new revision metadata.

return type: dict

Example

>>> details = client.repository.create_revision(artifact_uid)

delete(artifact_uid)

Delete model, experiment, pipeline, space, runtime, library or function from repository.

Parameters

Important

1. artifact_uid: Unique id of stored model, experiment, function, pipeline, space, library or runtime

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.repository.delete(artifact_uid)

download(artifact_uid, filename='downloaded_artifact.tar.gz', rev_uid=None, format=None)

Downloads configuration file for artifact with specified uid.

Parameters

Important

1. artifact_uid: Unique Id of model, function, runtime or library

   type: str

2. filename: Name of the file to which the artifact content has to be downloaded

   default value: downloaded_artifact.tar.gz

   type: str

Output

Important

returns: Path to the downloaded artifact content

return type: str

Note

If filename is not specified, the default filename is “downloaded_artifact.tar.gz”.

Example

>>> client.repository.download(model_uid, 'my_model.tar.gz')

get_details(artifact_uid=None)

Get metadata of stored artifacts. If artifact_uid is not specified returns all models, experiments, functions, pipelines, spaces, libraries and runtimes metadata.

Parameters

Important

1. artifact_uid: Unique Id of stored model, experiment, function, pipeline, space, library or runtime (optional)

   type: str

Output

Important

returns: stored artifact(s) metadata

return type: dict

dict (if artifact_uid is not None) or {“resources”: [dict]} (if artifact_uid is None)

Note

If artifact_uid is not specified, all models, experiments, functions, pipelines, spaces, libraries and runtimes metadata is fetched

Example

>>> details = client.repository.get_details(artifact_uid)
>>> details = client.repository.get_details()

get_experiment_details(experiment_uid=None, limit=None)

Get metadata of experiment. If no experiment_uid is specified all experiments metadata is returned.

Parameters

Important

1. experiment_uid: Unique Id of experiment (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: experiment(s) metadata

return type: dict

dict (if experiment_uid is not None) or {“resources”: [dict]} (if experiment_uid is None)

Note

If experiment_uid is not specified, all experiments metadata is fetched

Example

>>> experiment_details = client.respository.get_experiment_details(experiment_uid)

static get_experiment_href(experiment_details)

Get href of stored experiment.

Parameters

Important

1. experiment_details: Metadata of the stored experiment

   type: dict

Output

Important

returns: href of stored experiment

return type: str

Example

>>> experiment_details = client.repository.get_experiment_detailsf(experiment_uid)
>>> experiment_href = client.repository.get_experiment_href(experiment_details)

static get_experiment_id(experiment_details)

Get Unique Id of stored experiment.

Parameters

Important

1. experiment_details: Metadata of the stored experiment

   type: dict

Output

Important

returns: Unique Id of stored experiment

return type: str

Example

>>> experiment_details = client.repository.get_experiment_details(experiment_uid)
>>> experiment_uid = client.repository.get_experiment_id(experiment_details)

get_experiment_revision_details(experiment_uid, rev_id)

Get metadata of experiment revision.

Parameters

Important

1. experiment_uid: Unique Id of experiment

   type: str

2. rev_id: Unique id of experiment revision

   type: str

Output

Important

returns: experiment revision metadata

return type: dict

Example

>>> experiment_rev_details = client.respository.get_experiment__revision_details(experiment_uid, rev_uid)

static get_experiment_uid(experiment_details)

Get Unique Id of stored experiment.

Parameters

Important

1. experiment_details: Metadata of the stored experiment

   type: dict

Output

Important

returns: Unique Id of stored experiment

return type: str

Example

>>> experiment_details = client.repository.get_experiment_detailsf(experiment_uid)
>>> experiment_uid = client.repository.get_experiment_uid(experiment_details)

get_function_details(function_uid=None, limit=None)

Get metadata of function. If no function_uid is specified all functions metadata is returned.

Parameters

Important

1. function_uid: Unique Id of function (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: function(s) metadata

return type: dict (if function_uid is not None) or {“resources”: [dict]} (if function_uid is None)

Note

If function_uid is not specified, all functions metadata is fetched

Example

>>> function_details = client.respository.get_function_details(function_uid)
>>> function_details = client.respository.get_function_details()

static get_function_href(function_details)

Get href of stored function.

Parameters

Important

1. function_details: Metadata of the stored function

   type: dict

Output

Important

returns: href of stored function

return type: str

Example

>>> function_details = client.repository.get_function_detailsf(function_uid)
>>> function_url = client.repository.get_function_href(function_details)

static get_function_id(function_details)

Get Id of stored function.

Parameters

Important

1. function_details: Metadata of the stored function

   type: dict

Output

Important

returns: Id of stored function

return type: str

Example

>>> function_details = client.repository.get_function_details(function_uid)
>>> function_id = client.repository.get_function_id(function_details)

get_function_revision_details(function_uid, rev_id)

Get metadata of function revision.

Parameters

Important

1. function_uid: Unique Id of function

   type: str

2. rev_id: Unique Id of function revision

   type: str

Output

Important

returns: function revision metadata

return type: dict

Example

>>> function_rev_details = client.respository.get_function_revision_details(function_uid, rev_id)

static get_function_uid(function_details)

Get Unique Id of stored function. Deprecated!! Use get_function_id(function_details) instead

Parameters

Important

1. function_details: Metadata of the stored function

   type: dict

Output

Important

returns: Unique Id of stored function

return type: str

Example

>>> function_details = client.repository.get_function_detailsf(function_uid)
>>> function_uid = client.repository.get_function_uid(function_details)

static get_member_href(member_details)

Get member_href from member details.

Parameters

Important

1. space_details: Metadata of the stored member

   type: dict

Output

Important

returns: member href

return type: str

Example

>>> member_details = client.repository.get_member_details(member_id)
>>> member_href = client.repository.get_member_href(member_details)

static get_member_uid(member_details)

Get member_uid from member details.

Parameters

Important

1. member_details: Metadata of the created member

   type: dict

Output

Important

returns: unique id of member

return type: str

Example

>>> member_details = client.repository.get_member_details(member_id)
>>> member_id = client.repository.get_member_uid(member_details)

get_members_details(space_uid, member_id=None, limit=None)

Get metadata of members associated with a space. If member_uid is not specified, it returns all the members metadata.

Parameters

Important

1. space_uid: Unique id of member (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: metadata of member(s) of a space

return type: dict (if member_id is not None) or {“resources”: [dict]} (if member_id is None)

Note

If member id is not specified, all members metadata is fetched

Example

>>> member_details = client.repository.get_member_details(space_uid,member_id)

get_model_details(model_uid=None, limit=None)

Get metadata of stored model. If model_uid is not specified returns all models metadata.

Parameters

Important

1. model_uid: Unique Id of Model (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: metadata of model(s)

return type: dict (if model_uid is not None) or {“resources”: [dict]} (if model_uid is None)

Note

If model_uid is not specified, all models metadata is fetched

Example

>>> model_details = client.repository.get_model_details(model_uid)
>>> models_details = client.repository.get_model_details()

static get_model_href(model_details)

Get href of stored model.

Parameters

Important

1. model_details: Metadata of the stored model

   type: dict

Output

Important

returns: href of stored model

return type: str

Example

>>> model_details = client.repository.get_model_detailsf(model_uid)
>>> model_uid = client.repository.get_model_href(model_details)

static get_model_id(model_details)

Get Unique Id of stored model.

Parameters

Important

1. model_details: Metadata of the stored model

   type: dict

Output

Important

returns: Unique Id of stored model

return type: str

Example

>>> model_details = client.repository.get_model_details(model_uid)
>>> model_uid = client.repository.get_model_id(model_details)

get_model_revision_details(model_uid, rev_uid)

Get metadata of model revision.

Parameters

Important

1. experiment_uid: Unique Id of model

   type: str

2. limit: Unique id of model revision

   type: str

Output

Important

returns: model revision metadata

return type: dict

Example

>>> model_rev_details = client.respository.get_model_revision_details(model_uid, rev_uid)

static get_model_uid(model_details)

Get Unique Id of stored model.

Parameters

Important

1. model_details: Metadata of the stored model

   type: dict

Output

Important

returns: Unique Id of stored model

return type: str

Example

>>> model_details = client.repository.get_model_details(model_uid)
>>> model_uid = client.repository.get_model_uid(model_details)

get_pipeline_details(pipeline_uid=None, limit=None)

Get metadata of stored pipelines. If pipeline_uid is not specified returns all pipelines metadata.

Parameters

Important

1. pipeline_uid: Unique id of Pipeline(optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: metadata of pipeline(s)

return type: dict (if pipeline_uid is not None) or {“resources”: [dict]} (if pipeline_uid is None)

Note

If pipeline_uid is not specified, all pipelines metadata is fetched

Example

>>> pipeline_details = client.repository.get_pipeline_details(pipeline_uid)
>>> pipeline_details = client.repository.get_pipeline_details()

static get_pipeline_href(pipeline_details)

Get pipeline_hef from pipeline details.

Parameters

Important

1. pipeline_details: Metadata of the stored pipeline

   type: dict

Output

Important

returns: pipeline href

return type: str

Example

>>> pipeline_details = client.repository.get_pipeline_details(pipeline_uid)
>>> pipeline_href = client.repository.get_pipeline_href(pipeline_details)

static get_pipeline_id(pipeline_details)

Get pipeline_uid from pipeline details.

Parameters

Important

1. pipeline_details: Metadata of the stored pipeline

   type: dict

Output

Important

returns: Unique Id of pipeline

return type: str

Example

>>> pipeline_details = client.repository.get_pipeline_details(pipeline_uid)
>>> pipeline_uid = client.repository.get_pipeline_id(pipeline_details)

get_pipeline_revision_details(pipeline_uid, rev_id)

Get metadata of stored pipeline revision.

Parameters

Important

1. pipeline_uid: Unique id of Pipeline

   type: str

2. rev_id: Unique id Pipeline revision

   type: str

Output

Important

returns: metadata of revision pipeline(s)

return type: dict

Example

>>> pipeline_rev_details = client.repository.get_pipeline_revision_details(pipeline_uid, rev_id)

static get_pipeline_uid(pipeline_details)

Get pipeline_uid from pipeline details.

Parameters

Important

1. pipeline_details: Metadata of the stored pipeline

   type: dict

Output

Important

returns: Unique Id of pipeline

return type: str

Example

>>> pipeline_details = client.repository.get_pipeline_details(pipeline_uid)
>>> pipeline_uid = client.repository.get_pipeline_uid(pipeline_details)

get_space_details(space_uid=None, limit=None)

Get metadata of stored space. If space_uid is not specified returns all model spaces metadata.

Parameters

Important

1. space_uid: Unique id of Space (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: metadata of space(s)

return type: dict (if space_uid is not None) or {“resources”: [dict]} (if space_uid is None)

Note

If space_uid is not specified, all spaces metadata is fetched

Example

>>> space_details = client.repository.get_space_details(space_uid)
>>> space_details = client.repository.get_space_details()

static get_space_href(space_details)

Get space_href from space details.

Parameters

Important

1. space_details: Metadata of the stored space

   type: dict

Output

Important

returns: space href

return type: str

Example

>>> space_details = client.repository.get_space_details(space_uid)
>>> space_href = client.repository.get_space_href(space_details)

static get_space_uid(space_details)

Get space_uid from space details.

Parameters

Important

1. space_details: Metadata of the stored space

   type: dict

Output

Important

returns: Unique Id of space

return type: str

Example

>>> space_details = client.repository.get_space_details(space_uid)
>>> space_uid = client.repository.get_space_uid(space_details)

list()

List stored models, pipelines, runtimes, libraries, functions, spaces and experiments. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all models, pipelines, runtimes, libraries, functions, spaces and experiments in a table format.

return type: None

Example

>>> client.repository.list()

list_experiments(limit=None)

List stored experiments. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all experiments in a table format.

return type: None

Example

>>> client.repository.list_experiments()

list_experiments_revisions(experiment_uid, limit=None)

List stored experiment revisions. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. experiment_uid: Uniquie Id of the experiment

   type: str

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all revisions of given experiment ID in a table format.

return type: None

Example

>>> client.repository.list_experiments_revisions(experiment_uid)

list_functions(limit=None)

List stored functions. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all functions in a table format.

return type: None

Example

>>> client.respository.list_functions()

list_functions_revisions(function_uid, limit=None)

List stored function revisions. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. function_uid: Uniquie Id of the function

   type: str

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all revisions of given function ID in a table format.

return type: None

Example

>>> client.repository.list_functions_revisions(function_uid)

list_members(space_uid, limit=None)

List stored members of a space. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all members associated with a space in a table format.

return type: None

Example

>>> client.spaces.list_members()

list_models(limit=None)

List stored models. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all models in a table format.

return type: None

Example

>>> client.repository.list_models()

list_models_revisions(model_uid, limit=None)

List stored model revisions. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. model_uid: Uniquie Id of the model

   type: str

2. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all revisions of given model ID in a table format.

return type: None

Example

>>> client.repository.list_models_revisions(model_uid)

list_pipelines(limit=None)

List stored pipelines. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all pipelines in a table format.

return type: None

Example

>>> client.repository.list_pipelines()

list_pipelines_revisions(pipeline_uid, limit=None)

List stored pipeline revisions. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. model_uid: Uniquie Id of the pipeline

   type: str

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all revisions of given pipeline ID in a table format.

return type: None

Example

>>> client.repository.list_pipelines_revisions(pipeline_uid)

list_spaces(limit=None)

List stored spaces. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all spaces in a table format.

return type: None

Example

>>> client.repository.list_spaces()

load(artifact_uid)

Load model from repository to object in local environment.

Parameters

Important

1. artifact_uid: Unique Id of model

   type: str

Output

Important

returns: model object

return type: object

Example

>>> model_obj = client.repository.load(model_uid)

store_experiment(meta_props)

Create an experiment.

Parameters

Important

1. meta_props: meta data of the experiment configuration. To see available meta names use:

   >>> client.experiments.ConfigurationMetaNames.get()
   

   type: dict

Output

Important

returns: Metadata of the experiment created

return type: dict

Example

>>> metadata = {
>>>  client.experiments.ConfigurationMetaNames.NAME: 'my_experiment',
>>>  client.experiments.ConfigurationMetaNames.EVALUATION_METRICS: ['accuracy'],
>>>  client.experiments.ConfigurationMetaNames.TRAINING_REFERENCES: [
>>>      {
>>>        'pipeline': {'href': pipeline_href_1}
>>>      },
>>>      {
>>>        'pipeline': {'href':pipeline_href_2}
>>>      },
>>>   ]
>>> }
>>> experiment_details = client.repository.store_experiment(meta_props=metadata)
>>> experiment_href = client.repository.get_experiment_href(experiment_details)

store_function(function, meta_props)

Create a function.

Parameters

Important

1. meta_props: meta data or name of the function. To see available meta names use:

   >>> client.repository.FunctionMetaNames.show()
   

   type: dict

2. function: path to file with archived function content or function (as described above)

   • As a ‘function’ may be used one of the following:

   • filepath to gz file

   • ‘score’ function reference, where the function is the function which will be deployed

   • generator function, which takes no argument or arguments which all have primitive python default values and as result return ‘score’ function

   type: str or function

Output

Important

returns: Metadata of the function created.

return type: dict

Example

The most simple use is (using score function):

>>> meta_props = {
>>>    client.repository.FunctionMetaNames.NAME: "function",
>>>    client.repository.FunctionMetaNames.DESCRIPTION: "This is ai function",
>>>    client.repository.FunctionMetaNames.SOFTWARE_SPEC_UID: "53dc4cf1-252f-424b-b52d-5cdd9814987f"}

>>> def score(payload):
>>>      values = [[row[0]*row[1]] for row in payload['values']]
>>>      return {'fields': ['multiplication'], 'values': values}
>>> stored_function_details = client.repository.store_function(score, meta_props)

Other, more interesting example is using generator function. In this situation it is possible to pass some variables:

>>> wml_creds = {...}
>>> def gen_function(wml_credentials=wml_creds, x=2):
>>>        def f(payload):
>>>            values = [[row[0]*row[1]*x] for row in payload['values']]
>>>            return {'fields': ['multiplication'], 'values': values}
>>>        return f
>>> stored_function_details = client.repository.store_function(gen_function, meta_props)

store_model(model, meta_props=None, training_data=None, training_target=None, pipeline=None, feature_names=None, label_column_names=None, subtrainingId=None)

Create a model.

Parameters

Important

1. model:

   Can be one of following:

   • The train model object:

     • scikit-learn

     • xgboost

     • spark (PipelineModel)

   • path to saved model in format:

     • keras (.tgz)

     • pmml (.xml)

     • scikit-learn (.tar.gz)

     • tensorflow (.tar.gz)

     • spss (.str)

   • directory containing model file(s):

     • scikit-learn

     • xgboost

     • tensorflow

   • unique id of trained model

2. training_data: Spark DataFrame supported for spark models. Pandas dataframe, numpy.ndarray or array supported for scikit-learn models

   type: spark dataframe, pandas dataframe, numpy.ndarray or array

3. meta_props: meta data of the models configuration. To see available meta names use:

   >>> client.repository.ModelMetaNames.get()
   

   type: dict

4. training_target: array with labels required for scikit-learn models

   type: array

5. pipeline: pipeline required for spark mllib models

   type: object

6. feature_names: Feature names for the training data in case of Scikit-Learn/XGBoost models. This is applicable only in the case where the training data is not of type - pandas.DataFrame.

   type: numpy.ndarray or list

7. label_column_names: Label column names of the trained Scikit-Learn/XGBoost models.

   type: numpy.ndarray and list

Output

Important

returns: Metadata of the model created

return type: dict

Note

• For a keras model, model content is expected to contain a .h5 file and an archived version of it.

• feature_names is an optional argument containing the feature names for the training data in case of Scikit-Learn/XGBoost models. Valid types are numpy.ndarray and list. This is applicable only in the case where the training data is not of type - pandas.DataFrame.

• If the training data is of type pandas.DataFrame and feature_names are provided, feature_names are ignored.

• The value can be a single dictionary(being deprecated, use list even for single schema) or a list if you are using single input data schema. you can provide multiple schemas as dictionaries inside a list.

Example

>>> stored_model_details = client.repository.store_model(model, name)

In more complicated cases you should create proper metadata, similar to this one:

>>> sw_spec_id = client.software_specifications.get_id_by_name('scikit-learn_0.23-py3.7')
>>> sw_spec_id
>>> metadata = {
>>>        client.repository.ModelMetaNames.NAME: 'customer satisfaction prediction model',
>>>        client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: sw_spec_id,
>>>        client.repository.ModelMetaNames.TYPE: 'scikit-learn_0.23'
>>>}

In case when you want to provide input data schema of the model, you can provide it as part of meta

>>> sw_spec_id = client.software_specifications.get_id_by_name('spss-modeler_18.1')
>>> sw_spec_id
>>> metadata = {
>>>        client.repository.ModelMetaNames.NAME: 'customer satisfaction prediction model',
>>>        client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: sw_spec_id,
>>>        client.repository.ModelMetaNames.TYPE: 'spss-modeler_18.1',
>>>        client.repository.ModelMetaNames.INPUT_DATA_SCHEMA: [{'id': 'test',
>>>                                                             'type': 'list',
>>>                                                             'fields': [{'name': 'age', 'type': 'float'},
>>>                                                                        {'name': 'sex', 'type': 'float'},
>>>                                                                         {'name': 'fbs', 'type': 'float'},
>>>                                                                         {'name': 'restbp', 'type': 'float'}]
>>>                                                               },
>>>                                                               {'id': 'test2',
>>>                                                                'type': 'list',
>>>                                                                'fields': [{'name': 'age', 'type': 'float'},
>>>                                                                           {'name': 'sex', 'type': 'float'},
>>>                                                                           {'name': 'fbs', 'type': 'float'},
>>>                                                                           {'name': 'restbp', 'type': 'float'}]
>>>                                                               }]
>>>             }

A way you might use me with local tar.gz containing model:

>>> stored_model_details = client.repository.store_model(path_to_tar_gz, meta_props=metadata, training_data=None)

A way you might use me with local directory containing model file(s):

>>> stored_model_details = client.repository.store_model(path_to_model_directory, meta_props=metadata, training_data=None)

A way you might use me with trained model guid:

>>> stored_model_details = client.repository.store_model(trained_model_guid, meta_props=metadata, training_data=None)

store_pipeline(meta_props)

Create a pipeline.

Parameters

Important

1. meta_props: meta data of the pipeline configuration. To see available meta names use:

   >>> client.pipelines.ConfigurationMetaNames.get()
   

   type: dict

Output

Important

returns: Metadata of the pipeline createdn return type: dict

Example

>>> metadata = {
>>>  client.pipelines.ConfigurationMetaNames.NAME: 'my_training_definition',
>>>  client.pipelines.ConfigurationMetaNames.DOCUMENT: {"doc_type":"pipeline","version": "2.0","primary_pipeline": "dlaas_only","pipelines": [{"id": "dlaas_only","runtime_ref": "hybrid","nodes": [{"id": "training","type": "model_node","op": "dl_train","runtime_ref": "DL","inputs": [],"outputs": [],"parameters": {"name": "tf-mnist","description": "Simple MNIST model implemented in TF","command": "python3 convolutional_network.py --trainImagesFile ${DATA_DIR}/train-images-idx3-ubyte.gz --trainLabelsFile ${DATA_DIR}/train-labels-idx1-ubyte.gz --testImagesFile ${DATA_DIR}/t10k-images-idx3-ubyte.gz --testLabelsFile ${DATA_DIR}/t10k-labels-idx1-ubyte.gz --learningRate 0.001 --trainingIters 6000","compute": {"name": "k80","nodes": 1},"training_lib_href":"/v4/libraries/64758251-bt01-4aa5-a7ay-72639e2ff4d2/content"},"target_bucket": "wml-dev-results"}]}]}}
>>> pipeline_details = client.repository.store_pipeline(pipeline_filepath, meta_props=metadata)
>>> pipeline_href = client.repository.get_pipeline_href(pipeline_details)

store_space(meta_props)

Create a space.

Parameters

Important

1. meta_props: meta data of the space configuration. To see available meta names use:

   >>> client.spaces.ConfigurationMetaNames.get()
   

   type: dict

Output

Important

returns: Metadata of the space created

return type: dict

Example

>>> metadata = {
>>>  client.spaces.ConfigurationMetaNames.NAME: 'my_space'
>>> }
>>> space_details = client.repository.store_space(meta_props=metadata)
>>> space_href = client.repository.get_space_href(experiment_details)

update_experiment(experiment_uid, changes)

Updates existing experiment metadata.

Parameters

Important

1. experiment_uid: Unique of Id experiment which definition should be updated

   type: str

2. changes: elements which should be changed, where keys are ConfigurationMetaNames

   type: dict

Output

Important

returns: metadata of updated experiment

return type: dict

Example

>>> metadata = {
>>> client.repository.ExperimentMetaNames.NAME:"updated_exp"
>>> }
>>> exp_details = client.repository.update_experiment(experiment_uid, changes=metadata)

update_function(function_uid, changes, update_function=None)

Updates existing function metadata.

Parameters

Important

1. function_uid: Unique Id of function which define what should be updated

   type: str

2. changes: Elements which should be changed, where keys are ConfigurationMetaNames.

   type: dict

3. update_function: Path to file with archived function content or function which should be changed for specific function_uid. This parameters is valid only for CP4D 3.0.0.

   type: str or function

Output

Important

returns: metadata of updated function

return type: dict

Example

>>> metadata = {
>>> client.repository.FunctionMetaNames.NAME:"updated_function"
>>> }
>>>
>>> function_details = client.repository.update_function(function_uid, changes=metadata)

update_model(model_uid, updated_meta_props=None, update_model=None)

Updates existing model metadata.

Parameters

Important

1. model_uid: Unique id of model which definition should be updated

   type: str

2. updated_meta_props: elements which should be changed, where keys are ConfigurationMetaNames

   type: dict

3. update_model: archived model content file or path to directory containing archived model file which should be changed for specific model_uid. This parameters is valid only for CP4D 3.0.0.

   type: object or archived model content file

Output

Important

returns: metadata of updated model

return type: dict

Example 1

>>> metadata = {
>>> client.repository.ModelMetaNames.NAME:"updated_model"
>>> }
>>> model_details = client.repository.update_model(model_uid, updated_meta_props=metadata)

Example 2

>>> metadata = {
>>> client.repository.ModelMetaNames.NAME:"updated_model"
>>> }
>>> model_details = client.repository.update_model(model_uid, updated_meta_props=metadata, update_model="newmodel_content.tar.gz")

update_pipeline(pipeline_uid, changes)

Updates existing pipeline metadata.

Parameters

Important

1. pipeline_uid: Unique Id of pipeline which definition should be updated

   type: str

2. changes: elements which should be changed, where keys are ConfigurationMetaNames

   type: dict

Output

Important

returns: metadata of updated pipeline

return type: dict

Example

>>> metadata = {
>>> client.repository.PipelineMetanames.NAME:"updated_pipeline"
>>> }
>>> pipeline_details = client.repository.update_pipeline(pipeline_uid, changes=metadata)

update_space(space_uid, changes)

Updates existing space metadata.

Parameters

Important

1. space_uid: Unique Id of space which definition should be updated

   type: str

2. changes: elements which should be changed, where keys are ConfigurationMetaNames

   type: dict

Output

Important

returns: metadata of updated space

return type: dict

Example

>>> metadata = {
>>> client.repository.SpacesMetaNames.NAME:"updated_space"
>>> }
>>> space_details = client.repository.update_space(space_uid, changes=metadata)

class metanames.ModelMetaNames

Set of MetaNames for models.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
NAME	str	Y	my_model
DESCRIPTION	str	N	my_description
INPUT_DATA_SCHEMA	list	N	{'id': '1', 'type': 'struct', 'fields': [{'name': 'x', 'type': 'double', 'nullable': False, 'metadata': {}}, {'name': 'y', 'type': 'double', 'nullable': False, 'metadata': {}}]}	{'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}
TRAINING_DATA_REFERENCES	list	N	[]	[{'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}, 'schema(optional)': {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}}]
OUTPUT_DATA_SCHEMA	dict	N	{'id': '1', 'type': 'struct', 'fields': [{'name': 'x', 'type': 'double', 'nullable': False, 'metadata': {}}, {'name': 'y', 'type': 'double', 'nullable': False, 'metadata': {}}]}	{'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}
LABEL_FIELD	str	N	PRODUCT_LINE
TRANSFORMED_LABEL_FIELD	str	N	PRODUCT_LINE_IX
TAGS	list	N	['string', 'string']	['string', 'string']
SIZE	dict	N	{'in_memory': 0, 'content': 0}	{'in_memory(optional)': 'string', 'content(optional)': 'string'}
SPACE_UID	str	N	53628d69-ced9-4f43-a8cd-9954344039a8
PIPELINE_UID	str	N	53628d69-ced9-4f43-a8cd-9954344039a8
RUNTIME_UID	str	N	53628d69-ced9-4f43-a8cd-9954344039a8
TYPE	str	Y	mllib_2.1
CUSTOM	dict	N	{}
DOMAIN	str	N	Watson Machine Learning
HYPER_PARAMETERS	dict	N
METRICS	list	N
IMPORT	dict	N	{'connection': {'endpoint_url': 'https://s3-api.us-geo.objectstorage.softlayer.net', 'access_key_id': '***', 'secret_access_key': '***'}, 'location': {'bucket': 'train-data', 'path': 'training_path'}, 'type': 's3'}	{'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}}
TRAINING_LIB_UID	str	N	53628d69-ced9-4f43-a8cd-9954344039a8
MODEL_DEFINITION_UID	str	N	53628d6_cdee13-35d3-s8989343
SOFTWARE_SPEC_UID	str	N	53628d69-ced9-4f43-a8cd-9954344039a8
TF_MODEL_PARAMS	dict	N	{'save_format': 'None', 'signatures': 'struct', 'options': 'None', 'custom_objects': 'string'}

class metanames.ExperimentMetaNames

Set of MetaNames for experiments.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
NAME	str	Y	Hand-written Digit Recognition
DESCRIPTION	str	N	Hand-written Digit Recognition training
TAGS	list	N	[{'value': 'dsx-project.<project-guid>', 'description': 'DSX project guid'}]	[{'value(required)': 'string', 'description(optional)': 'string'}]
EVALUATION_METHOD	str	N	multiclass
EVALUATION_METRICS	list	N	[{'name': 'accuracy', 'maximize': False}]	[{'name(required)': 'string', 'maximize(optional)': 'boolean'}]
TRAINING_REFERENCES	list	Y	[{'pipeline': {'href': '/v4/pipelines/6d758251-bb01-4aa5-a7a3-72339e2ff4d8'}}]	[{'pipeline(optional)': {'href(required)': 'string', 'data_bindings(optional)': [{'data_reference(required)': 'string', 'node_id(required)': 'string'}], 'nodes_parameters(optional)': [{'node_id(required)': 'string', 'parameters(required)': 'dict'}]}, 'training_lib(optional)': {'href(required)': 'string', 'compute(optional)': {'name(required)': 'string', 'nodes(optional)': 'number'}, 'runtime(optional)': {'href(required)': 'string'}, 'command(optional)': 'string', 'parameters(optional)': 'dict'}}]
SPACE_UID	str	N	3c1ce536-20dc-426e-aac7-7284cf3befc6
LABEL_COLUMN	str	N	label
CUSTOM	dict	N	{'field1': 'value1'}

class metanames.FunctionMetaNames

Set of MetaNames for AI functions.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
NAME	str	Y	ai_function
DESCRIPTION	str	N	This is ai function
RUNTIME_UID	str	N	53628d69-ced9-4f43-a8cd-9954344039a8
SOFTWARE_SPEC_UID	str	N	53628d69-ced9-4f43-a8cd-9954344039a8
INPUT_DATA_SCHEMAS	list	N	[{'id': '1', 'type': 'struct', 'fields': [{'name': 'x', 'type': 'double', 'nullable': False, 'metadata': {}}, {'name': 'y', 'type': 'double', 'nullable': False, 'metadata': {}}]}]	[{'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}]
OUTPUT_DATA_SCHEMAS	list	N	[{'id': '1', 'type': 'struct', 'fields': [{'name': 'multiplication', 'type': 'double', 'nullable': False, 'metadata': {}}]}]	[{'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}]
TAGS	list	N	[{'value': 'ProjectA', 'description': 'Functions created for ProjectA'}]	[{'value(required)': 'string', 'description(optional)': 'string'}]
TYPE	str	N	python
CUSTOM	dict	N	{}
SAMPLE_SCORING_INPUT	list	N	{'input_data': [{'fields': ['name', 'age', 'occupation'], 'values': [['john', 23, 'student'], ['paul', 33, 'engineer']]}]}	{'id(optional)': 'string', 'fields(optional)': 'array', 'values(optional)': 'array'}
SPACE_UID	str	N	3628d69-ced9-4f43-a8cd-9954344039a8

class metanames.PipelineMetanames

Set of MetaNames for pipelines.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
NAME	str	Y	Hand-written Digit Recognitionu
DESCRIPTION	str	N	Hand-written Digit Recognition training
SPACE_UID	str	N	3c1ce536-20dc-426e-aac7-7284cf3befc6
TAGS	list	N	[{'value': 'dsx-project.<project-guid>', 'description': 'DSX project guid'}]	[{'value(required)': 'string', 'description(optional)': 'string'}]
DOCUMENT	dict	N	{'doc_type': 'pipeline', 'version': '2.0', 'primary_pipeline': 'dlaas_only', 'pipelines': [{'id': 'dlaas_only', 'runtime_ref': 'hybrid', 'nodes': [{'id': 'training', 'type': 'model_node', 'op': 'dl_train', 'runtime_ref': 'DL', 'inputs': [], 'outputs': [], 'parameters': {'name': 'tf-mnist', 'description': 'Simple MNIST model implemented in TF', 'command': 'python3 convolutional_network.py --trainImagesFile ${DATA_DIR}/train-images-idx3-ubyte.gz --trainLabelsFile ${DATA_DIR}/train-labels-idx1-ubyte.gz --testImagesFile ${DATA_DIR}/t10k-images-idx3-ubyte.gz --testLabelsFile ${DATA_DIR}/t10k-labels-idx1-ubyte.gz --learningRate 0.001 --trainingIters 6000', 'compute': {'name': 'k80', 'nodes': 1}, 'training_lib_href': '/v4/libraries/64758251-bt01-4aa5-a7ay-72639e2ff4d2/content'}, 'target_bucket': 'wml-dev-results'}]}]}	{'doc_type(required)': 'string', 'version(required)': 'string', 'primary_pipeline(required)': 'string', 'pipelines(required)': [{'id(required)': 'string', 'runtime_ref(required)': 'string', 'nodes(required)': [{'id': 'string', 'type': 'string', 'inputs': 'list', 'outputs': 'list', 'parameters': {'training_lib_href': 'string'}}]}]}
CUSTOM	dict	N	{'field1': 'value1'}
IMPORT	dict	N	{'connection': {'endpoint_url': 'https://s3-api.us-geo.objectstorage.softlayer.net', 'access_key_id': '***', 'secret_access_key': '***'}, 'location': {'bucket': 'train-data', 'path': 'training_path'}, 'type': 's3'}	{'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}}
RUNTIMES	list	N	[{'id': 'id', 'name': 'tensorflow', 'version': '1.13-py3'}]
COMMAND	str	N	convolutional_network.py --trainImagesFile train-images-idx3-ubyte.gz --trainLabelsFile train-labels-idx1-ubyte.gz --testImagesFile t10k-images-idx3-ubyte.gz --testLabelsFile t10k-labels-idx1-ubyte.gz --learningRate 0.001 --trainingIters 6000
LIBRARY_UID	str	N	fb9752c9-301a-415d-814f-cf658d7b856a
COMPUTE	dict	N	{'name': 'k80', 'nodes': 1}

Script

class client.Script(client)

Store and manage your scripts assets.

ConfigurationMetaNames = <ibm_watson_machine_learning.metanames.ScriptMetaNames object>

MetaNames for script Assets creation.

create_revision(script_uid)

Creates revision for the given script. Revisions are immutable once created. The metadata and attachment at script_uid is taken and a revision is created out of it

Parameters

Important

1. script_uid: Script ID. Mandatory.

   type: str

** Output**

Important

returns: Stored script revisions metadata.

return type: dict

Example

>>> script_revision = client.scripts.create_revision(script_uid)

delete(asset_uid)

Delete a stored script asset.

Parameters

Important

1. asset_uid: Unique Id of script asset

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.script.delete(asset_uid)

download(asset_uid, filename, rev_uid=None)

Download the content of a script asset.

Parameters

Important

1. asset_uid: The Unique Id of the script asset to be downloaded

   type: str

2. filename: filename to be used for the downloaded file

   type: str

3. rev_uid: Revision id

   type: str

Output

Important

returns: Path to the downloaded asset content

return type: str

Example

>>> client.script.download(asset_uid,"script_file.zip")

get_details(script_uid)

Get script asset details.

Parameters

Important

1. script_uid: Unique id of script

   type: str

Output

Important

returns: Metadata of the stored script asset

return type: dict

Example

>>> script_details = client.scripts.get_details(script_uid)

static get_href(asset_details)

Get url of stored scripts asset.

Parameters

Important

1. asset_details: stored script details

   type: dict

Output

Important

returns: href of stored script asset

return type: str

Example

>>> asset_details = client.script.get_details(asset_uid)
>>> asset_href = client.script.get_href(asset_details)

static get_id(asset_details)

Get Unique Id of stored script asset.

Parameters

Important

1. asset_details: Metadata of the stored script asset

   type: dict

Output

Important

returns: Unique Id of stored shiny asset

return type: str

Example

>>> asset_uid = client.script.get_id(asset_details)

get_revision_details(script_uid=None, rev_uid=None)

Get metadata of script_uid revision.

Paramaters

Important

1. script_uid: Script ID. Mandatory.

   type: str

2. rev_uid: Revision ID. If this parameter is not provided, returns latest revision if existing else error.

   type: int

Output

Important

returns: Stored script(s) metadata

return type: dict

Example

>>> script_details = client.scripts.get_revision_details(script_uid, rev_uid)

static get_uid(asset_details)

Get Unique Id of stored script asset. This method is deprecated. Use ‘get_id(asset_details)’ instead

Parameters

Important

1. asset_details: Metadata of the stored script asset

   type: dict

Output

Important

returns: Unique Id of stored script asset

return type: str

Example

>>> asset_uid = client.script.get_uid(asset_details)

list(limit=None)

List stored scripts. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all script in a table format.

return type: None

Example

>>> client.script.list()

list_revisions(script_uid, limit=None)

List all revisions for the given script uid.

Parameters

Important

1. script_uid: Stored script ID.

   type: str

2. limit: Limit number of fetched records (optional).

   type: int

Output

Important

This method only prints the list of all script in a table format.

return type: None

Example

>>> client.scripts.list_revisions(script_uid)

store(meta_props, file_path)

Creates a Scripts asset and uploads content to it.

Parameters

Important

1. meta_props: Name to be given to the Scripts asset

   type: str

2. file_path: Path to the content file to be uploaded

   type: str

Output

Important

returns: metadata of the stored Scripts asset

return type: dict

Example

>>> metadata = {
>>>        client.script.ConfigurationMetaNamess.NAME: 'my first script',
>>>        client.script.ConfigurationMetaNames.DESCRIPTION: 'description of the script',
>>>        client.script.ConfigurationMetaNames.SOFTWARE_SPEC_UID: '0cdb0f1e-5376-4f4d-92dd-da3b69aa9bda'
>>>    }
>>>
>>> asset_details = client.scripts.store(meta_props=metadata,file_path="/path/to/file")

update(script_uid, meta_props=None, file_path=None)

Update script with either metadata or attachment or both.

Parameters

Important

1. param script_uid: Script UID.

   type: str

Output

Important

returns: Updated metadata of script.

return type: dict

Example

>>> script_details = client.script.update(model_uid, meta, content_path)

class metanames.ScriptMetaNames

Set of MetaNames for Script Specifications.

Available MetaNames:

MetaName	Type	Required	Schema
NAME	str	Y	Python script
DESCRIPTION	str	N	my_description
SOFTWARE_SPEC_UID	str	Y	53628d69-ced9-4f43-a8cd-9954344039a8

Service instance

class client.ServiceInstance(client)

Connect, get details and check usage of your Watson Machine Learning service instance.

get_api_key()

Get api_key of Watson Machine Learning service.

Output

Important

returns api_key

return type: str

Example

>>> instance_details = client.service_instance.get_api_key()

get_details()

Get information about your Watson Machine Learning instance.

Output

Important

returns: metadata of service instance

return type: dict

Example

>>> instance_details = client.service_instance.get_details()

get_instance_id()

Get instance id of your Watson Machine Learning service.

Output

Important

returns: instance id

return type: str

Example

>>> instance_details = client.service_instance.get_instance_id()

get_password()

Get password for your Watson Machine Learning service.

Output

Important

returns: password

return type: str

Example

>>> instance_details = client.service_instance.get_password()

get_url()

Get instance url of your Watson Machine Learning service.

Output

Important

returns: instance url

return type: str

Example

>>> instance_details = client.service_instance.get_url()

get_username()

Get username for your Watson Machine Learning service.

Output

Important

returns: username

return type: str

Example

>>> instance_details = client.service_instance.get_username()

Set

class client.Set(client)

Set a space_id/project_id to be used in the subsequent actions.

default_project(project_id)

Set a project ID.

Parameters

Important

1. project_id: GUID of the project

   type: str

Output

Important

returns: “SUCCESS”

return type: str

Example

>>>  client.set.default_project(project_id)

default_space(space_uid)

Set a space ID.

Parameters

Important

1. space_uid: GUID of the space to be used:

   type: str

Output

Important

returns: The space that is set here is used for subsequent requests.

return type: str(“SUCCESS”/”FAILURE”)

Example

>>>  client.set.default_space(space_uid)

Spaces

class client.PlatformSpaces(client)

Store and manage your spaces

MemberMetaNames = <ibm_watson_machine_learning.metanames.SpacesPlatformMemberMetaNames object>

MetaNames for spaces creation.

create_member(space_id, meta_props)

Create a member within a space.

Parameters

Important

1. meta_props: meta data of the member configuration. To see available meta names use:

   >>> client.spaces.MemberMetaNames.get()
   

   type: dict

Output

Important

returns: metadata of the stored member

return type: dict

Note

• ‘role’ can be any one of the following “viewer, editor, admin”

• ‘type’ can be any one of the following “user,service”

• ‘id’ can be either service-ID or IAM-userID

Example

>>> metadata = {
>>>  client.spaces.MemberMetaNames.MEMBERS: [{"id":"IBMid-100000DK0B", "type": "user", "role": "admin" }]
>>> }
>>> members_details = client.spaces.create_member(space_id=space_id, meta_props=metadata)

>>> metadata = {
>>>  client.spaces.MemberMetaNames.MEMBERS: [{"id":"iam-ServiceId-5a216e59-6592-43b9-8669-625d341aca71", "type": "service", "role": "admin" }]
>>> }
>>> members_details = client.spaces.create_member(space_id=space_id, meta_props=metadata)

delete(space_id)

Delete a stored space.

Parameters

Important

1. space_uid: space ID

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.spaces.delete(deployment_id)

delete_member(space_id, member_id)

Delete a member associated with a space.

Parameters

Important

1. space_id: space UID

   type: str

2. member_id: member UID

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.spaces.delete_member(space_id,member_id)

get_details(space_id=None, limit=None)

Get metadata of stored space(s)

Parameters

Important

1. space_id: Space ID

   type: str

2. limit: Applicable when space_id is not provided. If space_id is provided, this will be ignored

   type: str

Output

Important

returns: metadata of stored space(s)

return type: dict

Example

>>> space_details = client.spaces.get_details(space_uid)

static get_id(space_details)

Get space_id from space details.

Parameters

Important

1. space_details: Metadata of the stored space

   type: dict

Output

Important

returns: space ID

return type: str

Example

>>> space_details = client.spaces.store(meta_props)
>>> space_id = client.spaces.get_id(space_details)

get_member_details(space_id, member_id)

Get metadata of member associated with a space

Parameters

Important

1. space_id: member ID

   type: str

Output

Important

returns: metadata of member of a space

return type: dict

Example

>>> member_details = client.spaces.get_member_details(space_uid,member_id)

static get_uid(space_details)

Get Unique Id of the space. This method is deprecated. Use ‘get_id(space_details)’ instead

Parameters

Important

1. asset_details: Metadata of the space

   type: dict

   type: dict

Output

Important

returns: Unique Id of space

return type: str

Example

>>> space_details = client.spaces.store(meta_props)
>>> space_uid = client.spaces.get_uid(space_details)

list(limit=None, member=None, roles=None)

List stored spaces. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

2. member: Filters the result list to only include spaces where the user with a matching user id

   is a member

   type: string

3. roles: limit number of fetched records

   type: string

Output

Important

This method only prints the list of all spaces in a table format.

return type: None

Example

>>> client.spaces.list()

list_members(space_id, limit=None, identity_type=None, role=None, state=None)

List stored members of a space. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

2. identity_type: Find the member by type

   type: string

3. role: Find the member by role

   type: string

4. state: Find the member by state

   type: string

Output

Important

This method only prints the list of all members associated with a space in a table format.

return type: None

Example

>>> client.spaces.list_members(space_id)

store(meta_props, background_mode=True)

Create a space. The instance associated with the space via COMPUTE will be used for billing purposes on cloud. Note that STORAGE and COMPUTE are applicable only for cloud

Parameters

Important

1. meta_props: meta data of the space configuration. To see available meta names use:

   >>> client.spaces.ConfigurationMetaNames.get()
   

type: dict

1. background_mode: Indicator if store() method will run in background (async) or (sync). Default: True

type: bool

Output

Important

returns: metadata of the stored space

return type: dict

Example

>>> metadata = {
>>>  client.spaces.ConfigurationMetaNames.NAME: 'my_space',
>>>  client.spaces.ConfigurationMetaNames.DESCRIPTION: 'spaces',
>>>  client.spaces.ConfigurationMetaNames.STORAGE: {"resource_crn": "provide crn of the COS storage"},
>>>  client.spaces.ConfigurationMetaNames.COMPUTE: {"name": "test_instance",
>>>                                                 "crn": "provide crn of the instance"}
>>> }
>>> spaces_details = client.spaces.store(meta_props=metadata)

update(space_id, changes)

Updates existing space metadata. ‘STORAGE’ cannot be updated STORAGE and COMPUTE are applicable only for cloud

Parameters

Important

1. space_uid: ID of space which definition should be updated

   type: str

2. changes: elements which should be changed, where keys are ConfigurationMetaNames

   type: dict

Output

Important

returns: metadata of updated space

return type: dict

Example

>>> metadata = {
>>> client.spaces.ConfigurationMetaNames.NAME:"updated_space",
>>> client.spaces.ConfigurationMetaNames.COMPUTE: {"name": "test_instance",
>>>                                                "crn": "v1:staging:public:pm-20-dev:us-south:a/09796a1b4cddfcc9f7fe17824a68a0f8:f1026e4b-77cf-4703-843d-c9984eac7272::"
>>>                                               }
>>> }
>>> space_details = client.spaces.update(space_id, changes=metadata)

update_member(space_id, member_id, changes)

Updates existing member metadata.

Parameters

Important

1. space_id: ID of space

   type: str

2. member_id: ID of member that needs to be updated

   type: str

3. changes: elements which should be changed, where keys are ConfigurationMetaNames

   type: dict

Output

Important

returns: metadata of updated member

return type: dict

Example

>>> metadata = {
>>>  client.spaces.MemberMetaNames.MEMBER: {"role": "editor"}
>>> }
>>> member_details = client.spaces.update_member(space_id, member_id, changes=metadata)

class metanames.SpacesPlatformMetaNames

Set of MetaNames for Platform Spaces Specs.

Available MetaNames:

MetaName	Type	Required	Schema
NAME	str	Y	my_space
DESCRIPTION	str	N	my_description
STORAGE	dict	N	{'type': 'bmcos_object_storage', 'resource_crn': '', 'delegated(optional)': 'false'}
COMPUTE	dict	N	{'name': 'name', 'crn': 'crn of the instance'}

class metanames.SpacesPlatformMemberMetaNames

Set of MetaNames for Platform Spaces Member Specs.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
MEMBERS	list	N	[{'id': 'iam-id1', 'role': 'editor', 'type': 'user', 'state': 'active'}, {'id': 'iam-id2', 'role': 'viewer', 'type': 'user', 'state': 'active'}]	[{'id(required)': 'string', 'role(required)': 'string', 'type(required)': 'string', 'state(optional)': 'string'}]
MEMBER	dict	N	{'id': 'iam-id1', 'role': 'editor', 'type': 'user', 'state': 'active'}

Software specifications

class client.SwSpec(client)

Store and manage your software specs.

ConfigurationMetaNames = <ibm_watson_machine_learning.metanames.SwSpecMetaNames object>

MetaNames for Software Specification creation.

add_package_extension(sw_spec_uid, pkg_extn_id)

Add a package extension to software specifications existing metadata.

Parameters

Important

1. sw_spec_uid: Unique Id of software specification which should be updated

   type: str

2. pkg_extn_id: Unique Id of package extension which should needs to added to software specification

   type: str

Example

>>> client.software_specifications.add_package_extension(sw_spec_uid, pkg_extn_id)

delete(sw_spec_uid)

Delete a software specification.

Parameters

Important

1. sw_spec_uid: Unique Id of software specification

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.software_specifications.delete(sw_spec_uid)

delete_package_extension(sw_spec_uid, pkg_extn_id)

Delete a package extension from software specifications existing metadata.

Parameters

Important

1. sw_spec_uid: Unique Id of software specification which should be updated

   type: str

2. pkg_extn_id: Unique Id of package extension which should needs to deleted from software specification

   type: str

Example

>>> client.software_specifications.delete_package_extension(sw_spec_uid, pkg_extn_id)

get_details(sw_spec_uid=None)

Get software specification details.

Parameters

Important

1. sw_spec_details: Metadata of the stored sw_spec

   type: dict

Output

Important

returns: sw_spec UID

return type: str

Example

>>> sw_spec_details = client.software_specifications.get_details(sw_spec_uid)

static get_href(sw_spec_details)

Get url of software specification.

Parameters

Important

1. sw_spec_details: software specification details

   type: dict

Output

Important

returns: href of software specification

return type: str

Example

>>> sw_spec_details = client.software_specifications.get_details(sw_spec_uid)
>>> sw_spec_href = client.software_specifications.get_href(sw_spec_details)

static get_id(sw_spec_details)

Get Unique Id of software specification.

Parameters

Important

1. sw_spec_details: Metadata of the software specification

   type: dict

Output

Important

returns: Unique Id of software specification

return type: str

Example

>>> asset_uid = client.software_specifications.get_id(sw_spec_details)

get_id_by_name(sw_spec_name)

Get Unique Id of software specification.

Parameters

Important

1. sw_spec_name: Name of the software specification

   type: str

Output

Important

returns: Unique Id of software specification

return type: str

Example

>>> asset_uid = client.software_specifications.get_id_by_name(sw_spec_name)

static get_uid(sw_spec_details)

Get Unique Id of software specification. Deprecated!! Use get_id(sw_spec_details) instead

Parameters

Important

1. sw_spec_details: Metadata of the software specification

   type: dict

Output

Important

returns: Unique Id of software specification

return type: str

Example

>>> asset_uid = client.software_specifications.get_uid(sw_spec_details)

get_uid_by_name(sw_spec_name)

Get Unique Id of software specification. Deprecated!! Use get_id_by_name(self, sw_spec_name) instead

Parameters

Important

1. sw_spec_name: Name of the software specification

   type: str

Output

Important

returns: Unique Id of software specification

return type: str

Example

>>> asset_uid = client.software_specifications.get_uid_by_name(sw_spec_name)

list()

List software specifications.

Output

Important

This method only prints the list of all software specifications in a table format.

return type: None

Example

>>> client.software_specifications.list()

store(meta_props)

Create a software specification.

Parameters

Important

1. meta_props: meta data of the space configuration. To see available meta names use:

   >>> client.software_specifications.ConfigurationMetaNames.get()
   

   type: dict

Output

Important

returns: metadata of the stored space

return type: dict

Example

>>> meta_props = {
>>>    client.software_specifications.ConfigurationMetaNames.NAME: "skl_pipeline_heart_problem_prediction",
>>>    client.software_specifications.ConfigurationMetaNames.DESCRIPTION: "description scikit-learn_0.20",
>>>    client.software_specifications.ConfigurationMetaNames.PACKAGE_EXTENSIONS_UID: [],
>>>    client.software_specifications.ConfigurationMetaNames.SOFTWARE_CONFIGURATIONS: {},
>>>    client.software_specifications.ConfigurationMetaNames.BASE_SOFTWARE_SPECIFICATION_ID: "guid"
>>> }

class metanames.SwSpecMetaNames

Set of MetaNames for Software Specifications Specs.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
NAME	str	Y	Python 3.6 with pre-installed ML package
DESCRIPTION	str	N	my_description
PACKAGE_EXTENSIONS	list	N	[{'guid': 'value'}]
SOFTWARE_CONFIGURATION	dict	N	{'platform': {'name': 'python', 'version': '3.6'}}	{'platform(required)': 'string'}
BASE_SOFTWARE_SPECIFICATION	dict	Y	{'guid': 'BASE_SOFTWARE_SPECIFICATION_ID'}

Training

class client.Training(client)

Train new models.

cancel(training_uid, hard_delete=False)

Cancel a training which is currently running and remove it. This method is also be used to delete metadata details of the completed or canceled training run when hard_delete parameter is set to True.

Parameters

Important

1. training_uid: Training UID

   type: str

2. hard_delete: specify True or False. True - To delete the completed or canceled training runs. False - To cancel the currently running training run. Default value is False. type: Boolean

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.training.cancel(training_uid)

get_details(training_uid=None, limit=None)

Get metadata of training(s). If training_uid is not specified returns all model spaces metadata.

Parameters

Important

1. training_uid: Unique Id of Training (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: metadata of training(s)

return type: dict

The output can be {“resources”: [dict]} or a dict

Note

If training_uid is not specified, all trainings metadata is fetched

Example

>>> training_run_details = client.training.get_details(training_uid)
>>> training_runs_details = client.training.get_details()

static get_href(training_details)

Get training_href from training details.

Parameters

Important

1. training_details: Metadata of the training created

   type: dict

Output

Important

returns: training href

return type: str

Example

>>> training_details = client.training.get_details(training_uid)
>>> run_url = client.training.get_href(training_details)

static get_id(training_details)

Get training_id from training details.

Parameters

Important

1. training_details: Metadata of the training created

   type: dict

Output

Important

returns: Unique id of training

return type: str

Example

>>> training_details = client.training.get_details(training_id)
>>> training_id = client.training.get_id(training_details)

get_metrics(training_uid)

Get metrics.

Parameters

Important

1. training_uid: training UID

   type: str

Output

Important

returns: Metrics of a training run

return type: list of dict

Example

>>> training_status = client.training.get_metrics(training_uid)

get_status(training_uid)

Get the status of a training created.

Parameters

Important

1. training_uid: training UID

   type: str

Output

Important

returns: training_status

return type: dict

Example

>>> training_status = client.training.get_status(training_uid)

static get_uid(training_details)

Get training_uid from training details.

Parameters

Important

1. training_details: Metadata of the training created

   type: dict

Output

Important

returns: Unique id of training

return type: str

Example

>>> training_details = client.training.get_details(training_uid)
>>> training_uid = client.training.get_uid(training_details)

list(limit=None)

List stored trainings. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all trainings in a table format.

return type: None

Example

>>> client.training.list()

list_intermediate_models(training_uid)

List the intermediate_models.

Parameters

Important

1. training_uid: Training GUID

   type: str

Output

Important

This method only prints the list of all intermediate_models associated with an AUTOAI training in a table format.

return type: None

Note

This method prints the training logs. This method is not supported for IBM Cloud Pak® for Data.

Example

>>> client.training.list_intermediate_models()

list_subtrainings(training_uid)

List the sub-trainings.

Parameters

Important

1. training_uid: Training GUID

   type: str

Output

Important

This method only prints the list of all sub-trainings associated with a training in a table format.

return type: None

Example

>>> client.training.list_subtrainings()

monitor_logs(training_uid)

Monitor the logs of a training created.

Parameters

Important

1. training_uid: Training UID

   type: str

Output

Important

returns: None

return type: None

Note

This method prints the training logs. This method is not supported for IBM Cloud Pak® for Data.

Example

>>> client.training.monitor_logs(training_uid)

monitor_metrics(training_uid)

Monitor the metrics of a training created.

Parameters

Important

1. training_uid: Training UID

   type: str

Output

Important

returns: None

return type: None

Note

This method prints the training metrics. This method is not supported for IBM Cloud Pak® for Data.

Example

>>> client.training.monitor_metrics(training_uid)

run(meta_props, asynchronous=True)

Create a new Machine Learning training.

Parameters

Important

1. meta_props: meta data of the training configuration. To see available meta names use:

   >>> client.training.ConfigurationMetaNames.show()
   

   type: str

2. asynchronous:

   • True - training job is submitted and progress can be checked later.

   • False - method will wait till job completion and print training stats.

   type: bool

Output

Important

returns: Metadata of the training created

return type: dict

Examples

Example meta_props for Training run creation in IBM Cloud Pak® for Data for Data version 3.0.1 or above:

>>> metadata = {
>>>  client.training.ConfigurationMetaNames.NAME: 'Hand-written Digit Recognition',
>>>  client.training.ConfigurationMetaNames.DESCRIPTION: 'Hand-written Digit Recognition Training',
>>>  client.training.ConfigurationMetaNames.PIPELINE: {
>>>                "id": "4cedab6d-e8e4-4214-b81a-2ddb122db2ab",
>>>                "rev": "12",
>>>                "model_type": "string",
>>>                "data_bindings": [
>>>                  {
>>>                    "data_reference_name": "string",
>>>                    "node_id": "string"
>>>                  }
>>>                ],
>>>                "nodes_parameters": [
>>>                  {
>>>                    "node_id": "string",
>>>                    "parameters": {}
>>>                  }
>>>                ],
>>>                "hardware_spec": {
>>>                  "id": "4cedab6d-e8e4-4214-b81a-2ddb122db2ab",
>>>                  "rev": "12",
>>>                  "name": "string",
>>>                  "num_nodes": "2"
>>>                }
>>>      },
>>>  client.training.ConfigurationMetaNames.TRAINING_DATA_REFERENCES: [{
>>>          'type': 's3',
>>>          'connection': {},
>>>          'location': {
>>>              'href': 'v2/assets/asset1233456',
>>>          }
>>>          "schema": "{ "id": "t1", "name": "Tasks", "fields": [ { "name": "duration", "type": "number" } ]}"
>>>      }],
>>> client.training.ConfigurationMetaNames.TRAINING_RESULTS_REFERENCE: {
>>>          'id' : 'string',
>>>          'connection': {
>>>              'endpoint_url': 'https://s3-api.us-geo.objectstorage.service.networklayer.com',
>>>              'access_key_id': '***',
>>>              'secret_access_key': '***'
>>>          },
>>>          'location': {
>>>              'bucket': 'wml-dev-results',
>>>               'path' : "path"
>>>          }
>>>          'type': 's3'
>>>      }
>>>   }

NOTE: You can provide either one of the below values can be provided for training:

• client.training.ConfigurationMetaNames.EXPERIMENT

• client.training.ConfigurationMetaNames.PIPELINE

• client.training.ConfigurationMetaNames.MODEL_DEFINITION

Example meta_prop values for training run creation in other versions:

>>> metadata = {
>>>  client.training.ConfigurationMetaNames.NAME: 'Hand-written Digit Recognition',
>>>  client.training.ConfigurationMetaNames.TRAINING_DATA_REFERENCES: [{
>>>          'connection': {
>>>              'endpoint_url': 'https://s3-api.us-geo.objectstorage.service.networklayer.com',
>>>              'access_key_id': '***',
>>>              'secret_access_key': '***'
>>>          },
>>>          'source': {
>>>              'bucket': 'wml-dev',
>>>          }
>>>          'type': 's3'
>>>      }],
>>> client.training.ConfigurationMetaNames.TRAINING_RESULTS_REFERENCE: {
>>>          'connection': {
>>>              'endpoint_url': 'https://s3-api.us-geo.objectstorage.service.networklayer.com',
>>>              'access_key_id': '***',
>>>              'secret_access_key': '***'
>>>          },
>>>          'target': {
>>>              'bucket': 'wml-dev-results',
>>>          }
>>>          'type': 's3'
>>>      },
>>> client.training.ConfigurationMetaNames.PIPELINE_UID : "/v4/pipelines/<PIPELINE-ID>"
>>> }
>>> training_details = client.training.run(definition_uid, meta_props=metadata)
>>> training_uid = client.training.get_uid(training_details)

class metanames.TrainingConfigurationMetaNames

Set of MetaNames for trainings.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
TRAINING_DATA_REFERENCES	list	Y	[{'connection': {'endpoint_url': 'https://s3-api.us-geo.objectstorage.softlayer.net', 'access_key_id': '***', 'secret_access_key': '***'}, 'location': {'bucket': 'train-data', 'path': 'training_path'}, 'type': 's3', 'schema': {'id': '1', 'fields': [{'name': 'x', 'type': 'double', 'nullable': 'False'}]}}]	[{'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}, 'schema(optional)': {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}}]
TRAINING_RESULTS_REFERENCE	dict	Y	{'connection': {'endpoint_url': 'https://s3-api.us-geo.objectstorage.softlayer.net', 'access_key_id': '***', 'secret_access_key': '***'}, 'location': {'bucket': 'test-results', 'path': 'training_path'}, 'type': 's3'}	{'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}}
TAGS	list	N	[{'value': 'string', 'description': 'string'}]	[{'value(required)': 'string', 'description(optional)': 'string'}]
PIPELINE_UID	str	N	3c1ce536-20dc-426e-aac7-7284cf3befc6
EXPERIMENT_UID	str	N	3c1ce536-20dc-426e-aac7-7284cf3befc6
PIPELINE_DATA_BINDINGS	str	N	[{'data_reference_name': 'string', 'node_id': 'string'}]	[{'data_reference_name(required)': 'string', 'node_id(required)': 'string'}]
PIPELINE_NODE_PARAMETERS	dict	N	[{'node_id': 'string', 'parameters': {}}]	[{'node_id(required)': 'string', 'parameters(required)': 'dict'}]
SPACE_UID	str	N	3c1ce536-20dc-426e-aac7-7284cf3befc6
TRAINING_LIB	dict	N	{'href': '/v4/libraries/3c1ce536-20dc-426e-aac7-7284cf3befc6', 'compute': {'name': 'k80', 'nodes': 0}, 'runtime': {'href': '/v4/runtimes/3c1ce536-20dc-426e-aac7-7284cf3befc6'}, 'command': 'python3 convolutional_network.py', 'parameters': {}}	{'href(required)': 'string', 'type(required)': 'string', 'runtime(optional)': {'href': 'string'}, 'command(optional)': 'string', 'parameters(optional)': 'dict'}
TRAINING_LIB_UID	str	N	3c1ce536-20dc-426e-aac7-7284cf3befc6
TRAINING_LIB_MODEL_TYPE	str	N	3c1ce536-20dc-426e-aac7-7284cf3befc6
TRAINING_LIB_RUNTIME_UID	str	N	3c1ce536-20dc-426e-aac7-7284cf3befc6
TRAINING_LIB_PARAMETERS	dict	N	3c1ce536-20dc-426e-aac7-7284cf3befc6
COMMAND	str	N	3c1ce536-20dc-426e-aac7-7284cf3befc6
COMPUTE	dict	N	3c1ce536-20dc-426e-aac7-7284cf3befc6
PIPELINE_MODEL_TYPE	str	N	tensorflow_1.1.3-py3

AutoAI (BETA)

This version of ibm-watson-machine-learning client introduces support for AutoAI Experiments. Note that AutoAI functionality is currently only available as a beta.

AutoAI experiment

AutoAI

class ibm_watson_machine_learning.experiment.autoai.autoai.AutoAI(wml_credentials: Optional[Union[dict, ibm_watson_machine_learning.workspace.workspace.WorkSpace]] = None, project_id: Optional[str] = None, space_id: Optional[str] = None)

Bases: ibm_watson_machine_learning.experiment.base_experiment.base_experiment.BaseExperiment

AutoAI class for pipeline models optimization automation.

wml_credentials: dictionary, required

Credentials to Watson Machine Learning instance.

project_id: str, optional

ID of the Watson Studio project.

space_id: str, optional

ID of the Watson Studio Space.

>>> from ibm_watson_machine_learning.experiment import AutoAI
>>>
>>> experiment = AutoAI(
>>>        wml_credentials={
>>>              "apikey": "...",
>>>              "iam_apikey_description": "...",
>>>              "iam_apikey_name": "...",
>>>              "iam_role_crn": "...",
>>>              "iam_serviceid_crn": "...",
>>>              "instance_id": "...",
>>>              "url": "https://us-south.ml.cloud.ibm.com"
>>>            },
>>>         project_id="...",
>>>         space_id="...")

class ClassificationAlgorithms(value)

Bases: enum.Enum

Classification algorithms that AutoAI could use.

DT = 'DecisionTreeClassifierEstimator'

EX_TREES = 'ExtraTreesClassifierEstimator'

GB = 'GradientBoostingClassifierEstimator'

LGBM = 'LGBMClassifierEstimator'

LR = 'LogisticRegressionEstimator'

RF = 'RandomForestClassifierEstimator'

XGB = 'XGBClassifierEstimator'

class DataConnectionTypes

Bases: object

Supported types of DataConnection. OneOf: [s3, FS]

CA = 'connection_asset'

DS = 'data_asset'

FS = 'fs'

S3 = 's3'

class Metrics

Bases: object

Supported types of classification and regression metrics in autoai.

ACCURACY_SCORE = 'accuracy'

AVERAGE_PRECISION_SCORE = 'average_precision'

EXPLAINED_VARIANCE_SCORE = 'explained_variance'

F1_SCORE = 'f1'

F1_SCORE_MACRO = 'f1_macro'

F1_SCORE_MICRO = 'f1_micro'

F1_SCORE_WEIGHTED = 'f1_weighted'

LOG_LOSS = 'neg_log_loss'

MEAN_ABSOLUTE_ERROR = 'neg_mean_absolute_error'

MEAN_SQUARED_ERROR = 'neg_mean_squared_error'

MEAN_SQUARED_LOG_ERROR = 'neg_mean_squared_log_error'

MEDIAN_ABSOLUTE_ERROR = 'neg_median_absolute_error'

PRECISION_SCORE = 'precision'

PRECISION_SCORE_MACRO = 'precision_macro'

PRECISION_SCORE_MICRO = 'precision_micro'

PRECISION_SCORE_WEIGHTED = 'precision_weighted'

R2_SCORE = 'r2'

RECALL_SCORE = 'recall'

RECALL_SCORE_MACRO = 'recall_macro'

RECALL_SCORE_MICRO = 'recall_micro'

RECALL_SCORE_WEIGHTED = 'recall_weighted'

ROC_AUC_SCORE = 'roc_auc'

ROOT_MEAN_SQUARED_ERROR = 'neg_root_mean_squared_error'

ROOT_MEAN_SQUARED_LOG_ERROR = 'neg_root_mean_squared_log_error'

class PipelineTypes

Bases: object

Supported types of Pipelines.

LALE = 'lale'

SKLEARN = 'sklearn'

class PredictionType

Bases: object

Supported types of learning. OneOf: [BINARY, MULTICLASS, REGRESSION]

BINARY = 'binary'

MULTICLASS = 'multiclass'

REGRESSION = 'regression'

class RegressionAlgorithms(value)

Bases: enum.Enum

Regression algorithms that AutoAI could use.

DT = 'DecisionTreeRegressorEstimator'

EX_TREES = 'ExtraTreesRegressorEstimator'

GB = 'GradientBoostingRegressorEstimator'

LGBM = 'LGBMRegressorEstimator'

LR = 'LinearRegressionEstimator'

RF = 'RandomForestRegressorEstimator'

RIDGE = 'RidgeEstimator'

XGB = 'XGBRegressorEstimator'

class TShirtSize

Bases: object

Possible sizes of the AutoAI POD Depends on the POD size, AutoAI could support different data sets sizes.

S - small (2vCPUs and 8GB of RAM) M - Medium (4vCPUs and 16GB of RAM) L - Large (8vCPUs and 32GB of RAM)) XL - Extra Large (16vCPUs and 64GB of RAM)

L = 'l'

M = 'm'

ML = 'ml'

S = 's'

XL = 'xl'

class Transformers

Bases: object

Supported types of congito transformers names in autoai.

ABS = 'abs'

CBRT = 'cbrt'

COS = 'cos'

CUBE = 'cube'

DIFF = 'diff'

DIVIDE = 'divide'

FEATUREAGGLOMERATION = 'featureagglomeration'

ISOFORESTANOMALY = 'isoforestanomaly'

LOG = 'log'

MAX = 'max'

MINMAXSCALER = 'minmaxscaler'

NXOR = 'nxor'

PCA = 'pca'

PRODUCT = 'product'

ROUND = 'round'

SIGMOID = 'sigmoid'

SIN = 'sin'

SQRT = 'sqrt'

SQUARE = 'square'

STDSCALER = 'stdscaler'

SUM = 'sum'

TAN = 'tan'

optimizer(name: str, *, prediction_type: ibm_watson_machine_learning.utils.autoai.enums.PredictionType, prediction_column: str, scoring: ibm_watson_machine_learning.utils.autoai.enums.Metrics, desc: Optional[str] = None, test_size: float = 0.1, max_number_of_estimators: int = 2, train_sample_rows_test_size: Optional[float] = None, daub_include_only_estimators: Optional[List[Union[ibm_watson_machine_learning.utils.autoai.enums.ClassificationAlgorithms, ibm_watson_machine_learning.utils.autoai.enums.RegressionAlgorithms]]] = None, cognito_transform_names: Optional[List[ibm_watson_machine_learning.utils.autoai.enums.Transformers]] = None, data_join_graph: Optional[ibm_watson_machine_learning.preprocessing.multiple_files_preprocessor.DataJoinGraph] = None, csv_separator: Union[List[str], str] = ',', excel_sheet: Union[str, int] = 0, encoding: str = 'utf-8', positive_label: Optional[str] = None, data_join_only: bool = False, drop_duplicates: bool = True, **kwargs) → Union[ibm_watson_machine_learning.experiment.autoai.optimizers.remote_auto_pipelines.RemoteAutoPipelines, ibm_watson_machine_learning.experiment.autoai.optimizers.local_auto_pipelines.LocalAutoPipelines]

Initialize an AutoAi optimizer.

name: str, required

Name for the AutoPipelines

prediction_type: PredictionType, required

Type of the prediction.

prediction_column: str, required

name of the target/label column

scoring: Metrics, required

Type of the metric to optimize with.

desc: str, optional

Description

test_size: float, optional

Percentage of the entire dataset to leave as a holdout. Default 0.1

max_number_of_estimators: int, optional

Maximum number (top-K ranked by DAUB model selection) of the selected algorithm, or estimator types, for example LGBMClassifierEstimator, XGBoostClassifierEstimator, or LogisticRegressionEstimator to use in pipeline composition. The default is 2, where only the highest ranked by model selection algorithm type is used. (min 1, max 4)

train_sample_rows_test_size: float, optional

Training data sampling percentage

daub_include_only_estimators: List[Union[‘ClassificationAlgorithms’, ‘RegressionAlgorithms’]], optional

List of estimators to include in computation process. See: AutoAI.ClassificationAlgorithms or AutoAI.RegressionAlgorithms

cognito_transform_names: List[‘Transformers’], optional

List of transformers to include in feature enginnering computation process. See: AutoAI.Transformers

csv_separator: Union[List[str], str], optional

The separator, or list of separators to try for separating columns in a CSV file. Not used if the file_name is not a CSV file. Default is ‘,’.

excel_sheet: Union[str, int], optional

Name or number of the excel sheet to use. Only use when xlsx file is an input. Default is 0.

encoding: str, optional

Encoding type for CSV training file.

positive_label: str, optional

The positive class to report when binary classification. When multiclass or regression, this will be ignored.

t_shirt_size: TShirtSize, optional

The size of the remote AutoAI POD instance (computing resources). Only applicable to a remote scenario. See: AutoAI.TShirtSize

data_join_graph: DataJoinGraph, optional

A graph object with definition of join structure for multiple input data sources. Data preprocess step for multiple files.

data_join_only: bool, optional

If True only preprocessing will be executed.

drop_duplicates: bool, optional

If True duplicated rows in data will be removed before further processing. Default is True.

RemoteAutoPipelines or LocalAutoPipelines, depends on how you initialize the AutoAI object.

>>> from ibm_watson_machine_learning.experiment import AutoAI
>>> experiment = AutoAI(...)
>>>
>>> optimizer = experiment.optimizer(
>>>        name="name of the optimizer.",
>>>        prediction_type=AutoAI.PredictionType.BINARY,
>>>        prediction_column="y",
>>>        scoring=AutoAI.Metrics.ROC_AUC_SCORE,
>>>        desc="Some description.",
>>>        test_size=0.1,
>>>        max_num_daub_ensembles=1,
>>>        cognito_transform_names=[AutoAI.Transformers.SUM,AutoAI.Transformers.MAX],
>>>        train_sample_rows_test_size=1,
>>>        daub_include_only_estimators=[AutoAI.ClassificationAlgorithms.LGBM, AutoAI.ClassificationAlgorithms.XGB],
>>>        t_shirt_size=AutoAI.TShirtSize.L
>>>    )
>>>
>>> optimizer = experiment.optimizer(
>>>        name="name of the optimizer.",
>>>        prediction_type=AutoAI.PredictionType.MULTICLASS,
>>>        prediction_column="y",
>>>        scoring=AutoAI.Metrics.ROC_AUC_SCORE,
>>>        desc="Some description.",
>>>    )

runs(*, filter: str) → Union[ibm_watson_machine_learning.experiment.autoai.runs.auto_pipelines_runs.AutoPipelinesRuns, ibm_watson_machine_learning.experiment.autoai.runs.local_auto_pipelines_runs.LocalAutoPipelinesRuns]

Get the historical runs but with WML Pipeline name filter (for remote scenario). Get the historical runs but with experiment name filter (for local scenario).

filter: str, required

WML Pipeline name to filter the historical runs. or experiment name to filter the local historical runs.

AutoPipelinesRuns or LocalAutoPipelinesRuns

>>> from ibm_watson_machine_learning.experiment import AutoAI
>>> experiment = AutoAI(...)
>>>
>>> experiment.runs(filter='Test').list()

Working with AutoAI class and optimizer

The AutoAI experiment class is responsible for creating experiments and scheduling training. All experiment results are stored automatically in the user-specified Cloud Object Storage (COS). Then, the AutoAI feature can fetch the results and provide them directly to the user for further usage.

Configure optimizer with one data source

For an AutoAI object initialization WML credentials (with apikey and url) and one of project_id or space_id.

from ibm_watson_machine_learning.experiment import AutoAI

experiment = AutoAI(wml_credentials,
    space_id='76g53e0-0b32-4a0e-9152-3d50324855ddb')
)

pipeline_optimizer = experiment.optimizer(
            name='test name',
            desc='test description',
            prediction_type=AutoAI.PredictionType.BINARY,
            prediction_column='y',
            scoring=AutoAI.Metrics.ACCURACY_SCORE,
            test_size=0.1,
            max_num_daub_ensembles=1,
            train_sample_rows_test_size=1.,
            daub_include_only_estimators = [
                 AutoAI.ClassificationAlgorithms.XGB,
                 AutoAI.ClassificationAlgorithms.LGBM
                 ],
            cognito_transform_names = [
                 AutoAI.Transformers.SUM,
                 AutoAI.Transformers.MAX
                 ]
        )

Configure optimizer with joined data

AutoAI allows to combine multiple data from various data sources into one input to experiment.

To define join graph all data connections should be defined with additional parameter data_join_node_name.

main_conn = DataConnection(
    data_join_node_name="main",
    connection=S3Connection(...),
    location=S3Location(bucket=bucket_name,
                        path=data_cos_path + 'group_customer_main.csv'))

customers_transactions_conn = DataConnection(
    data_join_node_name=["customers", "transactions"],
    connection=S3Connection(...),
    location=S3Location(bucket=bucket_name,
                        path=data_cos_path + 'group_customer.xlsx'))

customers_conn = DataConnection(
    data_join_node_name="customers",
    connection=S3Connection(...),
    location=S3Location(bucket=bucket_name,
                        path=data_cos_path + 'group_customer_customers.csv'))

transactions_conn = DataConnection(
    data_join_node_name="transactions",
    connection=S3Connection(...),
    location=S3Location(bucket=bucket_name,
                        path=data_cos_path + 'group_customer_transactions.csv'))

purchases_conn = DataConnection(
    data_join_node_name="purchases",
    connection=S3Connection(...),
    location=S3Location(bucket=bucket_name,
                        path=data_cos_path + 'group_customer_purchase.csv'))

products_conn = DataConnection(
    data_join_node_name="products",
    connection=S3Connection(...),
    location=S3Location(bucket=bucket_name,
                        path=data_cos_path + 'group_customer_products.csv'))

results_connection = DataConnection(
    connection=S3Connection(...),
    location=S3Location(bucket=bucket_name,
                        path=results_cos_path))

data_connections = [main_conn,
                    customers_transactions_conn,
                    purchases_conn,
                    products_conn]

Next step is to create join graph object with DataJoinGraph class. At this step additional params for specific data holding files can be added. For example, csv_separator for csv, or sheet_name for xlsx.

First nodes, then edges of graph are defined using data_join_node_names added in previous step.

data_join_graph = DataJoinGraph()
data_join_graph.node(name="main", csv_separator=";")
data_join_graph.node(name="customers", sheet_name="customers")
data_join_graph.node(name="transactions", sheet_name="transactions")
data_join_graph.node(name="purchases")
data_join_graph.node(name="products")
data_join_graph.edge(from_node="main", to_node="customers",
                     from_column=["group_customer_id"], to_column=["group_customer_id"])
data_join_graph.edge(from_node="main", to_node="transactions",
                     from_column=["transaction_id"], to_column=["transaction_id"])
data_join_graph.edge(from_node="main", to_node="purchases",
                     from_column=["group_id"], to_column=["group_id"])
data_join_graph.edge(from_node="transactions", to_node="products",
                     from_column=["product_id"], to_column=["product_id"])

That way prepared join graph can be used during optimizer configuration step.

For multi-data source scenario join graph is passed as parameter.

experiment = AutoAI(wml_credentials,
    space_id='76g53e0-0b32-4a0e-9152-3d50324855ddb')
)

pipeline_optimizer = experiment.optimizer(
    name='obm scenario',
    prediction_type=AutoAI.PredictionType.REGRESSION,
    prediction_column='next_purchase',
    scoring=AutoAI.Metrics.LOG_LOSS,
    max_number_of_estimators=1,
    daub_include_only_estimators=[experiment.RegressionAlgorithms.LGBM],
    data_join_graph=data_join_graph,
    t_shirt_size=experiment.TShirtSize.L,
    test_size=0.1
)

Get configuration parameters

To see what configuration parameters are used, call the get_params() method.

config_parameters = pipeline_optimizer.get_params()
print(config_parameters)
{
    'name': 'test name',
    'desc': 'test description',
    'prediction_type': 'classification',
    'prediction_column': 'y',
    'scoring': 'roc_auc',
    'test_size': 0.1,
    'max_num_daub_ensembles': 1
}

Fit optimizer

To schedule an AutoAI experiment, call a fit() method. This will trigger a training and an optimization process on WML. fit() method can be synchronous (background_mode=False), or asynchronous (background_mode=True). When you do not want to wait until the fit end, invoke an async version, which immediately returns only fit/run details. Otherwise, in the sync version, you will see a progress bar with information about the learning/optimization process.

fit_details = pipeline_optimizer.fit(
        training_data_reference=[training_data_connection],
        training_results_reference=results_connection,
        background_mode=True)

# OR

fit_details = pipeline_optimizer.fit(
        training_data_reference=[training_data_connection],
        training_results_reference=results_connection,
        background_mode=False)

Get run status, get run details

If you decided to use an asynchronous option during fit() method, you can monitor the run/fit details and status using the following two methods:

status = pipeline_optimizer.get_run_status()
print(status)
'running'

# OR

'completed'

run_details = pipeline_optimizer.get_run_details()
print(run_details)
{'entity': {'pipeline': {'href': '/v4/pipelines/5bfeb4c5-90df-48b8-9e03-ba232d8c0838'},
        'results_reference': {'connection': {'access_key_id': '...',
                                             'endpoint_url': '...',
                                             'secret_access_key': '...'},
                              'location': {'bucket': '...',
                                           'logs': '53c8cb7b-c8b5-44aa-8b52-6fde3c588462',
                                           'model': '53c8cb7b-c8b5-44aa-8b52-6fde3c588462/model',
                                           'path': '.',
                                           'pipeline': './33825fa2-5fca-471a-ab1a-c84820b3e34e/pipeline.json',
                                           'training': './33825fa2-5fca-471a-ab1a-c84820b3e34e',
                                           'training_status': './33825fa2-5fca-471a-ab1a-c84820b3e34e/training-status.json'},
                              'type': 's3'},
        'space': {'href': '/v4/spaces/71ab11ea-bb77-4ae6-b98a-a77f30ade09d'},
        'status': {'completed_at': '2020-02-17T10:46:32.962Z',
                   'message': {'level': 'info',
                               'text': 'Training job '
                                       '33825fa2-5fca-471a-ab1a-c84820b3e34e '
                                       'completed'},
                   'state': 'completed'},
        'training_data_references': [{'connection': {'access_key_id': '...',
                                                     'endpoint_url': '...',
                                                     'secret_access_key': '...'},
                                      'location': {'bucket': '...',
                                                   'path': '...'},
                                      'type': 's3'}]},
 'metadata': {'created_at': '2020-02-17T10:44:22.532Z',
              'guid': '33825fa2-5fca-471a-ab1a-c84820b3e34e',
              'href': '/v4/trainings/33825fa2-5fca-471a-ab1a-c84820b3e34e',
              'id': '33825fa2-5fca-471a-ab1a-c84820b3e34e',
              'modified_at': '2020-02-17T10:46:32.987Z'}}

Get data connections

data_connections = pipeline_optimizer.get_data_connections()
# note: data_connections is a list with all training_connections that you referenced during fit() call

Summary

You can get a ranking of all computed pipeline models sorted based on a scorer metric supplied at the beginning. The output is a pandas.DataFrame with pipeline names, computation timestamps, machine learning metrics and the number of enhancements implemented in each of the pipeline.

results = pipeline_optimizer.summary()
print(results)
               Number of enhancements  ...  training_f1
Pipeline Name                          ...
Pipeline_4                          3  ...     0.555556
Pipeline_3                          2  ...     0.554978
Pipeline_2                          1  ...     0.503175
Pipeline_1                          0  ...     0.529928

Get pipeline details

To see pipeline composition steps and nodes, use get_pipeline_details(). Empty pipeline_name returns the best computed pipeline details.

pipeline_params = pipeline_optimizer.get_pipeline_details(pipeline_name='Pipeline_1')
print(pipeline_params)
{
    'composition_steps': [
        'TrainingDataset_full_199_16', 'Split_TrainingHoldout',
        'TrainingDataset_full_179_16', 'Preprocessor_default', 'DAUB'
        ],
    'pipeline_nodes': [
        'PreprocessingTransformer', 'LogisticRegressionEstimator'
        ]
}

Get pipeline

Use this method to load a specific pipeline. By default, get_pipeline() returns a Lale (https://github.com/ibm/lale) pipeline.

pipeline = pipeline_optimizer.get_pipeline(pipeline_name='Pipeline_4')
print(type(pipeline))
'lale.operators.TrainablePipeline'

There is an additional option to load a pipeline as a scikit pipeline model type.

lale_pipeline = pipeline_optimizer.get_pipeline(pipeline_name='Pipeline_4', astype=AutoAI.PipelineTypes.SKLEARN)
print(type(lale_pipeline))
<class 'sklearn.pipeline.Pipeline'>

Working with deployments

The following classes enable you to work with Watson Machine Learning deployments.

Web Service

Web Service is an online type of deployment. It allows you to upload and deploy your model to be able to score it via online web service. You need to pass the location where the training was performed (source_space_id or source_project_id). The model can be deployed to any space or project described by target_space_id or target_project_id.

WebService supports only AutoAI deployment.

from ibm_watson_machine_learning.deployment import WebService

# note: only AutoAI deployment is possible now
service = WebService(wml_credentials,
     source_space_id='76g53e0-0b32-4a0e-9152-3d50324855ddb',
     target_space_id='1234abc1234abc1234abc1234abc1234abcd')
 )

service.create(
       experiment_run_id="...",
       model=model,
       deployment_name='My new deployment'
   )

Batch

Batch manages batch type of deployment. It allows upload and deploy model and run batch deployment job. As in Web Service, you need to pass the location where the training was performed (source_space_id or source_project_id). The model can be deployed to any space or project described by target_space_id or target_project_id.

Batch supports only AutoAI deployment. The input data can be provided as one of: pandas.DataFrame, data-asset or Cloud Object Storage file.

Example of batch deployment creation:

from ibm_watson_machine_learning.deployment import Batch

service_batch = Batch(wml_credentials, source_space_id='76g53e0-0b32-4a0e-9152-3d50324855ddb')
service_batch.create(
        experiment_run_id="6ce62a02-3e41-4d11-89d1-484c2deaed75",
        model="Pipeline_4",
        deployment_name='Batch deployment')

Example of batch job creation with inline data as type pandas.DataFrame:

scoring_params = service_batch.run_job(
            payload=test_X_df,
            background_mode=False)

Example of batch job creation with COS object:

from ibm_watson_machine_learning.helpers.connections import S3Connection, S3Location, DataConnection

payload_reference = DataConnection(
        connection=S3Connection(endpoint_url='url of the COS endpoint',
                                access_key_id='COS access key id',
                                secret_access_key='COS secret acces key'
                                ),
        location=S3Location(bucket='bucket_name',   # note: COS bucket name where deployment payload dataset is located
                            path='my_path'  # note: path within bucket where your deployment payload dataset is located
                            )
    )

results_reference = DataConnection(
        connection=S3Connection(endpoint_url='url of the COS endpoint',
                                access_key_id='COS access key id',
                                secret_access_key='COS secret acces key'
                                ),
        location=S3Location(bucket='bucket_name',   # note: COS bucket name where deployment output should be located
                            path='my_path_where_output_will_be_saved'  # note: path within bucket where your deployment output should be located
                            )
    )
payload_reference.write("local_path_to_the_batch_payload_csv_file", remote_name="batch_payload_location.csv"])

scoring_params = service_batch.run_job(
    payload=[payload_reference],
    output_data_reference=results_reference,
    background_mode=False)   # If background_mode is False, then synchronous mode is started. Otherwise job status need to be monitored.

Example of batch job creation with data-asset object.

from ibm_watson_machine_learning.helpers.connections import DataConnection, CloudAssetLocation, DeploymentOutputAssetLocation

payload_reference = DataConnection(location=CloudAssetLocation(asset_id=asset_id))
results_reference = DataConnection(
        location=DeploymentOutputAssetLocation(name="batch_output_file_name.csv"))

    scoring_params = service_batch.run_job(
        payload=[payload_reference],
        output_data_reference=results_reference,
        background_mode=False)

Working with DataConnection

DataConnection is the base class to start working with your data storage needed for AutoAI backend to fetch training data and store all of the results. There are several ways you can use the DataConnection object. This is one basic scenario.

To start an AutoAI experiment, first you must specify where your training dataset is located. Currently, WML AutoAI supports Cloud Object Storage (COS) and data assets on Cloud.

Cloud DataConnection Initialization

To upload your experiment dataset, you must initialize DataConnection with your COS credentials.

from ibm_watson_machine_learning.autoai.helpers.connections import S3Connection, S3Location, DataConnection

# note: this DataConnection will be used as a reference where to find your training dataset
training_data_connection = DataConnection(
        connection=S3Connection(endpoint_url='url of the COS endpoint',
                                access_key_id='COS access key id',
                                secret_access_key='COS secret acces key'
                                ),
        location=S3Location(bucket='bucket_name',   # note: COS bucket name where training dataset is located
                            path='my_path'  # note: path within bucket where your training dataset is located
                            )
    )

# note: this DataConnection will be used as a reference where to save all of the AutoAI experiment results
results_connection = DataConnection(
        connection=S3Connection(endpoint_url='url of the COS endpoint',
                                access_key_id='COS access key id',
                                secret_access_key='COS secret acces key'
                                ),
        # note: bucket name and path could be different or the same as specified in the training_data_connection
        location=S3Location(bucket='bucket_name',
                            path='my_path'
                            )
    )

Upload your training dataset

An AutoAI experiment should be able to access your training data. If you do not have a training dataset stored already, you can do it by invoking the write() method of the DataConnection.

training_data_connection.write(data='local_path_to_the_dataset', remote_name='training_dataset.csv')

Download your training dataset

To download stored dataset, use the read() method of DataConnection.

dataset = training_data_connection.read()   # note: returns a pandas DataFrame

AutoAI Helpers

DataConnection

class ibm_watson_machine_learning.helpers.connections.connections.DataConnection(location: Union[ibm_watson_machine_learning.helpers.connections.connections.S3Location, ibm_watson_machine_learning.helpers.connections.connections.FSLocation, ibm_watson_machine_learning.helpers.connections.connections.AssetLocation, ibm_watson_machine_learning.helpers.connections.connections.CP4DAssetLocation, ibm_watson_machine_learning.helpers.connections.connections.WMLSAssetLocation, ibm_watson_machine_learning.helpers.connections.connections.WSDAssetLocation, ibm_watson_machine_learning.helpers.connections.connections.CloudAssetLocation, ibm_watson_machine_learning.helpers.connections.connections.NFSLocation, ibm_watson_machine_learning.helpers.connections.connections.DeploymentOutputAssetLocation], connection: Optional[Union[ibm_watson_machine_learning.helpers.connections.connections.S3Connection, ibm_watson_machine_learning.helpers.connections.connections.NFSConnection]] = None, data_join_node_name: Optional[Union[str, List[str]]] = None)

Bases: ibm_watson_machine_learning.helpers.connections.base_data_connection.BaseDataConnection

Data Storage Connection class needed for WML training metadata (input data).

connection: Union[S3Connection], required

connection parameters of specific type

location: Union[S3Location, FSLocation, AssetLocation],

required location parameters of specific type

data_join_node_name: Union[str, List[str]], optional

Names for node(s). If no value provided, data file name will be used as node name. If str will be passed, it will became node name. If multiple names will be passed, several nodes will have the same data connection (used for excel files with multiple sheets).

classmethod from_studio(path: str) → List[ibm_watson_machine_learning.helpers.connections.connections.DataConnection]

Create DataConnections from the credentials stored (connected) in Watson Studio. Only for COS.

path: str, required

Path in COS bucket to the training dataset.

List with DataConnection objects.

>>> data_connections = DataConnection.from_studio(path='iris_dataset.csv')

read(with_holdout_split: bool = False, csv_separator: str = ',', excel_sheet: Union[str, int] = 0, encoding: Optional[str] = 'utf-8') → Union[pandas.core.frame.DataFrame, Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]]

Download dataset stored in remote data storage.

with_holdout_split: bool, optional

If True, data will be split to train and holdout dataset as it was by AutoAI.

csv_separator: str, optional

Separator / delimiter for CSV file, default is ‘,’

excel_sheet: Union[str, int], optional

Excel file sheet name to use, default is 0.

encoding: str, optional

Encoding type of the CSV

pandas.DataFrame contains dataset from remote data storage or Tuple[pandas.DataFrame, pandas.DataFrame]

containing training data and holdout data from remote storage (only if only_holdout == True and auto_pipeline_params was passed)

write(data: Union[str, pandas.core.frame.DataFrame], remote_name: str) → None

Upload file to a remote data storage.

data: str, required

Local path to the dataset or pandas.DataFrame with data.

remote_name: str, required

Name that dataset should be stored with in remote data storage.

S3Connection

class ibm_watson_machine_learning.helpers.connections.connections.S3Connection(endpoint_url: str, access_key_id: Optional[str] = None, secret_access_key: Optional[str] = None, api_key: Optional[str] = None, service_name: Optional[str] = None, auth_endpoint: Optional[str] = None)

Bases: ibm_watson_machine_learning.helpers.connections.base_connection.BaseConnection

Connection class to COS data storage in S3 format.

endpoint_url: str, required

S3 data storage url (COS)

access_key_id: str, optional

access_key_id of the S3 connection (COS)

secret_access_key: str, optional

secret_access_key of the S3 connection (COS)

api_key: str, optional

API key of the S3 connection (COS)

service_name: str, optional

Service name of the S3 connection (COS)

auth_endpoint: str, optional

Authentication endpoint url of the S3 connection (COS)

S3Location

class ibm_watson_machine_learning.helpers.connections.connections.S3Location(bucket: str, path: str, **kwargs)

Bases: ibm_watson_machine_learning.helpers.connections.base_location.BaseLocation

Connection class to COS data storage in S3 format.

bucket: str, required

COS bucket name

path: str, required

COS data path in the bucket

model_location: str, optional

Path to the pipeline model in the COS.

training_status: str, optional

Path t the training status json in COS.

CloudAssetLocation

class ibm_watson_machine_learning.helpers.connections.connections.CloudAssetLocation(asset_id: str)

Bases: ibm_watson_machine_learning.helpers.connections.connections.AssetLocation

Connection class to data assets as input data references to batch deployment job on Cloud.

asset_id: str, required

Asset ID of the file loaded on space on Cloud.

DeploymentOutputAssetLocation

class ibm_watson_machine_learning.helpers.connections.connections.DeploymentOutputAssetLocation(name: str, description: str = '')

Bases: ibm_watson_machine_learning.helpers.connections.base_location.BaseLocation

Connection class to data assets where output of batch deployment will be stored.

name: str, required

name of .csv file which will be saved as data asset.

description: str, optional

description of the data asset

get_credentials_from_config

class ibm_watson_machine_learning.helpers.helpers.get_credentials_from_config(env_name, credentials_name, config_path='./config.ini')

Load credentials from config file.

[DEV_LC]

wml_credentials = { } cos_credentials = { }

Parameters

• env_name (str) – the name of [ENV] defined in config file

• credentials_name (str) – name of credentials

• config_path (str) – path to the config file

Returns

dict

>>> get_credentials_from_config(env_name='DEV_LC', credentials_name='wml_credentials')

Enums

class ibm_watson_machine_learning.utils.autoai.enums.ClassificationAlgorithms(value)

Bases: enum.Enum

Classification algorithms that AutoAI could use.

DT = 'DecisionTreeClassifierEstimator'

EX_TREES = 'ExtraTreesClassifierEstimator'

GB = 'GradientBoostingClassifierEstimator'

LGBM = 'LGBMClassifierEstimator'

LR = 'LogisticRegressionEstimator'

RF = 'RandomForestClassifierEstimator'

XGB = 'XGBClassifierEstimator'

class ibm_watson_machine_learning.utils.autoai.enums.DataConnectionTypes

Bases: object

Supported types of DataConnection. OneOf: [s3, FS]

CA = 'connection_asset'

DS = 'data_asset'

FS = 'fs'

S3 = 's3'

class ibm_watson_machine_learning.utils.autoai.enums.Directions

Bases: object

Possible metrics directions

ASCENDING = 'ascending'

DESCENDING = 'descending'

class ibm_watson_machine_learning.utils.autoai.enums.Metrics

Bases: object

Supported types of classification and regression metrics in autoai.

ACCURACY_SCORE = 'accuracy'

AVERAGE_PRECISION_SCORE = 'average_precision'

EXPLAINED_VARIANCE_SCORE = 'explained_variance'

F1_SCORE = 'f1'

F1_SCORE_MACRO = 'f1_macro'

F1_SCORE_MICRO = 'f1_micro'

F1_SCORE_WEIGHTED = 'f1_weighted'

LOG_LOSS = 'neg_log_loss'

MEAN_ABSOLUTE_ERROR = 'neg_mean_absolute_error'

MEAN_SQUARED_ERROR = 'neg_mean_squared_error'

MEAN_SQUARED_LOG_ERROR = 'neg_mean_squared_log_error'

MEDIAN_ABSOLUTE_ERROR = 'neg_median_absolute_error'

PRECISION_SCORE = 'precision'

PRECISION_SCORE_MACRO = 'precision_macro'

PRECISION_SCORE_MICRO = 'precision_micro'

PRECISION_SCORE_WEIGHTED = 'precision_weighted'

R2_SCORE = 'r2'

RECALL_SCORE = 'recall'

RECALL_SCORE_MACRO = 'recall_macro'

RECALL_SCORE_MICRO = 'recall_micro'

RECALL_SCORE_WEIGHTED = 'recall_weighted'

ROC_AUC_SCORE = 'roc_auc'

ROOT_MEAN_SQUARED_ERROR = 'neg_root_mean_squared_error'

ROOT_MEAN_SQUARED_LOG_ERROR = 'neg_root_mean_squared_log_error'

class ibm_watson_machine_learning.utils.autoai.enums.MetricsToDirections(value)

Bases: enum.Enum

Map of metrics directions.

ACCURACY = 'ascending'

AVERAGE_PRECISION = 'ascending'

EXPLAINED_VARIANCE = 'ascending'

F1 = 'ascending'

F1_MACRO = 'ascending'

F1_MICRO = 'ascending'

F1_WEIGHTED = 'ascending'

NEG_LOG_LOSS = 'descending'

NEG_MEAN_ABSOLUTE_ERROR = 'descending'

NEG_MEAN_SQUARED_ERROR = 'descending'

NEG_MEAN_SQUARED_LOG_ERROR = 'descending'

NEG_MEDIAN_ABSOLUTE_ERROR = 'descending'

NEG_ROOT_MEAN_SQUARED_ERROR = 'descending'

NEG_ROOT_MEAN_SQUARED_LOG_ERROR = 'descending'

NORMALIZED_GINI_COEFFICIENT = 'ascending'

PRECISION = 'ascending'

PRECISION_MACRO = 'ascending'

PRECISION_MICRO = 'ascending'

PRECISION_WEIGHTED = 'ascending'

R2 = 'ascending'

RECALL = 'ascending'

RECALL_MACRO = 'ascending'

RECALL_MICRO = 'ascending'

RECALL_WEIGHTED = 'ascending'

ROC_AUC = 'ascending'

class ibm_watson_machine_learning.utils.autoai.enums.PipelineTypes

Bases: object

Supported types of Pipelines.

LALE = 'lale'

SKLEARN = 'sklearn'

class ibm_watson_machine_learning.utils.autoai.enums.PositiveLabelClass

Bases: object

Metrics that need positive label definition for binary classification.

AVERAGE_PRECISION_SCORE = 'average_precision'

F1_SCORE = 'f1'

F1_SCORE_MACRO = 'f1_macro'

F1_SCORE_MICRO = 'f1_micro'

F1_SCORE_WEIGHTED = 'f1_weighted'

PRECISION_SCORE = 'precision'

PRECISION_SCORE_MACRO = 'precision_macro'

PRECISION_SCORE_MICRO = 'precision_micro'

PRECISION_SCORE_WEIGHTED = 'precision_weighted'

RECALL_SCORE = 'recall'

RECALL_SCORE_MACRO = 'recall_macro'

RECALL_SCORE_MICRO = 'recall_micro'

RECALL_SCORE_WEIGHTED = 'recall_weighted'

class ibm_watson_machine_learning.utils.autoai.enums.PredictionType

Bases: object

Supported types of learning. OneOf: [BINARY, MULTICLASS, REGRESSION]

BINARY = 'binary'

MULTICLASS = 'multiclass'

REGRESSION = 'regression'

class ibm_watson_machine_learning.utils.autoai.enums.RegressionAlgorithms(value)

Bases: enum.Enum

Regression algorithms that AutoAI could use.

DT = 'DecisionTreeRegressorEstimator'

EX_TREES = 'ExtraTreesRegressorEstimator'

GB = 'GradientBoostingRegressorEstimator'

LGBM = 'LGBMRegressorEstimator'

LR = 'LinearRegressionEstimator'

RF = 'RandomForestRegressorEstimator'

RIDGE = 'RidgeEstimator'

XGB = 'XGBRegressorEstimator'

class ibm_watson_machine_learning.utils.autoai.enums.RunStateTypes

Bases: object

Supported types of AutoAI fit/run.

COMPLETED = 'completed'

FAILED = 'failed'

class ibm_watson_machine_learning.utils.autoai.enums.TShirtSize

Bases: object

Possible sizes of the AutoAI POD Depends on the POD size, AutoAI could support different data sets sizes.

S - small (2vCPUs and 8GB of RAM) M - Medium (4vCPUs and 16GB of RAM) L - Large (8vCPUs and 32GB of RAM)) XL - Extra Large (16vCPUs and 64GB of RAM)

L = 'l'

M = 'm'

ML = 'ml'

S = 's'

XL = 'xl'

class ibm_watson_machine_learning.utils.autoai.enums.Transformers

Bases: object

Supported types of congito transformers names in autoai.

ABS = 'abs'

CBRT = 'cbrt'

COS = 'cos'

CUBE = 'cube'

DIFF = 'diff'

DIVIDE = 'divide'

FEATUREAGGLOMERATION = 'featureagglomeration'

ISOFORESTANOMALY = 'isoforestanomaly'

LOG = 'log'

MAX = 'max'

MINMAXSCALER = 'minmaxscaler'

NXOR = 'nxor'

PCA = 'pca'

PRODUCT = 'product'

ROUND = 'round'

SIGMOID = 'sigmoid'

SIN = 'sin'

SQRT = 'sqrt'

SQUARE = 'square'

STDSCALER = 'stdscaler'

SUM = 'sum'

TAN = 'tan'

class ibm_watson_machine_learning.utils.autoai.enums.VisualizationTypes

Bases: object

Types of visualization options.

INPLACE = 'inplace'

PDF = 'pdf'

Deployment Modules for AutoAI models

Batch

class ibm_watson_machine_learning.deployment.Batch(source_wml_credentials: Union[dict, WorkSpace] = None, source_project_id: str = None, source_space_id: str = None, target_wml_credentials: Union[dict, WorkSpace] = None, target_project_id: str = None, target_space_id: str = None, wml_credentials: Union[dict, WorkSpace] = None, project_id: str = None, space_id: str = None)

Bases: ibm_watson_machine_learning.deployment.base_deployment.BaseDeployment

The Batch Deployment class. With this class object you can manage any batch deployment.

source_wml_credentials: dictionary, required

Credentials to Watson Machine Learning instance where training was performed.

source_project_id: str, optional

ID of the Watson Studio project where training was performed.

source_space_id: str, optional

ID of the Watson Studio Space where training was performed.

target_wml_credentials: dictionary, required

Credentials to Watson Machine Learning instance where you want to deploy.

target_project_id: str, optional

ID of the Watson Studio project where you want to deploy.

target_space_id: str, optional

ID of the Watson Studio Space where you want to deploy.

create(model: str, deployment_name: str, metadata: Optional[Dict] = None, training_data: Optional[Union[DataFrame, ndarray]] = None, training_target: Optional[Union[DataFrame, ndarray]] = None, experiment_run_id: Optional[str] = None) → None

Create deployment from a model.

model: str, required

AutoAI model name.

deployment_name: str, required

Name of the deployment

training_data: Union[‘pandas.DataFrame’, ‘numpy.ndarray’], optional

Training data for the model

training_target: Union[‘pandas.DataFrame’, ‘numpy.ndarray’], optional

Target/label data for the model

metadata: dictionary, optional

Model meta properties.

experiment_run_id: str, optional

ID of a training/experiment (only applicable for AutoAI deployments)

>>> from ibm_watson_machine_learning.deployment import Batch
>>>
>>> deployment = Batch(
>>>        wml_credentials={
>>>              "apikey": "...",
>>>              "iam_apikey_description": "...",
>>>              "iam_apikey_name": "...",
>>>              "iam_role_crn": "...",
>>>              "iam_serviceid_crn": "...",
>>>              "instance_id": "...",
>>>              "url": "https://us-south.ml.cloud.ibm.com"
>>>            },
>>>         project_id="...",
>>>         space_id="...")
>>>
>>> deployment.create(
>>>        experiment_run_id="...",
>>>        model=model,
>>>        deployment_name='My new deployment'
>>>    )

delete(deployment_id: str = None) → None

Delete deployment on WML.

deployment_id: str, optional

ID of the deployment to delete. If empty, current deployment will be deleted.

>>> deployment = Batch(workspace=...)
>>> # Delete current deployment
>>> deployment.delete()
>>> # Or delete a specific deployment
>>> deployment.delete(deployment_id='...')

get(deployment_id: str) → None

Get WML deployment.

deployment_id: str, required

ID of the deployment to work with.

WebService deployment object

>>> deployment = Batch(workspace=...)
>>> deployment.get(deployment_id="...")

get_job_id(batch_scoring_details)

Get id from batch scoring details.

get_job_params(scoring_job_id: str = None) → Dict

Get batch deployment job parameters.

scoring_job_id: str

Id of scoring job.

Dictionary with parameters of the scoring job.

get_job_result(scoring_job_id: str) → pandas.core.frame.DataFrame

Get batch deployment results of job with id scoring_job_id.

scoring_job_id: str

Id of scoring job which results will be returned.

DataFrame with result.

In case of incompleted or failed MissingScoringResults scoring exception is raised.

get_job_status(scoring_job_id: str) → Dict

Get status of scoring job.

scoring_job_id: str

Id of scoring job.

Dictionary with state of scoring job (one of: [completed, failed, starting, queued])

and additional details if they exist.

get_params() → Dict

Get deployment parameters.

list(limit=None) → pandas.core.frame.DataFrame

List WML deployments.

limit: int, optional

Set the limit of how many deployments to list. Default is None (all deployments should be fetched)

Pandas DataFrame with information about deployments.

>>> deployment = Batch(workspace=...)
>>> deployments_list = deployment.list()
>>> print(deployments_list)
                     created_at  ...  status
    0  2020-03-06T10:50:49.401Z  ...   ready
    1  2020-03-06T13:16:09.789Z  ...   ready
    4  2020-03-11T14:46:36.035Z  ...  failed
    3  2020-03-11T14:49:55.052Z  ...  failed
    2  2020-03-11T15:13:53.708Z  ...   ready

list_jobs()

Returns pandas DataFrame with list of deployment jobs

rerun_job(scoring_job_id: str, background_mode: bool = True) → Union[dict, pandas.core.frame.DataFrame, ibm_watson_machine_learning.helpers.connections.connections.DataConnection]

Rerun scoring job with the same parameters as job described by ‘scoring_job_id’.

scoring_job_id: str

Id described scoring job.

background_mode: bool, optional

Indicator if score_rerun() method will run in background (async) or (sync).

Dictionary with scoring job details.

>>> scoring_details = deployment.score_rerun(scoring_job_id)

run_job(payload: Union[pandas.core.frame.DataFrame, List[ibm_watson_machine_learning.helpers.connections.connections.DataConnection]], output_data_reference: ibm_watson_machine_learning.helpers.connections.connections.DataConnection = None, background_mode: bool = True) → Union[Dict, Dict[str, List], ibm_watson_machine_learning.helpers.connections.connections.DataConnection]

Batch scoring job on WML. Payload or Payload data reference is required. It is passed to the WML where model have been deployed.

payload: pandas.DataFrame or List[DataConnection], required

DataFrame with data to test the model or data storage connection details to inform where payload data is stored.

output_data_reference: DataConnection, optional

DataConnection to the output COS for storing predictions. Required nly when DataConnections are as a payload.

background_mode: bool, optional

Indicator if score() method will run in background (async) or (sync).

Dictionary with scoring job details.

>>> score_details = deployment.score(payload=test_data)
>>> print(score_details['entity']['scoring'])
    {'input_data': [{'fields': ['sepal_length',
                  'sepal_width',
                  'petal_length',
                  'petal_width'],
                 'values': [[4.9, 3.0, 1.4, 0.2]]}],
   'predictions': [{'fields': ['prediction', 'probability'],
                 'values': [['setosa',
                   [0.9999320742502246,
                    5.1519823540224506e-05,
                    1.6405926235405522e-05]]]}]
>>>
>>> payload_reference = DataConnection(location=DSLocation(asset_id=asset_id))
>>> score_details = deployment.score(payload=payload_reference, output_data_filename = "scoring_output.csv")

score(**kwargs)

Scoring on WML. Payload is passed to the WML scoring endpoint where model have been deployed.

payload: pandas.DataFrame, required

DataFrame with data to test the model.

WebService

class ibm_watson_machine_learning.deployment.WebService(source_wml_credentials: Union[dict, WorkSpace] = None, source_project_id: str = None, source_space_id: str = None, target_wml_credentials: Union[dict, WorkSpace] = None, target_project_id: str = None, target_space_id: str = None, wml_credentials: Union[dict, WorkSpace] = None, project_id: str = None, space_id: str = None)

Bases: ibm_watson_machine_learning.deployment.base_deployment.BaseDeployment

An Online Deployment class aka. WebService. With this class object you can manage any online (WebService) deployment.

source_wml_credentials: dictionary, required

Credentials to Watson Machine Learning instance where training was performed.

source_project_id: str, optional

ID of the Watson Studio project where training was performed.

source_space_id: str, optional

ID of the Watson Studio Space where training was performed.

target_wml_credentials: dictionary, required

Credentials to Watson Machine Learning instance where you want to deploy.

target_project_id: str, optional

ID of the Watson Studio project where you want to deploy.

target_space_id: str, optional

ID of the Watson Studio Space where you want to deploy.

create(model: str, deployment_name: str, metadata: Optional[Dict] = None, training_data: Optional[Union[DataFrame, ndarray]] = None, training_target: Optional[Union[DataFrame, ndarray]] = None, experiment_run_id: Optional[str] = None) → None

Create deployment from a model.

model: str, required

AutoAI model name.

deployment_name: str, required

Name of the deployment

training_data: Union[‘pandas.DataFrame’, ‘numpy.ndarray’], optional

Training data for the model

training_target: Union[‘pandas.DataFrame’, ‘numpy.ndarray’], optional

Target/label data for the model

metadata: dictionary, optional

Model meta properties.

experiment_run_id: str, optional

ID of a training/experiment (only applicable for AutoAI deployments)

>>> from ibm_watson_machine_learning.deployment import WebService
>>>
>>> deployment = WebService(
>>>        wml_credentials={
>>>              "apikey": "...",
>>>              "iam_apikey_description": "...",
>>>              "iam_apikey_name": "...",
>>>              "iam_role_crn": "...",
>>>              "iam_serviceid_crn": "...",
>>>              "instance_id": "...",
>>>              "url": "https://us-south.ml.cloud.ibm.com"
>>>            },
>>>         project_id="...",
>>>         space_id="...")
>>>
>>> deployment.create(
>>>        experiment_run_id="...",
>>>        model=model,
>>>        deployment_name='My new deployment'
>>>    )

delete(deployment_id: str = None) → None

Delete deployment on WML.

deployment_id: str, optional

ID of the deployment to delete. If empty, current deployment will be deleted.

>>> deployment = WebService(workspace=...)
>>> # Delete current deployment
>>> deployment.delete()
>>> # Or delete a specific deployment
>>> deployment.delete(deployment_id='...')

get(deployment_id: str) → None

Get WML deployment.

deployment_id: str, required

ID of the deployment to work with.

WebService deployment object

>>> deployment = WebService(workspace=...)
>>> deployment.get(deployment_id="...")

get_params() → Dict

Get deployment parameters.

list(limit=None) → pandas.core.frame.DataFrame

List WML deployments.

limit: int, optional

Set the limit of how many deployments to list. Default is None (all deployments should be fetched)

Pandas DataFrame with information about deployments.

>>> deployment = WebService(workspace=...)
>>> deployments_list = deployment.list()
>>> print(deployments_list)
                     created_at  ...  status
    0  2020-03-06T10:50:49.401Z  ...   ready
    1  2020-03-06T13:16:09.789Z  ...   ready
    4  2020-03-11T14:46:36.035Z  ...  failed
    3  2020-03-11T14:49:55.052Z  ...  failed
    2  2020-03-11T15:13:53.708Z  ...   ready

score(payload: pandas.core.frame.DataFrame) → Dict[str, List]

Online scoring on WML. Payload is passed to the WML scoring endpoint where model have been deployed.

payload: pandas.DataFrame, required

DataFrame with data to test the model.

Dictionary with list od model output/predicted targets.

>>> predictions = deployment.score(payload=test_data)
>>> print(predictions)
    {'predictions':
        [{
            'fields': ['prediction', 'probability'],
            'values': [['no', [0.9221385608558003, 0.07786143914419975]],
                      ['no', [0.9798324002736079, 0.020167599726392187]]
        }]}

API for IBM Cloud Pak® for Data , IBM Watson Machine Learning Server

To use IBM Watson Machine learning Python client APIs in IBM Cloud Pak® for Data or IBM Watson Machine Learning Server , user must create an instance of APIClient with authentication details.

Authentication

Authentication for IBM Cloud Pak® for Data(CP4D)

IBM Cloud Pak® for Data version 3.5 user can create IBM Watson Machine Learning python client by providing the credentials as given below:

Example of creation of client using user Credentials:

from ibm_watson_machine_learning import APIClient
wml_credentials = {
                   "url": "<URL>",
                   "username": "<USERNAME>",
                   "password" : "<PASSWORD>",
                   "instance_id": "openshift",
                   "version" : "3.5"

                  }

client = APIClient(wml_credentials)

Example of creating the client using token:

In IBM Cloud Pak® for Data version 3.5 user can authenticate with token set in the notebook environment.

access_token = os.environ['USER_ACCESS_TOKEN']
from ibm_watson_machine_learning import APIClient

wml_credentials = {
                   "url": "https://us-south.ml.cloud.ibm.com",
                   "token": access_token,
                   "instance_id": "openshift"
                   "version" : "3.5"
                  }
client = APIClient(wml_credentials)

Note

• The version value should be set to “3.0.0” for In IBM Cloud Pak® for Data version 3.0.0 users.

• The version value should be set to “3.0.1” for In IBM Cloud Pak® for Data version 3.0.1 users.

• The version value should be set to “3.5” for In IBM Cloud Pak® for Data version 3.5 users.

• Setting default space id or project id is mandatory. Refer to client.set.default_space() and client.set.default_project() APIs in this document for more example.

Authentication for WML Server

In IBM Watson Machine Learning Server user can authenticate with token set in the notebook environment or with user credentials.

Example of creating the client using User Credentials:

from ibm_watson_machine_learning import APIClient

wml_credentials = {
                   "url": "<URL>",
                   "username": "<USERNAME>",
                   "password" : "<PASSWORD>",
                   "instance_id": "wml_local",
                   "version" : "1.1"

                  }

client = APIClient(wml_credentials)

Example of creating the client using token:

access_token = os.environ['USER_ACCESS_TOKEN']
from ibm_watson_machine_learning import APIClient

wml_credentials = {
                   "url": "https://us-south.ml.cloud.ibm.com",
                   "token": access_token,
                   "instance_id": "wml_local"
                   "version" : "1.1"
                  }

client = APIClient(wml_credentials)

Note

• The version value should be set to corresponding WML Server Product version.( Example: “1.1” or “2.0”).

• Setting default space id or project id is mandatory. Refer to client.set.default_space() and client.set.default_project() APIs in this document for more example.

• The “url” field value should be having the port number as well. For example, the value can be “https://wmlserver.xxx.com:31843”

Supported machine learning frameworks

For the list of supported machine learning frameworks (models) of IBM Cloud Pak® for Data version 3.0.0, Please refer to Watson Machine Learning Documentation.

For the list of supported machine learning frameworks (models) of IBM Cloud Pak® for Data version 3.0.1, Please refer to Watson Machine Learning Documentation.

For the list of supported machine learning frameworks (models) of IBM Watson Machine Learning Server, Please refer to Watson Machine Learning Documentation.

Connections

class client.Connections(client)

Store and manage your Connections.

ConfigurationMetaNames = <ibm_watson_machine_learning.metanames.ConnectionMetaNames object>

MetaNames for Connection creation.

create(meta_props)

Creates a connection. Input to PROPERTIES field examples

1. MySQL

   >>> client.connections.ConfigurationMetaNames.PROPERTIES: {
   >>>   "database": "database",
   >>>   "password": "password",
   >>>   "port": "3306",
   >>>   "host": "host url",
   >>>   "ssl": "false",
   >>>   "username": "username"
   >>> }
   

2. Google Big query

   1. Method1: Use service account json. The service account json generated can be provided as

      input as-is. Provide actual values in json. Example is only indicative to show the fields. Refer to Google big query documents how to generate the service account json

      >>> client.connections.ConfigurationMetaNames.PROPERTIES: {
      >>>     "type": "service_account",
      >>>     "project_id": "project_id",
      >>>     "private_key_id": "private_key_id",
      >>>     "private_key": "private key contents",
      >>>     "client_email": "client_email",
      >>>     "client_id": "client_id",
      >>>     "auth_uri": "https://accounts.google.com/o/oauth2/auth",
      >>>     "token_uri": "https://oauth2.googleapis.com/token",
      >>>     "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
      >>>     "client_x509_cert_url": "client_x509_cert_url"
      >>> }
      

   2. Method2: Using OAuth Method. Refer to Google big query documents how to generate OAuth token

      >>> client.connections.ConfigurationMetaNames.PROPERTIES: {
      >>>    "access_token": "access token generated for big query",
      >>>    "refresh_token": "refresh token",
      >>>    "project_id": "project_id",
      >>>    "client_secret": "This is your gmail account password",
      >>>   "client_id": "client_id"
      >>> }
      

3. MS SQL

   >>> client.connections.ConfigurationMetaNames.PROPERTIES: {
   >>>      "database": "database",
   >>>      "password": "password",
   >>>      "port": "1433",
   >>>      "host": "host",
   >>>      "username": "username"
   >>> }
   

4. Tera data

   >>> client.connections.ConfigurationMetaNames.PROPERTIES: {
   >>>      "database": "database",
   >>>      "password": "password",
   >>>      "port": "1433",
   >>>      "host": "host",
   >>>      "username": "username"
   >>> }
   

Parameters

Important

1. meta_props: meta data of the connection configuration. To see available meta names use:

   >>> client.connections.ConfigurationMetaNames.get()
   

   type: dict

Output

Important

returns: metadata of the stored connection

return type: dict

Example

>>> sqlserver_data_source_type_id = client.connections.get_datasource_type_uid_by_name('sqlserver')
>>> connections_details = client.connections.create({
>>> client.connections.ConfigurationMetaNames.NAME: "sqlserver connection",
>>> client.connections.ConfigurationMetaNames.DESCRIPTION: "connection description",
>>> client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: sqlserver_data_source_type_id,
>>> client.connections.ConfigurationMetaNames.PROPERTIES: { "database": "database",
>>>                                                         "password": "password",
>>>                                                         "port": "1433",
>>>                                                         "host": "host",
>>>                                                         "username": "username"}
>>> })

delete(connection_id)

Delete a stored Connection.

Parameters

Important

1. connection_id: Unique id of the connection to be deleted.

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.connections.delete(connection_id)

get_datasource_type_uid_by_name(name)

Get stored datasource types id for the given datasource type name.

Parameters

Important

1. name: name of datasource type

   type: int

Output

Important

This method only prints the id of given datasource type name.

return type: Str

Example

>>> client.connections.get_datasource_type_uid_by_name('cloudobjectstorage')

get_details(connection_id)

Get connection details for the given unique Connection id.

Parameters

Important

1. connection_id: Unique id of Connection

   type: str

Output

Important

returns: Metadata of the stored Connection

return type: dict

Example

>>> connection_details = client.connections.get_details(connection_id)

static get_uid(connection_details)

Get Unique Id of stored connection.

Parameters

Important

1. connection_details: Metadata of the stored connection

   type: dict

Output

Important

returns: Unique Id of stored connection

return type: str

Example

>>> connection_uid = client.connection.get_uid(connection_details)

list()

List all stored connections.

Output

Important

This method only prints the list of all connections in a table format.

return type: None

Example

>>> client.connections.list()

list_datasource_types()

List stored datasource types assets.

Output

Important

This method only prints the list of datasources type in a table format.

return type: None

Example

>>> client.connections.list_datasource_types()

class metanames.ConnectionMetaNames

Set of MetaNames for Spaces Specs.

Available MetaNames:

MetaName	Type	Required	Schema
NAME	str	Y	my_space
DESCRIPTION	str	N	my_description
DATASOURCE_TYPE	str	Y	1e3363a5-7ccf-4fff-8022-4850a8024b68
PROPERTIES	dict	Y	{'database': 'BLUDB', 'host': 'dashdb-txn-sbox-yp-dal09-04.services.dal.bluemix.net', 'password': 'a1b2c3d4#', 'username': 'usr21370'}

Data assets

class client.Assets(client)

Store and manage your data assets.

ConfigurationMetaNames = <ibm_watson_machine_learning.metanames.AssetsMetaNames object>

MetaNames for Data Assets creation.

create(name, file_path)

Creates a data asset and uploads content to it.

Parameters

Important

1. name: Name to be given to the data asset

   type: str

2. file_path: Path to the content file to be uploaded

   type: str

Output

Important

returns: metadata of the stored data asset

return type: dict

Example

>>> asset_details = client.data_assets.create(name="sample_asset",file_path="/path/to/file")

delete(asset_uid)

Delete a stored data asset.

Parameters

Important

1. asset_uid: Unique Id of data asset

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.data_assets.delete(asset_uid)

download(asset_uid, filename)

Download the content of a data asset.

Parameters

Important

1. asset_uid: The Unique Id of the data asset to be downloaded

   type: str

2. filename: filename to be used for the downloaded file

   type: str

Output

Important

returns: Path to the downloaded asset content

return type: str

Example

>>> client.data_assets.download(asset_uid,"sample_asset.csv")

get_details(asset_uid)

Get data asset details.

Parameters

Important

1. asset_details: Metadata of the stored data asset

   type: dict

Output

Important

returns: Unique id of asset

return type: str

Example

>>> asset_details = client.data_assets.get_details(asset_uid)

static get_href(asset_details)

Get url of stored data asset.

Parameters

Important

1. asset_details: stored data asset details

   type: dict

Output

Important

returns: href of stored data asset

return type: str

Example

>>> asset_details = client.data_assets.get_details(asset_uid)
>>> asset_href = client.data_assets.get_href(asset_details)

static get_id(asset_details)

Get Unique Id of stored script asset.

Parameters

Important

1. asset_details: Metadata of the stored script asset

   type: dict

   type: dict

Output

Important

returns: Unique Id of stored data asset

return type: str

Example

>>> asset_id = client.data_assets.get_id(asset_details)

static get_uid(asset_details)

Get Unique Id of stored data asset. Deprecated!! Use get_id(details) instead

Parameters

Important

1. asset_details: Metadata of the stored data asset

   type: dict

   type: dict

Output

Important

returns: Unique Id of stored asset

return type: str

Example

>>> asset_uid = client.data_assets.get_uid(asset_details)

list(limit=None)

List stored data assets. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all data assets in a table format.

return type: None

Example

>>> client.data_assets.list()

store(meta_props)

Creates a data asset and uploads content to it.

Parameters

Important

1. meta_props: meta data of the space configuration. To see available meta names use:

   >>> client.data_assets.ConfigurationMetaNames.get()
   

   type: dict

Example

Example for data asset creation for files :

>>> metadata = {
>>>  client.data_assets.ConfigurationMetaNames.NAME: 'my data assets',
>>>  client.data_assets.ConfigurationMetaNames.DESCRIPTION: 'sample description',
>>>  client.data_assets.ConfigurationMetaNames.DATA_CONTENT_NAME: 'sample.csv'
>>> }
>>> asset_details = client.data_assets.store(meta_props=metadata)

Example of data asset creation using connection:

>>> metadata = {
>>>  client.data_assets.ConfigurationMetaNames.NAME: 'my data assets',
>>>  client.data_assets.ConfigurationMetaNames.DESCRIPTION: 'sample description',
>>>  client.data_assets.ConfigurationMetaNames.CONNECTION_ID: '39eaa1ee-9aa4-4651-b8fe-95d3ddae',
>>>  client.data_assets.ConfigurationMetaNames.DATA_CONTENT_NAME: 't1/sample.csv'
>>> }
>>> asset_details = client.data_assets.store(meta_props=metadata)

Example for data asset creation with database sources type connection:

>>> metadata = {
>>>  client.data_assets.ConfigurationMetaNames.NAME: 'my data assets',
>>>  client.data_assets.ConfigurationMetaNames.DESCRIPTION: 'sample description',
>>>  client.data_assets.ConfigurationMetaNames.CONNECTION_ID: '23eaf1ee-96a4-4651-b8fe-95d3dadfe',
>>>  client.data_assets.ConfigurationMetaNames.DATA_CONTENT_NAME: 't1'
>>> }
>>> asset_details = client.data_assets.store(meta_props=metadata)

class metanames.AssetsMetaNames

Set of MetaNames for Data Asset Specs.

Available MetaNames:

MetaName	Type	Required	Schema
NAME	str	Y	my_data_asset
DATA_CONTENT_NAME	str	Y	/test/sample.csv
CONNECTION_ID	str	N	39eaa1ee-9aa4-4651-b8fe-95d3ddae
DESCRIPTION	str	N	my_description

Deployments

class client.Deployments(client)

Deploy and score published artifacts (models and functions).

create(artifact_uid=None, meta_props=None, rev_id=None, **kwargs)

Create a deployment from an artifact. As artifact we understand model or function which may be deployed.

Parameters

Important

1. artifact_uid: Published artifact UID (model or function uid)

   type: str

2. meta_props: metaprops. To see the available list of metanames use:

   >>> client.deployments.ConfigurationMetaNames.get()
   

   type: dict

Output

Important

returns: metadata of the created deployment

return type: dict

Example

>>> meta_props = {
>>> wml_client.deployments.ConfigurationMetaNames.NAME: "SAMPLE DEPLOYMENT NAME",
>>> wml_client.deployments.ConfigurationMetaNames.ONLINE: {},
>>> wml_client.deployments.ConfigurationMetaNames.HARDWARE_SPEC : { "id":  "e7ed1d6c-2e89-42d7-aed5-8sb972c1d2b"}
>>> }
>>> deployment_details = client.deployments.create(artifact_uid, meta_props)

create_job(deployment_id, meta_props)

Create an asynchronous deployment job.

Parameters

Important

1. deployment_id: Unique Id of Deployment

   type: str

2. meta_props: metaprops. To see the available list of metanames use:

   >>> client.deployments.ScoringMetaNames.get() or client.deployments.DecisionOptimizationmetaNames.get()
   

   type: dict

Output

Important

returns: metadata of the created async deployment job

return type: dict

Note

• The valid payloads for scoring input are either list of values, pandas or numpy dataframes.

Example

>>> scoring_payload = {wml_client.deployments.ScoringMetaNames.INPUT_DATA: [{'fields': ['GENDER','AGE','MARITAL_STATUS','PROFESSION'], 'values': [['M',23,'Single','Student'],['M',55,'Single','Executive']]}]}
>>> async_job = client.deployments.create_job(deployment_id, scoring_payload)

delete(deployment_uid)

Delete deployment.

Parameters

Important

1. deployment uid: Unique Id of Deployment

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.deployments.delete(deployment_uid)

delete_job(job_uid, hard_delete=False)

Cancels a deployment job that is currenlty running. This method is also be used to delete metadata details of the completed or canceled jobs when hard_delete parameter is set to True.

Parameters

Important

1. job_uid: Unique Id of deployment job which should be canceled

   type: str

2. hard_delete: specify True or False.

   True - To delete the completed or canceled job. False - To cancel the currently running deployment job. Default value is False.

   type: Boolean

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.deployments.delete_job(job_uid)

download(virtual_deployment_uid, filename=None)

Downloads file deployment of specified deployment Id. Currently supported format is Core ML.

Parameters

Important

1. virtual_deployment_uid: Unique Id of virtual deployment.

   type: str

2. filename: filename of downloaded archive (optional).

   type: str

Output

Important

returns: Path to downloaded file.

return type: str

Example

>>> client.deployments.download(virtual_deployment_uid)

get_details(deployment_uid=None, limit=None)

Get information about your deployment(s). If deployment_uid is not passed, all deployment details are fetched.

Parameters

Important

1. deployment_uid: Unique Id of Deployment (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: metadata of deployment(s)

return type: dict

dict (if deployment_uid is not None) or {“resources”: [dict]} (if deployment_uid is None)

Note

If deployment_uid is not specified, all deployments metadata is fetched

Example

>>> deployment_details = client.deployments.get_details(deployment_uid)
>>> deployment_details = client.deployments.get_details(deployment_uid=deployment_uid)
>>> deployments_details = client.deployments.get_details()

static get_download_url(deployment_details)

Get deployment_download_url from deployment details.

Parameters

Important

1. deployment_details: Created deployment details.

   type: dict

Output

Important

returns: Deployment download URL that is used to get file deployment (for example: Core ML).

return type: str

Example

>>> deployment_url = client.deployments.get_download_url(deployment)

static get_href(deployment_details)

Get deployment_href from deployment details.

Parameters

Important

1. deployment_details: Metadata of the deployment.

   type: dict

Output

Important

returns: Deployment href that is used to manage the deployment.

return type: str

Example

>>> deployment_href = client.deployments.get_href(deployment)

static get_id(deployment_details)

Get deployment id from deployment details.

Parameters

Important

1. deployment_details: Metadata of the deployment

   type: dict

Output

Important

returns: deployment ID that is used to manage the deployment

return type: str

Example

>>> deployment_id = client.deployments.get_id(deployment)

get_job_details(job_uid=None, limit=None)

Get information about your deployment job(s). If deployment job_uid is not passed, all deployment jobs details are fetched.

Parameters

Important

1. job_uid: Unique Job ID (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: metadata of deployment job(s)

return type: dict

dict (if job_uid is not None) or {“resources”: [dict]} (if job_uid is None)

Note

If job_uid is not specified, all deployment jobs metadata associated with the deployment Id is fetched

Example

>>> deployment_details = client.deployments.get_job_details()
>>> deployments_details = client.deployments.get_job_details(job_uid=job_uid)

get_job_href(job_details)

Get the href of the deployment job.

Parameters

Important

1. job_details: metadata of the deployment job

   type: dict

Output

Important

returns: href of the deployment job

return type: str

Example

>>> job_details = client.deployments.get_job_details(job_uid=job_uid)
>>> job_status = client.deployments.get_job_href(job_details)

get_job_status(job_id)

Get the status of the deployment job.

Parameters

Important

1. job_id: Unique Id of the deployment job

   type: str

Output

Important

returns: status of the deployment job

return type: dict

Example

>>> job_status = client.deployments.get_job_status(job_uid)

get_job_uid(job_details)

Get the Unique Id of the deployment job.

Parameters

Important

1. job_details: metadata of the deployment job

   type: dict

Output

Important

returns: Unique Id of the deployment job

return type: str

Example

>>> job_details = client.deployments.get_job_details(job_uid=job_uid)
>>> job_status = client.deployments.get_job_uid(job_details)

static get_scoring_href(deployment_details)

Get scoring url from deployment details.

Parameters

Important

1. deployment_details: Metadata of the deployment

   type: dict

Output

Important

returns: scoring endpoint url that is used for making scoring requests

return type: str

Example

>>> scoring_href = client.deployments.get_scoring_href(deployment)

static get_uid(deployment_details)

Get deployment_uid from deployment details. Deprecated!! Use get_id(deployment_details) instead

Parameters

Important

1. deployment_details: Metadata of the deployment

   type: dict

Output

Important

returns: deployment UID that is used to manage the deployment

return type: str

Example

>>> deployment_uid = client.deployments.get_uid(deployment)

list(limit=None)

List deployments. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all deployments in a table format.

return type: None

Example

>>> client.deployments.list()

list_jobs(limit=None)

List the async deployment jobs. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all async jobs in a table format.

return type: None

Note

• This method list only async deployment jobs created for WML deployment.

Example

>>> client.deployments.list_jobs()

score(deployment_id, meta_props)

Make scoring requests against deployed artifact.

Parameters

Important

1. deployment_id: Unique Id of the deployment to be scored

   type: str

2. meta_props: Meta props for scoring

   >>> Use client.deployments.ScoringMetaNames.show() to view the list of ScoringMetaNames.
   

   type: dict

3. transaction_id: transaction id to be passed with records during payload logging (optional)

   type: str

Output

Important

returns: scoring result containing prediction and probability

return type: dict

Note

• client.deployments.ScoringMetaNames.INPUT_DATA is the only metaname valid for sync scoring.

• The valid payloads for scoring input are either list of values, pandas or numpy dataframes.

Example

>>> scoring_payload = {wml_client.deployments.ScoringMetaNames.INPUT_DATA:
>>>        [{'fields':
>>>            ['GENDER','AGE','MARITAL_STATUS','PROFESSION'],
>>>            'values': [
>>>                ['M',23,'Single','Student'],
>>>                ['M',55,'Single','Executive']
>>>                ]
>>>         }
>>>       ]}
>>> predictions = client.deployments.score(deployment_id, scoring_payload)
>>> predictions = client.deployments.score(deployment_id, scoring_payload,async=True)

update(deployment_uid, changes)

Updates existing deployment metadata. If ASSET is patched, then ‘id’ field is mandatory and it starts a deployment with the provided asset id/rev. Deployment id remains same

Parameters

Important

1. deployment_uid: Unique Id of deployment which should be updated

   type: str

2. changes: elements which should be changed, where keys are ConfigurationMetaNames

   type: dict

Output

Important

returns: metadata of updated deployment

return type: dict

Example

>>> metadata = {
>>> client.deployments.ConfigurationMetaNames.NAME:"updated_Deployment",
>>> client.deployments.ConfigurationMetaNames.ASSET: { "id": "ca0cd864-4582-4732-b365-3165598dc945", "rev":"2" }
>>> }
>>>
>>> deployment_details = client.deployments.update(deployment_uid, changes=metadata)

class metanames.DeploymentMetaNames

Set of MetaNames for Deployments Specs.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
NAME	str	N	my_deployment
TAGS	list	N	[{'value': 'dsx-project.<project-guid>', 'description': 'DSX project guid'}]	[{'value(required)': 'string', 'description(optional)': 'string'}]
DESCRIPTION	str	N	my_deployment
CUSTOM	dict	N	{}
AUTO_REDEPLOY	bool	N	False
SPACE_UID	str	N	3c1ce536-20dc-426e-aac7-7284cf3befc6
COMPUTE	dict	N	None
ONLINE	dict	N	{}
BATCH	dict	N	{}
VIRTUAL	dict	N	{}
ASSET	dict	N	{}
R_SHINY	dict	N	{}
HYBRID_PIPELINE_HARDWARE_SPECS	list	N	[{'id': '3342-1ce536-20dc-4444-aac7-7284cf3befc'}]
HARDWARE_SPEC	dict	N	{'id': '3342-1ce536-20dc-4444-aac7-7284cf3befc'}

class metanames.ScoringMetaNames

Set of MetaNames for Scoring.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
NAME	str	N	jobs test
INPUT_DATA	list	N	[{'fields': ['name', 'age', 'occupation'], 'values': [['john', 23, 'student']]}]	[{'name(optional)': 'string', 'id(optional)': 'string', 'fields(optional)': 'array[string]', 'values': 'array[array[string]]'}]
INPUT_DATA_REFERENCES	list	N		[{'id(optional)': 'string', 'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}, 'schema(optional)': {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}}]
OUTPUT_DATA_REFERENCE	dict	N		{'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}, 'schema(optional)': {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}}
EVALUATIONS_SPEC	list	N	[{'id': 'string', 'input_target': 'string', 'metrics_names': ['auroc', 'accuracy']}]	[{'id(optional)': 'string', 'input_target(optional)': 'string', 'metrics_names(optional)': 'array[string]'}]
ENVIRONMENT_VARIABLES	dict	N	{'my_env_var1': 'env_var_value1', 'my_env_var2': 'env_var_value2'}

class metanames.DecisionOptimizationMetaNames

Set of MetaNames for Decision Optimization.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
INPUT_DATA	list	N	[{'fields': ['name', 'age', 'occupation'], 'values': [['john', 23, 'student']]}]	[{'name(optional)': 'string', 'id(optional)': 'string', 'fields(optional)': 'array[string]', 'values': 'array[array[string]]'}]
INPUT_DATA_REFERENCES	list	N	[{'fields': ['name', 'age', 'occupation'], 'values': [['john', 23, 'student']]}]	[{'name(optional)': 'string', 'id(optional)': 'string', 'fields(optional)': 'array[string]', 'values': 'array[array[string]]'}]
OUTPUT_DATA	list	N		[{'name(optional)': 'string'}]
OUTPUT_DATA_REFERENCES	list	N		{'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}, 'schema(optional)': {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}}
SOLVE_PARAMETERS	dict	N

Hardware specifications

class client.HwSpec(client)

Store and manage your hardware specs.

ConfigurationMetaNames = <ibm_watson_machine_learning.metanames.HwSpecMetaNames object>

MetaNames for Hardware Specification.

get_details(hw_spec_uid)

Get hardware specification details.

Parameters

Important

hw_spec_uid : Unique id of the hardware spec ** type**: str

Output

Important

returns: Metadata of the hardware specifications

return type: dict

Example

>>> hw_spec_details = client.hardware_specifications.get_details(hw_spec_uid)

static get_href(hw_spec_details)

Get url of hardware specifications.

Parameters

Important

1. hw_spec_details: hardware specifications details

   type: dict

Output

Important

returns: href of hardware specifications

return type: str

Example

>>> hw_spec_details = client.hw_spec.get_details(hw_spec_uid)
>>> hw_spec_href = client.hw_spec.get_href(hw_spec_details)

static get_id(hw_spec_details)

Get ID of hardware specifications asset.

Parameters

Important

1. asset_details: Metadata of the hardware specifications

   type: dict

Output

Important

returns: Unique Id of hardware specifications

return type: str

Example

>>> asset_uid = client.hardware_specifications.get_id(hw_spec_details)

get_id_by_name(hw_spec_name)

Get Unique Id of hardware specification for the given name.

Parameters

Important

1. hw_spec_name: name of the hardware spec

   type: str

Output

Important

returns: Unique Id of hardware specification

return type: str

Example

>>> asset_uid = client.hardware_specifications.get_id_by_name(hw_spec_name)

static get_uid(hw_spec_details)

Get UID of hardware specifications asset. Deprecated!! Use get_id(hw_spec_details) instead

Parameters

Important

1. asset_details: Metadata of the hardware specifications

   type: dict

Output

Important

returns: Unique Id of hardware specifications

return type: str

Example

>>> asset_uid = client.hardware_specifications.get_uid(hw_spec_details)

get_uid_by_name(hw_spec_name)

Get Unique Id of hardware specification for the given name. Deprecated!! Use get_id_by_name(self, hw_spec_name) instead

Parameters

Important

1. hw_spec_name: name of the hardware spec

   type: str

Output

Important

returns: Unique Id of hardware specification

return type: str

Example

>>> asset_uid = client.hardware_specifications.get_uid_by_name(hw_spec_name)

list(name=None)

List hardware specifications.

Parameters

Important

name : Unique id of the hardware spec ** type**: str

Output

Important

This method only prints the list of all assets in a table format.

return type: None

Example

>>> client.hardware_specifications.list()

class metanames.HwSpecMetaNames

Set of MetaNames for Software Specifications Specs.

Available MetaNames:

MetaName	Type	Required	Schema
NAME	str	Y	Python 3.6 with pre-installed ML package
DESCRIPTION	str	N	my_description
HARDWARE_CONFIGURATION	dict	N	{}

Model definitions

class client.ModelDefinition(client)

Store and manage your model_definitions.

ConfigurationMetaNames = <ibm_watson_machine_learning.metanames.ModelDefinitionMetaNames object>

MetaNames for model_definition creation.

create_revision(model_definition_uid)

Creates revision for the given model_definition. Revisions are immutable once created. The metadata and attachment at model_definition is taken and a revision is created out of it.

Parameters

Important

1. model_definition: model_definition ID.

   type: str

Output

Important

returns: Stored model_definition revisions metadata.

return type: dict

Example

>>> model_definition_revision = client.model_definitions.create_revision(model_definition_id)

delete(model_definition_uid)

Delete a stored model_definition.

Parameters

Important

1. model_definition_uid: Unique Id of stored Model definition

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.model_definitions.delete(model_definition_uid)

download(model_definition_uid, filename, rev_id=None)

Download the content of a script asset.

Parameters

Important

1. model_definition_uid: The Unique Id of the model_definition asset to be downloaded

   type: str

2. filename: filename to be used for the downloaded file

   type: str

3. rev_id: Revision id

   type: str

Output

Important

returns: Path to the downloaded asset content

return type: str

Example

>>> client.script.download(asset_uid,"script_file.zip")

get_details(model_definition_uid)

Get metadata of stored model_definition.

Parameters

Important

1. model_definition_uid: Unique Id of model_definition

   type: str

Output

Important

returns: metadata of model definition

return type: dict (if model_definition_uid is not None)

Example

>>> model_definition_details = client.model_definitions.get_details(model_definition_uid)

get_href(model_definition_details)

Get href of stored model_definition.

Parameters

Important

1. model_definition_details: Stored model_definition details.

   type: dict

Output

Important

returns: href of stored model_definition.

return type: str

Example

>>> model_definition_uid = client.model_definitions.get_href(model_definition_details)

get_id(model_definition_details)

Get Unique Id of stored model_definition asset.

Parameters

Important

1. model_definition_details: Metadata of the stored model_definition asset

   type: dict

Output

Important

returns: Unique Id of stored model_definition asset

return type: str

Example

>>> asset_uid = client.model_definition.get_id(asset_details)

get_revision_details(model_definition_uid, rev_uid=None)

Get metadata of model_definit

Parameters

Important

1. model_definition_uid: model_definition ID.

   type: str

2. rev_uid: Revision ID. If this parameter is not provided, returns latest revision if existing else error.

   type: int

Output

Important

returns: Stored model definitions metadata.

return type: dict

Example

>>> script_details = client.model_definitions.get_revision_details(model_definition_uid, rev_uid)

get_uid(model_definition_details)

Get uid of stored model. Deprecated!! Use get_id(model_definition_details) instead

Parameters

Important

1. param model_definition_details: stored model_definition details

   type: dict

Output

Important

returns: uid of stored model_definition

return type: str

Example

>>> model_definition_uid = client.model_definitions.get_uid(model_definition_details)

list(limit=None)

List stored model_definition assets. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all model_definition assets in a table format.

return type: None

Example

>>> client.model_definitions.list()

list_revisions(model_definition_uid, limit=None)

List stored model_definition assets. If limit is set to None there will be only first 50 records shown.

Parameters

Important

1. model_definition_uid: Unique id of model_definition

   type: str

2. limit: limit number of fetched records

   type: int

Output

Important

This method only prints the list of all model_definition revision in a table format.

return type: None

Example

>>> client.model_definitions.list_revisions()

store(model_definition, meta_props)

Create a model_definitions.

Parameters

Important

1. meta_props: meta data of the model_definition configuration. To see available meta names use:

   >>> client.model_definitions.ConfigurationMetaNames.get()
   

   type: dict

2. model_definition: Path to the content file to be uploaded

   type: str

Output

Important

returns: Metadata of the model_defintion created

return type: dict

Example

>>>  client.model_definitions.store(model_definition, meta_props)

update(model_definition_id, meta_props=None, file_path=None)

Update model_definition with either metadata or attachment or both.

Parameters

Important

1. model_definition_id: model_definition ID.

   type: str

2. file_path: Path to the content file to be uploaded.

   type: str

** Output**

Important

returns: Updated metadata of model_definition.

return type: dict

Example

>>> model_definition_details = client.model_definition.update(model_definition_id, meta_props, file_path)

class metanames.ModelDefinitionMetaNames

Set of MetaNames for Model Definition.

Available MetaNames:

MetaName	Type	Required	Example value	Schema
NAME	str	Y	my_model_definition
DESCRIPTION	str	N	my model_definition
PLATFORM	dict	Y	{'name': 'python', 'versions': ['3.5']}	{'name(required)': 'string', 'versions(required)': ['versions']}
VERSION	str	Y	1.0
COMMAND	str	N	python3 convolutional_network.py
CUSTOM	dict	N	{'field1': 'value1'}
SPACE_UID	str	N	3c1ce536-20dc-426e-aac7-7284cf3befc6

Package extensions

class client.PkgExtn(client)

Store and manage your software Packages Extension specs.

ConfigurationMetaNames = <ibm_watson_machine_learning.metanames.PkgExtnMetaNames object>

MetaNames for Package Extensions creation.

delete(pkg_extn_uid)

Delete a package extension.

Parameters

Important

1. pkg_extn_uid: Unique Id of package extension

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.package_extensions.delete(pkg_extn_uid)

download(pkg_extn_id, filename)

Download a package extension.

Parameters

Important

1. pkg_extn_uid: The Unique Id of the package extension to be downloaded

   type: str

2. filename: filename to be used for the downloaded file

   type: str

Output

Important

returns: Path to the downloaded package extension content

return type: str

Example

>>> client.assets.download(asset_uid,"sample_conda.yml/custom_library.zip")

get_details(pkg_extn_uid)

Get package extensions details.

Parameters

Important

1. pkg_extn_details: Metadata of the package extensions

   type: dict

Output

Important

returns: pkg_extn UID

return type: str

Example

>>> pkg_extn_details = client.pkg_extn.get_details(pkg_extn_uid)

static get_href(pkg_extn_details)

Get url of stored package extensions.

Parameters

Important

1. asset_details: package extensions details

   type: dict

Output

Important

returns: href of package extensions details

return type: str

Example

>>> pkg_extn_details = client.package_extensions.get_details(pkg_extn_uid)
>>> pkg_extn_href = client.package_extensions.get_href(pkg_extn_details)

static get_id(pkg_extn_details)

Get Unique Id of package extensions.

Parameters

Important

1. asset_details: Metadata of the package extensions

   type: dict

Output

Important

returns: Unique Id of package extension

return type: str

Example

>>> asset_id = client.package_extensions.get_id(pkg_extn_details)

get_id_by_name(pkg_extn_name)

Get ID of package extensions.

Parameters

Important

1. asset_details: Metadata of the package extension

   type: dict

Output

Important

returns: Unique Id of package extension

return type: str

Example

>>> asset_id = client.package_extensions.get_id_by_name(pkg_extn_name)

static get_uid(pkg_extn_details)

Get Unique Id of package extensions. Deprecated!! use get_id(pkg_extn_details) instead

Parameters

Important

1. asset_details: Metadata of the package extensions

   type: dict

Output

Important

returns: Unique Id of package extension

return type: str

Example

>>> asset_uid = client.package_extensions.get_uid(pkg_extn_details)

get_uid_by_name(pkg_extn_name)

Get UID of package extensions. Deprecated!! Use get_id_by_name(pkg_extn_name) instead

Parameters

Important

1. asset_details: Metadata of the package extension

   type: dict

Output

Important

returns: Unique Id of package extension

return type: str

Example

>>> asset_uid = client.package_extensions.get_uid_by_name(pkg_extn_name)

list()

List package extensions.

Output

Important

This method only prints the list of all package extensionss in a table format.

return type: None

Example

>>> client.package_extensions.list()

store(meta_props, file_path)

Create a package extensions.

Parameters

Important

1. meta_props: meta data of the pacakge extension. To see available meta names use:

   >>> client.package_extensions.ConfigurationMetaNames.get()
   

   type: dict

Output

Important

returns: metadata of the package extensions

return type: dict

Example

>>> meta_props = {
>>>    client.package_extensions.ConfigurationMetaNames.NAME: "skl_pipeline_heart_problem_prediction",
>>>    client.package_extensions.ConfigurationMetaNames.DESCRIPTION: "description scikit-learn_0.20",
>>>    client.package_extensions.ConfigurationMetaNames.TYPE: "conda_yml",
>>> }

Example

>>> pkg_extn_details = client.package_extensions.store(meta_props=meta_props,file_path="/path/to/file")

class metanames.PkgExtnMetaNames

Set of MetaNames for Package Extensions Specs.

Available MetaNames:

MetaName	Type	Required	Schema
NAME	str	Y	Python 3.6 with pre-installed ML package
DESCRIPTION	str	N	my_description
TYPE	str	Y	conda_yml/custom_library

Repository

class client.Repository(client)

Store and manage your models, functions, spaces, pipelines and experiments using Watson Machine Learning Repository.

Important

1. To view ModelMetaNames, use:

   >>> client.repository.ModelMetaNames.show()
   

2. To view ExperimentMetaNames, use:

   >>> client.repository.ExperimentMetaNames.show()
   

3. To view FunctionMetaNames, use:

   >>> client.repository.FunctionMetaNames.show()
   

4. To view PipelineMetaNames, use:

   >>> client.repository.PipelineMetaNames.show()
   

clone(artifact_id, space_id=None, action='copy', rev_id=None)

Creates a new resource(models, runtimes, libraries, experiments, functions, pipelines) identical with the model either in the same space or in a new space. All dependent assets will be cloned too.

Parameters

Important

1. model_id: Guid of the artifact to be cloned:

   type: str

2. space_id: Guid of the space to which the model needs to be cloned. (optional)

   type: str

3. action: Action specifying “copy” or “move”. (optional)

   type: str

4. rev_id: Revision ID of the artifact. (optional)

   type: str

Output

Important

returns: Metadata of the model cloned.

return type: dict

Example

>>> client.repository.clone(artifact_id=artifact_id,space_id=space_uid,action="copy")

Note

• If revision id is not specified, all revisions of the artifact are cloned

• Default value of the parameter action is copy

• Space guid is mandatory for move action

create_experiment_revision(experiment_uid)

Create a new version for a experiment.

Parameters

Important

1. experiment_uid: Unique ID of the experiment.

   type: str

Output

Important

returns: experiment version details.

return type: dict

Example

>>> stored_experiment_revision_details = client.repository.create_experiment_revision(experiment_uid)

create_function_revision(function_uid)

Create a new version for a function.

Parameters

Important

1. function_uid: Unique ID of the function.

   type: str

Output

Important

returns: Function version details.

return type: dict

Example

>>> stored_function_revision_details = client.repository.create_function_revision( function_uid)

create_member(space_uid, meta_props)

Create a member within a space.

Parameters

Important

1. meta_props: meta data of the member configuration. To see available meta names use:

   >>> client.spaces.ConfigurationMetaNames.get()
   

   type: dict

Output

Important

returns: metadata of the stored member

return type: dict

Note

• client.spaces.MemberMetaNames.ROLE can be any one of the following “viewer, editor, admin”

• client.spaces.MemberMetaNames.IDENTITY_TYPE can be any one of the following “user,service”

• client.spaces.MemberMetaNames.IDENTITY can be either service-ID or IAM-userID

Example

>>> metadata = {
>>>  client.spaces.MemberMetaNames.ROLE:"Admin",
>>>  client.spaces.MemberMetaNames.IDENTITY:"iam-ServiceId-5a216e59-6592-43b9-8669-625d341aca71",
>>>  client.spaces.MemberMetaNames.IDENTITY_TYPE:"service"
>>> }
>>> members_details = client.repository.create_member(space_uid=space_id, meta_props=metadata)

create_model_revision(model_uid)

Create a new version for a model.

Parameters

Important

1. model_uid: Model ID.

   type: str

Output

Important

returns: Model version details.

return type: dict

Example

>>> stored_model_revision_details = client.repository.create_model_revision( model_uid="MODELID")

create_pipeline_revision(pipeline_uid)

Create a new version for a model.

Parameters

Important

1. pipeline_uid: Unique ID of the Pipeline.

   type: str

Output

Important

returns: Pipeline version details.

return type: dict

Example

>>> stored_pipeline_revision_details = client.repository.create_pipeline_revision( pipeline_uid)

create_revision(artifact_uid)

Create revision for passed artifact_uid.

Parameters

Important

1. artifact_uid: Unique id of stored model, experiment, function or pipelines.

   type: str

Output

Important

returns: Artifact new revision metadata.

return type: dict

Example

>>> details = client.repository.create_revision(artifact_uid)

delete(artifact_uid)

Delete model, experiment, pipeline, space, runtime, library or function from repository.

Parameters

Important

1. artifact_uid: Unique id of stored model, experiment, function, pipeline, space, library or runtime

   type: str

Output

Important

returns: status (“SUCCESS” or “FAILED”)

return type: str

Example

>>> client.repository.delete(artifact_uid)

download(artifact_uid, filename='downloaded_artifact.tar.gz', rev_uid=None, format=None)

Downloads configuration file for artifact with specified uid.

Parameters

Important

1. artifact_uid: Unique Id of model, function, runtime or library

   type: str

2. filename: Name of the file to which the artifact content has to be downloaded

   default value: downloaded_artifact.tar.gz

   type: str

Output

Important

returns: Path to the downloaded artifact content

return type: str

Note

If filename is not specified, the default filename is “downloaded_artifact.tar.gz”.

Example

>>> client.repository.download(model_uid, 'my_model.tar.gz')

get_details(artifact_uid=None)

Get metadata of stored artifacts. If artifact_uid is not specified returns all models, experiments, functions, pipelines, spaces, libraries and runtimes metadata.

Parameters

Important

1. artifact_uid: Unique Id of stored model, experiment, function, pipeline, space, library or runtime (optional)

   type: str

Output

Important

returns: stored artifact(s) metadata

return type: dict

dict (if artifact_uid is not None) or {“resources”: [dict]} (if artifact_uid is None)

Note

If artifact_uid is not specified, all models, experiments, functions, pipelines, spaces, libraries and runtimes metadata is fetched

Example

>>> details = client.repository.get_details(artifact_uid)
>>> details = client.repository.get_details()

get_experiment_details(experiment_uid=None, limit=None)

Get metadata of experiment. If no experiment_uid is specified all experiments metadata is returned.

Parameters

Important

1. experiment_uid: Unique Id of experiment (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: experiment(s) metadata

return type: dict

dict (if experiment_uid is not None) or {“resources”: [dict]} (if experiment_uid is None)

Note

If experiment_uid is not specified, all experiments metadata is fetched

Example

>>> experiment_details = client.respository.get_experiment_details(experiment_uid)

static get_experiment_href(experiment_details)

Get href of stored experiment.

Parameters

Important

1. experiment_details: Metadata of the stored experiment

   type: dict

Output

Important

returns: href of stored experiment

return type: str

Example

>>> experiment_details = client.repository.get_experiment_detailsf(experiment_uid)
>>> experiment_href = client.repository.get_experiment_href(experiment_details)

static get_experiment_id(experiment_details)

Get Unique Id of stored experiment.

Parameters

Important

1. experiment_details: Metadata of the stored experiment

   type: dict

Output

Important

returns: Unique Id of stored experiment

return type: str

Example

>>> experiment_details = client.repository.get_experiment_details(experiment_uid)
>>> experiment_uid = client.repository.get_experiment_id(experiment_details)

get_experiment_revision_details(experiment_uid, rev_id)

Get metadata of experiment revision.

Parameters

Important

1. experiment_uid: Unique Id of experiment

   type: str

2. rev_id: Unique id of experiment revision

   type: str

Output

Important

returns: experiment revision metadata

return type: dict

Example

>>> experiment_rev_details = client.respository.get_experiment__revision_details(experiment_uid, rev_uid)

static get_experiment_uid(experiment_details)

Get Unique Id of stored experiment.

Parameters

Important

1. experiment_details: Metadata of the stored experiment

   type: dict

Output

Important

returns: Unique Id of stored experiment

return type: str

Example

>>> experiment_details = client.repository.get_experiment_detailsf(experiment_uid)
>>> experiment_uid = client.repository.get_experiment_uid(experiment_details)

get_function_details(function_uid=None, limit=None)

Get metadata of function. If no function_uid is specified all functions metadata is returned.

Parameters

Important

1. function_uid: Unique Id of function (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: function(s) metadata

return type: dict (if function_uid is not None) or {“resources”: [dict]} (if function_uid is None)

Note

If function_uid is not specified, all functions metadata is fetched

Example

>>> function_details = client.respository.get_function_details(function_uid)
>>> function_details = client.respository.get_function_details()

static get_function_href(function_details)

Get href of stored function.

Parameters

Important

1. function_details: Metadata of the stored function

   type: dict

Output

Important

returns: href of stored function

return type: str

Example

>>> function_details = client.repository.get_function_detailsf(function_uid)
>>> function_url = client.repository.get_function_href(function_details)

static get_function_id(function_details)

Get Id of stored function.

Parameters

Important

1. function_details: Metadata of the stored function

   type: dict

Output

Important

returns: Id of stored function

return type: str

Example

>>> function_details = client.repository.get_function_details(function_uid)
>>> function_id = client.repository.get_function_id(function_details)

get_function_revision_details(function_uid, rev_id)

Get metadata of function revision.

Parameters

Important

1. function_uid: Unique Id of function

   type: str

2. rev_id: Unique Id of function revision

   type: str

Output

Important

returns: function revision metadata

return type: dict

Example

>>> function_rev_details = client.respository.get_function_revision_details(function_uid, rev_id)

static get_function_uid(function_details)

Get Unique Id of stored function. Deprecated!! Use get_function_id(function_details) instead

Parameters

Important

1. function_details: Metadata of the stored function

   type: dict

Output

Important

returns: Unique Id of stored function

return type: str

Example

>>> function_details = client.repository.get_function_detailsf(function_uid)
>>> function_uid = client.repository.get_function_uid(function_details)

static get_member_href(member_details)

Get member_href from member details.

Parameters

Important

1. space_details: Metadata of the stored member

   type: dict

Output

Important

returns: member href

return type: str

Example

>>> member_details = client.repository.get_member_details(member_id)
>>> member_href = client.repository.get_member_href(member_details)

static get_member_uid(member_details)

Get member_uid from member details.

Parameters

Important

1. member_details: Metadata of the created member

   type: dict

Output

Important

returns: unique id of member

return type: str

Example

>>> member_details = client.repository.get_member_details(member_id)
>>> member_id = client.repository.get_member_uid(member_details)

get_members_details(space_uid, member_id=None, limit=None)

Get metadata of members associated with a space. If member_uid is not specified, it returns all the members metadata.

Parameters

Important

1. space_uid: Unique id of member (optional)

   type: str

2. limit: limit number of fetched records (optional)

   type: int

Output

Important

returns: metadata of member(s) of a space

return type: dict (if member_id is not None) or {“resources”: [dict]} (if member_id is None)

Note

If member id is not specified, all members metadata is fetched

Example

>>> member_details = client.repository.get_member_details(space_uid,member_id)

get_model_details(model_uid=None, limit=None)