Welcome to watson-machine-learning-client(V4) documentation

watson-machine-learning-client_V4 is a python library that allows to work with Watson Machine Learning service on IBM Cloud and IBM Cloud Pak for Data. Train, store, deploy your models and score them using the APIs and integrate them with your application development.

Installation

The Watson Machine Learning Python Client package is available on pypi. At present it is a beta version. Use this command to install the package:

$pip install -i https://test.pypi.org/simple/ watson-machine-learning-client-V4

Requirements

  • For Watson Machine Learning service on IBM Cloud, you can create a service instance using this link.

  • For Watson Machine Learning services on IBM® Cloud Pak for Data, create a new IBM® Cloud Pak for Data user with administrator privileges so that you can use the credentials for access.

  • Only Python 3.5 or newer is supported.

  • For Jupyter notebook environment be sure to choose the Jupyter with Python 3.5 or later version.

Supported machine learning frameworks

Sample notebooks

Sample notebooks provide examples of various techniques. Sample notebooks can be found here.

You can refer to basic steps for training deep learning model using the Watson Machine Learning Python client from a notebook in this tutorial.

API

To use IBM Watson Machine Learning Python client API, you must authenticate and create an instance of Watson Machine Learning Python client object.

To authenticate and create an instance of the Watson Machine Learning Python client , you must pass your credentials to WatsonMachineLearningAPIClient.

See: Refer to the Terminologies related to authentication.

Authentication for IBM Cloud

  • Using Service Instance Credentials

For IBM Cloud, use Watson Machine Learning service instance credentials for authentication.

See: Looking up credentials .

Look up your Watson Machine Learning service instance credentials in Watson Studio (Refer to Service Credentials from IBM Cloud) and copy your API key, instance id and URL.

from watson_machine_learning_client import WatsonMachineLearningAPIClient

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

client = WatsonMachineLearningAPIClient(wml_credentials)
  • Using IBM Cloud Identity and Access Management (IAM) token

If you are accessing watson-machine-learning-client from a notebook, use this method of authentication to protect your authentication credentials.

Refer to python client example for generating IAM Token. Once you have the token, you can refer to the code below to create an instance of Watson Machine Learning Python client.

from watson_machine_learning_client import WatsonMachineLearningAPIClient

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

client = WatsonMachineLearningAPIClient(wml_credentials)

Get a user access token to connect to the server. This will mask your user credentials.

Authentication for IBM Cloud Pak for Data

See Authentication documentation.

IBM Cloud Pak for Data also requires username and password as part of your credentials to authenticate and create instance of Watson Machine Learning Python client. In that case, your credentials would be:

from watson_machine_learning_client import WatsonMachineLearningAPIClient

wml_credentials = {
                   "url": "<URL>",
                   "username": "<USERNAME>",
                   "password" : "<PASSWORD>",
                   "instance_id": "wml_local",
                   "version" : "2.5.0"
                  }

client = WatsonMachineLearningAPIClient(wml_credentials)

Guidelines for providing credentials to WatsonMachineLearningAPIClient

  • Apikey does not apply and is not used for Cloud Pak for Data.

  • instance_id must be set to :

    • “openshift” (Used when user wants to run against a specific port/when url is a cname)

    • “wml_local” (Used for IBM Cloud Pak for Data version 2.5.0)

  • url must be set to the ip address where Watson Studio Local is located

  • version is provided when Cloud Pak for Data version is 2.0.1 or 2.5.0

NOTE: Setting default space id is mandatory for IBM Cloud Pak for Data.

service_instance - for IBM Cloud only

class client.ServiceInstance(client)[source]

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

get_api_key()[source]

Get api_key of Watson Machine Learning service. :returns: api_key :rtype: str A way you might use this is: >>> instance_details = client.service_instance.get_api_key()

get_details()[source]

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()[source]

Get instance id of your Watson Machine Learning service.

Output

Important

returns: Unique Id of instance.

return type: str

Example

>>> instance_details = client.service_instance.get_instance_id()
get_password()[source]

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()[source]

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()[source]

Get username for your Watson Machine Learning service.

Output

Important

returns: username

return type: str

Example

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

spaces

class client.Spaces(client)[source]

Store and manage your deployment spaces. For details on deployment spaces refer to document about deployment spaces.

MemberMetaNames = <watson_machine_learning_client.metanames.MemberMetaNames object>

MetaNames for spaces creation.

create_member(space_uid, meta_props)[source]

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

  • 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.spaces.create_member(space_uid=space_id, meta_props=metadata)
delete(space_uid)[source]

Delete a stored space.

Parameters

Important

  1. space_uid: Unique Id of space to be deleted.

    type: str

Output

Important

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

return type: str

Example

>>> client.spaces.delete(space_uid)
delete_members(space_uid, member_id)[source]

Delete a member associated with a space.

Parameters

Important

  1. space_uid: Unique Id of space

    type: str

  2. member_uid: Unique Id of member

    type: str

Output

Important

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

return type: str

Example

>>> client.spaces.delete_member(space_uid,member_id)
get_details(space_uid=None, limit=None)[source]

Get metadata of stored space(s). If Unique Id of space is not specified, it returns all the 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 stored space(s)

return type: dict dict (if unique Id of space is not None) or {“resources”: [dict]} (if Unique Id of space is None)

Note

If Unique Id of space is not specified, all spaces metadata is fetched

Example

>>> space_details = client.spaces.get_details(space_uid)
>>> space_details = client.spaces.get_details()
static get_href(spaces_details)[source]

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.spaces.get_details(space_uid)
>>> space_href = client.spaces.get_href(deployment)
static get_member_href(member_details)[source]

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.spaces.get_member_details(member_id)
>>> member_href = client.spaces.get_member_href(member_details)
static get_member_uid(member_details)[source]

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.spaces.get_member_details(member_id)
>>> member_id = client.spaces.get_member_uid(member_details)
get_members_details(space_uid, member_id=None, limit=None)[source]

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 the 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 dict (if Unique ID of space is not None) or {“resources”: [dict]} (if Unique Id of space is None)

Note

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

Example

>>> member_details = client.spaces.get_member_details(space_uid,member_id)
static get_uid(spaces_details)[source]

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.spaces.get_details(space_uid)
>>> space_uid = client.spaces.get_uid(spaces_details)
list(limit=None)[source]

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.spaces.list()
list_members(space_uid, limit=None)[source]

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()
store(meta_props)[source]

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 stored space

metadata contains the Unique Id(UID) of the created space for later reference

return type: dict

Example

>>> metadata = {
>>>  client.spaces.ConfigurationMetaNames.NAME: 'my_space',
>>>  client.spaces.ConfigurationMetaNames.DESCRIPTION: 'spaces',
>>> }
>>> spaces_details = client.spaces.store(training_definition_filepath, meta_props=metadata)
>>> spaces_href = client.spaces.get_href(spaces_details)
update(space_uid, changes)[source]

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.spaces.ConfigurationMetaNames.NAME:"updated_space"
>>> }
>>> space_details = client.spaces.update(space_uid, changes=metadata)
update_member(space_uid, member_id, changes)[source]

Updates existing member metadata.

Parameters

Important

  1. space_uid: Unique Id of space

    type: str

  2. member_id: Unique 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.ConfigurationMetaNames.ROLE:"viewer"
>>> }
>>> member_details = client.spaces.update_member(space_uid, member_id, changes=metadata)
class metanames.SpacesMetaNames[source]

Set of MetaNames for Spaces specifications.

Available MetaNames:

MetaName

Type

Required

Example value

Schema

NAME

str

Y

my_space

TAGS

list

N

[{'value': '<project-guid>', 'description': 'Unique id  of project'}]

[{'value(required)': 'string', 'description(optional)': 'string'}]

CUSTOM

dict

N

{"field1":"value1"}

DESCRIPTION

str

N

my_description

class metanames.MemberMetaNames[source]

Set of MetaNames for Member specifications.

Available MetaNames:

MetaName

Type

Required

Schema

IDENTITY

str

Y

IBMid-060000123A (service-ID or IAM-userID)

ROLE

str

Y

Supported values - Viewer/Editor/Admin

IDENTITY_USER

str

Y

Supported values - service/user

set default space/project id - for IBM Cloud Pak for Data only

class client.Set(client)[source]

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

default_project(project_id)[source]

Set a project ID.

Parameters

Important

  1. project_id: Unique ID(UID) of the project

    type: str

Output

Important

returns: “SUCCESS”

return type: str

Example

>>>  client.set.default_project(project_id)
default_space(space_uid)[source]

Set a space ID.

Parameters

Important

  1. space_uid: Unique Id 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)

repository

class client.Repository(client)[source]

Repository provides set of APIs to store and manage your models, functions, spaces, pipelines and experiments using Watson Machine Learning.

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()
    
  5. To view SpacesMetaNames, use:

    >>> client.repository.SpacesMetaNames.show()
    
  6. To view MemberMetaNames, use:

    >>> client.repository.MemberMetaNames.show()
    
clone(artifact_id, space_id=None, action='copy', rev_id=None)[source]

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 also be cloned.

Parameters

Important

  1. artifact_id: Unique ID of the artifact to be cloned:

    type: str

  2. space_id: Unique ID 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_id,action="copy")

Note

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

  • Default value of the parameter action is copy

  • Unique ID of Space(space_id) is mandatory for move action

create_member(space_uid, meta_props)[source]

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)
delete(artifact_uid)[source]

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')[source]

Downloads configuration file for artifact with specified unique ID.

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 must be downloaded

    default value: downloaded_artifact.tar.gz

    type: str

Output

Important

returns: Path to the downloaded artifact content

return type: str

Example

>>> client.repository.download(model_uid, 'my_model.tar.gz')
get_details(artifact_uid=None)[source]

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

Parameters

Important

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

    type: str

Output

Important

returns: stored artifact(s) metadata

return type: dict

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

Note

If unique ID 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)[source]

Get metadata of experiment. If no experiment Id 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 Unique ID of experiment is not None) or {“resources”: [dict]} (if Unique Id of experiment is None)

Note

If unique ID of experiment is not specified, all experiments metadata is fetched

Example

>>> experiment_details = client.respository.get_experiment_details(experiment_uid)
>>> experiment_details = client.respository.get_experiment_details()
static get_experiment_href(experiment_details)[source]

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_uid(experiment_details)[source]

Get Unique ID(UID) 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)[source]

Get metadata of function. If no function Unique ID of function 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

dict (if Unique ID of function is not None) or {“resources”: [dict]} (if Unique Id of function is None)

Note

If Unique ID of function 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)[source]

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_uid(function_details)[source]

Get Unique ID(uid) of stored function.

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)[source]

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)[source]

Get Unique ID of member (member_uid) from member details.

Parameters

Important

  1. member_details: Metadata of the created member

    type: dict

Output

Important

returns: member Unique ID

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)[source]

Get metadata of members associated with a space. If unique ID of member 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 dict (if Unique ID of member is not None) or {“resources”: [dict]} (if unique 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)[source]

Get metadata of stored model. If unique ID of model 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 dict (if unique ID of model is not None) or {“resources”: [dict]} (if Unique Id of model is None)

Note

If unique ID of the model 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)[source]

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_uid(model_details)[source]

Get unique ID(UID) 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_detailsf(model_uid)
>>> model_uid = client.repository.get_model_uid(model_details)
get_pipeline_details(pipeline_uid=None, limit=None)[source]

Get metadata of stored pipelines. If unique ID of pipeline 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 dict (if Unique ID of Pipeline is not None) or {“resources”: [dict]} (if UID is None)

Note

If unique ID of Pipeline 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)[source]

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_uid(pipeline_details)[source]

Get unique ID of pipeline from pipeline details.

Parameters

Important

  1. pipeline_details: Metadata of the stored pipeline

    type: dict

Output

Important

returns: pipeline Unique ID

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)[source]

Get metadata of stored space. If Unique ID of space (UID) is not specified returns all model spaces metadata.

Parameters

Important

  1. space_uid: Space Unique ID (optional)

    type: str

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

    type: int

Output

Important

returns: metadata of space(s)

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

Note

If Unique Id of the space 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)[source]

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)[source]

Get Unique ID of space (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()[source]

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)[source]

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_functions(limit=None)[source]

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_members(space_uid, limit=None)[source]

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)[source]

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_pipelines(limit=None)[source]

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_spaces(limit=None)[source]

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)[source]

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)[source]

Create IBM Watson Machine Learning experiment. An Experiment is a logical grouping of one or more deep learning experiment training runs.

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

Metadata contains the Unique ID of the experiment for future reference.

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)[source]

Create a function.

Parameters

Important

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

    >>> client.functions.ConfigurationMetaNames.get()
    

    type: dict

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

    • As a ‘function’ can use 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.

This metadata contains the unique ID (UID) of function.

return type: dict

Example

The simplest use is (using score function):

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

Another, more interesting example is using the 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.functions.store(gen_function, name)

In more complicated cases you should create proper metadata, For example:

>>> metadata = {
>>>    client.repository.FunctionMetaNames.NAME: "function",
>>>    client.repository.FunctionMetaNames.DESCRIPTION: "This is ai function",
>>>    client.repository.FunctionMetaNames.RUNTIME_UID: "53dc4cf1-252f-424b-b52d-5cdd9814987f",
>>>    client.repository.FunctionMetaNames.INPUT_DATA_SCHEMAS: [{"fields": [{"metadata": {}, "type": "string", "name": "GENDER", "nullable": True}]}],
>>>    client.repository.FunctionMetaNames.OUTPUT_DATA_SCHEMAS:[{"fields": [{"metadata": {}, "type": "string", "name": "GENDER", "nullable": True}]}],
>>>    client.repository.FunctionMetaNames.TAGS: [{"value": "ProjectA", "description": "Functions created for ProjectA"}]
>>> }
>>> stored_function_details = client.repository.store_function(score, metadata)
store_model(model, meta_props=None, training_data=None, training_target=None, pipeline=None, feature_names=None, label_column_names=None, subtrainingId=None)[source]

Save your model to IBM Watson Machine Learning.

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

  8. subtrainingId: The subtraining ID for a training created via an experiment.

    type: str

Output

Important

returns: Metadata of the model created

Metadata contains the Unique ID(UID) of model

return type: dict

Note

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

  • For deploying a keras model, it is mandatory to pass the FRAMEWORK_LIBRARIES along with other metaprops.

    >>> client.repository.ModelMetaNames.FRAMEWORK_LIBRARIES: [{'name':'keras', 'version': '2.1.3'}]
    
  • 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.

Example

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

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

>>> metadata = {
>>>        client.repository.ModelMetaNames.NAME: 'customer satisfaction prediction model',
>>>        client.repository.ModelMetaNames.TYPE: 'tensorflow_1.5',
>>>        client.repository.ModelMetaNames.RUNTIME_UID: 'tensorflow_1.5-py3'
>>>}

A way you might use this with a 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 this a 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 with Unique ID( uid) of trained model:

>>> stored_model_details = client.repository.store_model(trained_model_uid, meta_props=metadata, training_data=None)
store_pipeline(meta_props)[source]

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 created

Metadata contains the unique ID (UID) of pipeline created

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)[source]

Create an IBM Watson Machine Learning deployment space to deploy models,functions, and manage your deployments. A space is a logical grouping of Watson Machine Learning assets.

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

Metadata contains the unique ID (UID) of 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)

A space is an optional argument when saving or accessing other assets in the repository.

update_experiment(experiment_uid, changes)[source]

Updates existing experiment metadata.

Parameters

Important

  1. experiment_uid: Unique ID of 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)[source]

Updates existing function metadata.

Parameters

Important

  1. function_uid: Unique ID of function which defines what should be updated

    type: str

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

    type: dict

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, changes=None)[source]

Updates existing model metadata.

Parameters

Important

  1. model_uid: Unique ID of model 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 model

return type: dict

Example

>>> metadata = {
>>> client.repository.ModelMetaNames.NAME:"updated_model"
>>> }
>>> model_details = client.repository.update_model(model_uid, changes=metadata)
update_pipeline(pipeline_uid, changes)[source]

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)[source]

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[source]

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

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'}]}

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

[{'value': 'string', 'description': 'string'}]

[{'value(required)': 'string', 'description(optional)': '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

Y

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

class metanames.ExperimentMetaNames[source]

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': 'project-id', 'description': 'Unique ID of Project'}]

[{'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[source]

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

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[source]

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': '<project-uid>', 'description': 'Unique ID of project'}]

[{'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}

class metanames.SpacesMetaNames[source]

Set of MetaNames for Spaces specifications.

Available MetaNames:

MetaName

Type

Required

Example value

Schema

NAME

str

Y

my_space

TAGS

list

N

[{'value': '<project-guid>', 'description': 'Unique id  of project'}]

[{'value(required)': 'string', 'description(optional)': 'string'}]

CUSTOM

dict

N

{"field1":"value1"}

DESCRIPTION

str

N

my_description

deployments

class client.Deployments(client)[source]

Deploy and score published artifacts (models and functions).

create(artifact_uid=None, meta_props=None, **kwargs)[source]

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

Parameters

Important

  1. artifact_uid: Unique Id (UID) of Published artifact (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: {}
>>> }
>>> deployment_details = client.deployments.create(artifact_uid, meta_props)
create_job(deployment_id, meta_props)[source]

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

metadata contains the Unique Id(UID) of the job created for later reference

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)[source]

Delete deployment.

Parameters

Important

  1. deployment uid: Unique Id of deployment to be deleted

    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)[source]

Cancels a deployment job.

Parameters

Important

  1. job_uid: UID of deployment job which should be canceled

    type: str

Output

Important

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

return type: str

Example

>>> client.deployments.delete_job(job_uid)
download(virtual_deployment_uid, filename=None)[source]

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

Parameters
  • virtual_deployment_uid ({str_type}) – Unique Id of virtual deployment

  • filename ({str_type}) – filename of downloaded archive (optional)

Returns

path to downloaded file

Return type

{str_type}

get_details(deployment_uid=None, limit=None)[source]

Get information about your deployment(s). If Unique Id of the Deployment(deployment_uid) is not passed, all deployment details are fetched.

Parameters

Important

  1. deployment_uid: Unique Id of Deployment for which details are requested (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 Unique Id of deployment 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)[source]

Get deployment_download_url from deployment details.

Parameters

deployment_details (dict) – Created deployment details

Returns

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

Return type

{str_type}

A way you might use this is:

>>> deployment_url = client.deployments.get_download_url(deployment)
static get_href(deployment_details)[source]

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)
get_job_details(job_uid=None, limit=None)[source]

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

Parameters

Important

  1. job_uid: Unique Id(UID) of the job(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 UID 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)[source]

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)[source]

Get the status of the deployment job.

Parameters

Important

  1. job_id: Unique Id(UID) 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)[source]

Get the UID of the deployment job.

Parameters

Important

  1. job_details: metadata of the deployment job

    type: dict

Output

Important

returns: UID 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)[source]

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)[source]

Get Unique ID of Deployment(deployment_uid) from deployment details.

Parameters

Important

  1. deployment_details: Metadata of the deployment

    type: dict

Output

Important

returns: Unique Id of deployment that is used to manage the deployment

return type: str

Example

>>> deployment_uid = client.deployments.get_uid(deployment)
list(limit=None)[source]

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)[source]

List the async 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

Example

>>> client.deployments.list_jobs()
score(deployment_id, meta_props)[source]

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_id, changes)[source]

Updates existing deployment metadata.

Parameters

Important

  1. deployment_uid: Unique Id of the 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"
>>> }
>>> deployment_details = client.deployments.update(deployment_uid, changes=metadata)
class metanames.DeploymentMetaNames[source]

Set of MetaNames for Deployments specifications.

Available MetaNames:

MetaName

Type

Required

Example value

Schema

NAME

str

N

my_deployment

TAGS

list

N

[{'value': '<project-guid>', 'description': 'Unique Id of project'}]

[{'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

{}

class metanames.ScoringMetaNames[source]

Set of MetaNames for Scoring.

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

[{'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'}]}}

COMPUTE_METRICS

list

N

['auroc', 'accuracy']

class metanames.DecisionOptimizationMetaNames[source]

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

training

class client.Training(client)[source]

Train new models.

delete(training_uid, hard_delete=False)[source]

Delete a training.

Parameters

Important

  1. training_uid: Unique Id of training

    type: str

Output

Important

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

return type: str

Example

>>> client.training.delete(training_uid)
get_details(training_uid=None, limit=None)[source]

Get metadata of training(s). If unique Id (UID)of training 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 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)[source]

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)
get_metrics(training_uid)[source]

Get metrics.

Parameters

Important

  1. training_uid: Unique Id of training

    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)[source]

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)[source]

Get Unique Id(UID) of training (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)
>>> model_uid = client.training.get_uid(training_details)
list(limit=None)[source]

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)[source]

List the intermediate_models.

Parameters

Important

  1. training_uid: Unique Id of Training

    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

Example

>>> client.training.list_intermediate_models()
list_subtrainings(training_uid)[source]

List the sub-trainings.

Parameters

Important

  1. training_uid: Unique Id of Training

    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)[source]

Monitor the logs of a training created.

Parameters

Important

  1. training_uid: Unique Id of Training

    type: str

Output

Important

returns: None

return type: None

Note

This method prints the training logs.

Example

>>> client.training.monitor_logs(training_uid)
monitor_metrics(training_uid)[source]

Monitor the metrics of a training created.

Parameters

Important

  1. training_uid: Unique Id of Training

    type: str

Output

Output

Important

returns: None

return type: None

Note

This method prints the training metrics.

Example

>>> client.training.monitor_metrics(training_uid)
run(meta_props, asynchronous=True)[source]

Create a 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

Metadata contains the Unique Id(UID) of the training for later reference

return type: dict

Example

>>> 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[source]

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

experiments

class client.Experiments(client)[source]

Run new experiment.

ConfigurationMetaNames = <watson_machine_learning_client.metanames.ExperimentMetaNames object>

MetaNames for experiments creation.

clone(experiment_uid, space_id=None, action='copy', rev_id=None)[source]

Creates a new experiment identical with the given experiment either in the same space or in a new space. All dependent assets will be cloned too.

Parameters

Important

  1. model_id: Unique Id of the experiment to be cloned:

    type: str

  2. space_id: Unique Id of the space to which the experiment needs to be cloned. (optional)

    type: str

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

    type: str

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

    type: str

Output

Important

returns: Metadata of the experiment cloned.

return type: dict

Example
>>> client.experiments.clone(experiment_uid=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

  • Unique Id of Space is mandatory for move action

delete(experiment_uid)[source]

Delete a stored experiment.

Parameters

Important

  1. experiment_uid: Unique ID of the stored experiment

    type: str

Output

Important

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

return type: str

Example

>>> client.experiments.delete(experiment_uid)
get_details(experiment_uid=None, limit=None)[source]

Get metadata of experiment(s). If Unique ID of experiment (experiment_uid) is not 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 UID is not None) or {“resources”: [dict]} (if UID is None)

Note

If Unique ID is not specified, all experiments metadata is fetched

Example

>>> experiment_details = client.experiments.get_details(experiment_uid)
>>> experiment_details = client.experiments.get_details()
static get_href(experiment_details)[source]

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.experiments.get_detailsf(experiment_uid)
>>> experiment_href = client.experiments.get_href(experiment_details)
static get_uid(experiment_details)[source]

Get Unique ID(UID) 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.experiments.get_detailsf(experiment_uid)
>>> experiment_uid = client.experiments.get_uid(experiment_details)
list(limit=None)[source]

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.experiments.list()
store(meta_props)[source]

Create IBM Watson Machine Learning experiment. A deep learning experiment is a logical grouping of one or more deep learning experiment training runs.

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: stored experiment metadata

Metatdata contains the Unique Id (UID) of 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.experiments.store(meta_props=metadata)
>>> experiment_href = client.experiments.get_href(experiment_details)
update(experiment_uid, changes)[source]

Updates existing experiment metadata.

Parameters

Important

  1. experiment_uid: Unique Id of experiment for 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.experiments.ConfigurationMetaNames.NAME:"updated_exp"
>>> }
>>> exp_details = client.experiments.update(experiment_uid, changes=metadata)
class metanames.ExperimentMetaNames[source]

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': 'project-id', 'description': 'Unique ID of Project'}]

[{'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'}

Data assets - for IBM Cloud Pak for Data only

class client.Assets(client)[source]

Store and manage your IBM Watson Machine Learning assets.

create(name, file_path)[source]

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 asset

Metadata contains the unique Id of Asset created for later reference

return type: dict

Example
>>> asset_details = client.assets.create(name="sample_asset",file_path="/path/to/file")
delete(asset_uid)[source]

Delete a stored asset.

Parameters

Important

  1. asset_uid: asset UID

    type: str

Output

Important

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

return type: str

Example

>>> client.assets.delete(asset_uid)
download(asset_uid, filename)[source]

Download an asset.

Parameters

Important

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

    type: str

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

    type: str

Output

returns: Path to the downloaded asset content

return type: str

Example

>>> client.assets.download(asset_uid,"sample_asset.csv")
get_details(asset_uid)[source]

Get asset details.

Parameters

Important

  1. asset_details: Unique Id of stored Asset

    type:str

Output

Important

returns: asset metadata of unique Asset

return type: dict

Example

>>> asset_details = client.assets.get_details(asset_uid)
static get_href(asset_details)[source]

Get url of stored asset.

Parameters

Important

  1. asset_details: stored asset details

    type: dict

Output

Important

returns: href of stored asset

return type: str

Example

>>> asset_details = client.assets.get_details(asset_uid)
>>> asset_href = client.assets.get_href(asset_details)
static get_uid(asset_details)[source]

Get Unique Id of stored asset.

Parameters

Important

  1. asset_details: Metadata of the stored asset

    type: dict

    type: dict

Output

Important

returns: Unique Id of stored asset

return type: str

Example

>>> asset_uid = client.assets.get_uid(asset_details)
list(limit=None)[source]

List stored space 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 assets in a table format.

return type: None

Example

>>> client.assets.list()

model_definitions - for IBM Cloud Pak for Data only

class client.ModelDefinition(client)[source]

Store and manage your model_definitions.

ConfigurationMetaNames = <watson_machine_learning_client.metanames.ModelDefinitionMetaNames object>

MetaNames for model_definition creation.

delete(model_definition_uid)[source]

Delete a stored model_definition.

Parameters

Important

  1. model_definition_uid: Model definition UID

    type: str

Output

Important

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

return type: str

Example

>>> client.model_definitions.delete(model_definition_uid)
get_details(model_definition_uid)[source]

Get metadata of stored model_definition.

Parameters

Important

  1. model_definition_uid: Unique Id(UID) of model_definition

    type: str

Output

Important

returns: metadata of model definition

return type: dict dict (if UID is not None)

Example

>>> model_definition_details = client.model_definitions.get_details(model_definition_uid)
get_href(model_definition_details)[source]

Get href of stored model_definition.

Parameters

model_definition_details (dict) – stored model_definition details

Returns

href of stored model_definition

Return type

{str_type}

A way you might use this is:

>>> model_definition_uid = client.model_definitions.get_href(model_definition_details)
get_uid(model_definition_details)[source]

Get uid of stored model.

Parameters

model_definition_details (dict) – stored model_definition details

Returns

uid of stored model_definition

Return type

{str_type}

A way you might use this is:

>>> model_definition_uid = client.model_definitions.get_uid(model_definition_details)
list(limit=None)[source]

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()
store(model_definition, meta_props)[source]

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

Output

Important

returns: Metadata of the model_defintion created

Metadata contains unique Id of the model_definition creation

return type: dict

Example

Creating a model_definition

class metanames.ModelDefinitionMetaNames[source]

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', 'version(required)': 'version'}

VERSION

str

Y

1.0

SPACE_UID

str

N

3c1ce536-20dc-426e-aac7-7284cf3befc6

pipelines

class client.Pipelines(client)[source]

Store and manage your pipelines.

ConfigurationMetaNames = <watson_machine_learning_client.metanames.PipelineMetanames object>

MetaNames for pipelines creation.

clone(pipeline_uid, space_id=None, action='copy', rev_id=None)[source]

Creates a new pipeline identical with the given pipeline either in the same space or in a new space. All dependent assets will be cloned too.

Parameters

Important

  1. pipeline_uid: Guid of the pipeline to be cloned:

    type: str

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

    type: str

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

    type: str

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

    type: str

Output

Important

returns: Metadata of the pipeline cloned.

return type: dict

Example
>>> client.pipelines.clone(pipeline_uid=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

delete(pipeline_uid)[source]

Delete a stored pipeline.

Parameters

Important

  1. pipeline_uid: Pipeline UID

    type: str

Output

Important

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

return type: str

Example

>>> client.pipelines.delete(deployment_uid)
get_details(pipeline_uid=None, limit=None)[source]

Get metadata of stored pipeline(s). If pipeline UID is not specified returns all pipelines metadata.

Parameters

Important

  1. pipeline_uid: Pipeline UID (optional)

    type: str

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

    type: int

Output

Important

returns: metadata of pipeline(s)

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

Note

If UID is not specified, all pipelines metadata is fetched

Example

>>> pipeline_details = client.pipelines.get_details(pipeline_uid)
>>> pipeline_details = client.pipelines.get_details()
static get_href(pipeline_details)[source]

Get href 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.pipelines.get_details(pipeline_uid)
>>> pipeline_href = client.pipelines.ger_href(pipeline_details)
static get_uid(pipeline_details)[source]

Get pipeline_uid from pipeline details.

Parameters

Important

  1. pipeline_details: Metadata of the stored pipeline

    type: dict

Output

Important

returns: pipeline UID

return type: str

Example

>>> pipeline_details = client.pipelines.get_details(pipeline_uid)
>>> pipeline_uid = client.pipelines.get_uid(deployment)
list(limit=None)[source]

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.pipelines.list()
store(meta_props)[source]

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: stored pipeline metadata

return type: dict

Example

>>> metadata = {
>>>  client.pipelines.ConfigurationMetaNames.NAME: 'my_pipeline',
>>>  client.pipelines.ConfigurationMetaNames.DESCRIPTION: 'sample description'
>>> }
>>> pipeline_details = client.pipelines.store(training_definition_filepath, meta_props=metadata)
>>> pipeline_url = client.pipelines.get_href(pipeline_details)
update(pipeline_uid, changes)[source]

Updates existing pipeline metadata.

Parameters

Important

  1. pipeline_uid: UID 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.pipelines.ConfigurationMetaNames.NAME:"updated_pipeline"
>>> }
>>> pipeline_details = client.pipelines.update(pipeline_uid, changes=metadata)
class metanames.PipelineMetanames[source]

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': '<project-uid>', 'description': 'Unique ID of project'}]

[{'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}

runtimes

class client.Runtimes(client)[source]

Creates runtime specifications and associated custom libraries.

Note

There are a list of pre-defined runtimes available. To see the list of pre-defined runtimes, use:

>>> client.runtimes.list(pre_defined=True)
clone_library(library_uid, space_id=None, action='copy', rev_id=None)[source]

Creates a new function library with the given library either in the same space or in a new space. All dependent assets will be cloned too.

Parameters

Important

  1. library_id: Unique Id(UID) of the library to be cloned:

    type: str

  2. space_id: Unique ID (UID) of the space to which the library needs to be cloned. (optional)

    type: str

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

    type: str

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

    type: str

Output

Important

returns: Metadata of the library cloned.

return type: dict

Example
>>> client.runtmes.clone_library(library_uid=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

clone_runtime(runtime_uid, space_id=None, action='copy', rev_id=None)[source]

Creates a new runtime identical with the given runtime either in the same space or in a new space. All dependent assets will be cloned too.

Parameters

Important

  1. runtime_id: Unique ID of the runtime to be cloned:

    type: str

  2. space_id: Unique Id (UID) of the space to which the runtime needs to be cloned. (optional)

    type: str

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

    type: str

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

    type: str

Output

Important

returns: Metadata of the runtime cloned.

return type: dict

Example
>>> client.runtimes.clone_runtime(runtime_uid=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

  • Unique Id of Space is required for move action

delete(runtime_uid, with_libraries=False)[source]

Delete a runtime.

Parameters

Important

  1. runtime_uid: Unique Id of Runtime

    type: str

  2. with_libraries: Boolean value indicating an option to delete the libraries associated with the runtime

    type: bool

Output

Important

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

return type: str

Example

>>> client.runtimes.delete(deployment_uid)
delete_library(library_uid)[source]

Delete a library.

Parameters

Important

  1. library_uid: Unique Id of Library

    type: str

Output

Important

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

return type: str

Example

>>> client.runtimes.delete_library(library_uid)
download_configuration(runtime_uid, filename=None)[source]

Downloads configuration file for runtime with specified uid.

Parameters

Important

  1. runtime_uid: Unique Id(UID) of runtime

    type: str

  2. filename: filename of downloaded archive (optional)

    default value: runtime_configuration.yaml

    type: str

Output

Important

returns: Path to the downloaded runtime configuration

return type: str

Note

If filename is not specified, the default filename is “runtime_configuration.yaml”.

Example

>>> filename="runtime.yml"
>>> client.runtimes.download_configuration(runtime_uid, filename=filename)
download_library(library_uid, filename=None)[source]

Downloads library content with specified uid.

Parameters

Important

  1. library_uid: Unique ID(UID) of library

    type: str

  2. filename: filename of downloaded archive (optional)

    default value: <LIBRARY-NAME>-<LIBRARY-VERSION>.zip

    type: str

Output

Important

returns: Path to the downloaded library content

return type: str

Note

If filename is not specified, the default filename is “<LIBRARY-NAME>-<LIBRARY-VERSION>.zip”.

Example

>>> filename="library.tgz"
>>> client.runtimes.download_library(runtime_uid, filename=filename)
get_details(runtime_uid=None, pre_defined=False, limit=None)[source]

Get metadata of stored runtime(s). If runtime ID is not specified returns all runtimes metadata.

Parameters

Important

  1. runtime_uid: Unique Id(UID) of runtime (optional)

    type: str

  2. pre_defined: Boolean indicating to display predefined runtimes only. Default value is set to ‘False’

    type: bool

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

    type: int

Output

Important

returns: metadata of runtime(s)

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

Note

If UID is not specified, all runtimes metadata is fetched

Example

>>> runtime_details = client.runtimes.get_details(runtime_uid)
>>> runtime_details = client.runtimes.get_details(runtime_uid=runtime_uid)
>>> runtime_details = client.runtimes.get_details()
static get_href(details)[source]

Get runtime_href from runtime details.

Parameters

Important

  1. runtime_details: Metadata of the runtime

    type: dict

Output

Important

returns: runtime href

return type: str

Example

>>> runtime_details = client.runtimes.get_details(runtime_uid)
>>> runtime_href = client.runtimes.get_href(runtime_details)
get_library_details(library_uid=None, limit=None)[source]

Get metadata of stored libraries. If library UID is not specified returns all libraries metadata.

Parameters

Important

  1. library_uid: library UID (optional)

    type: str

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

    type: int

Output

Important

returns: metadata of library(s)

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

Note

If UID is not specified, all libraries metadata is fetched

Example

>>> library_details = client.runtimes.get_library_details(library_uid)
>>> library_details = client.runtimes.get_library_details(library_uid=library_uid)
>>> library_details = client.runtimes.get_library_details()
static get_library_href(library_details)[source]

Get library_href from library details.

Parameters

Important

  1. library_details: Metadata of the library

    type: dict

Output

Important

returns: library href

return type: str

Example

>>> library_details = client.runtimes.get_library_details(library_uid)
>>> library_url = client.runtimes.get_library_href(library_details)
static get_library_uid(library_details)[source]

Get library_uid from library details.

Parameters

Important

  1. library_details: Metadata of the library

    type: dict

Output

Important

returns: Unique Id of library

return type: str

Example

>>> library_details = client.runtimes.get_library_details(library_uid)
>>> library_uid = client.runtimes.get_library_uid(library_details)
static get_uid(details)[source]

Get runtime_uid from runtime details.

Parameters

Important

  1. runtime_details: Metadata of the runtime

    type: dict

Output

Important

returns: Unique Id of runtime

return type: str

Example

>>> runtime_details = client.runtimes.get_details(runtime_uid)
>>> runtime_uid = client.runtimes.get_uid(runtime_details)
list(limit=None, pre_defined=False)[source]

List stored runtimes. 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. pre_defined: Boolean indicating to display predefined runtimes only. Default value is set to ‘False’

    type: bool

Output

Important

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

return type: None

Example

>>> client.runtimes.list()
>>> client.runtimes.list(pre_defined=True)
list_libraries(runtime_uid=None, limit=None)[source]

List stored libraries. If runtime UID is not provided, all libraries are listed else, libraries associated with a runtime are listed. If limit is set to None there will be only first 50 records shown.

Parameters

Important

  1. runtime_uid: Unique Id of runtime (optional)

    type: str

  2. limit: limit number of fetched records

    type: int

Output

Important

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

return type: None

Example

>>> client.runtimes.list_libraries()
>>> client.runtimes.list_libraries(runtime_uid)
store(meta_props)[source]

Create a runtime.

Parameters

Important

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

    >>> client.runtimes.ConfigurationMetaNames.get()
    

    type: dict

Output

Important

returns: Metadata of the runtime created

return type: dict

Example

Creating a library

>>> lib_meta = {
>>> client.runtimes.LibraryMetaNames.NAME: "libraries_custom",
>>> client.runtimes.LibraryMetaNames.DESCRIPTION: "custom libraries for scoring",
>>> client.runtimes.LibraryMetaNames.FILEPATH: "/home/user/my_lib.zip",
>>> client.runtimes.LibraryMetaNames.VERSION: "1.0",
>>> client.runtimes.LibraryMetaNames.PLATFORM: {"name": "python", "versions": ["3.5"]}
>>> }
>>> custom_library_details = client.runtimes.store_library(lib_meta)
>>> custom_library_uid = client.runtimes.get_library_uid(custom_library_details)

Creating a runtime

>>> runtime_meta = {
>>> client.runtimes.ConfigurationMetaNames.NAME: "runtime_spec_python_3.5",
>>> client.runtimes.ConfigurationMetaNames.DESCRIPTION: "test",
>>> client.runtimes.ConfigurationMetaNames.PLATFORM: {
>>> "name": "python",
>>>  "version": "3.5"
>>> },
>>> client.runtimes.ConfigurationMetaNames.LIBRARIES_UIDS: [custom_library_uid] # already existing lib is linked here
>>> }
>>> runtime_details = client.runtimes.store(meta)
store_library(meta_props)[source]

Create a library.

Parameters

Important

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

    >>> client.runtimes.LibraryMetaNames.get()
    

    type: dict

Output

Important

returns: Metadata of the library created.

return type: dict

Example

>>> library_details = client.runtimes.store_library({
>>> client.runtimes.LibraryMetaNames.NAME: "libraries_custom",
>>> client.runtimes.LibraryMetaNames.DESCRIPTION: "custom libraries for scoring",
>>> client.runtimes.LibraryMetaNames.FILEPATH: custom_library_path,
>>> client.runtimes.LibraryMetaNames.VERSION: "1.0",
>>> client.runtimes.LibraryMetaNames.PLATFORM: {"name": "python", "versions": ["3.5"]}
>>> })
update_library(library_uid, changes)[source]

Updates existing library metadata.

Parameters

Important

  1. library_uid: Unique ID(UID) of library 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 library

return type: dict

Example

>>> metadata = {
>>> client.runtimes.LibraryMetaNames.NAME:"updated_lib"
>>> }
>>> library_details = client.runtimes.update_library(library_uid, changes=metadata)
update_runtime(runtime_uid, changes)[source]

Updates existing runtime metadata.

Parameters

Important

  1. runtime_uid: Unique ID(UID) of runtime 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 runtime

return type: dict

Example

>>> metadata = {
>>> client.runtimes.ConfigurationMetaNames.NAME:"updated_runtime"
>>> }
>>> runtime_details = client.runtimes.update(runtime_uid, changes=metadata)
class metanames.RuntimeMetaNames[source]

Set of MetaNames for Runtime specifications.

Available MetaNames:

MetaName

Type

Required

Example value

Schema

NAME

str

Y

runtime_spec_python_3.5

DESCRIPTION

str

N

sample runtime

PLATFORM

dict

Y

{"name":python","version":"3.5")

{'name(required)': 'string', 'version(required)': 'version'}

LIBRARIES_UIDS

list

N

['46dc9cf1-252f-424b-b52d-5cdd9814987f']

CONFIGURATION_FILEPATH

str

N

/home/env_config.yaml

TAGS

list

N

[{'value': '<project-guid>', 'description': 'Uique Id of project'}]

[{'value(required)': 'string', 'description(optional)': 'string'}]

CUSTOM

dict

N

{"field1": "value1"}

SPACE_UID

str

N

46dc9cf1-252f-424b-b52d-5cdd9814987f

COMPUTE

dict

N

{'name': 'name1', 'nodes': 1}

{'name(required)': 'string', 'nodes(optional)': 'string'}

class metanames.LibraryMetaNames[source]

Set of MetaNames for Custom Libraries.

Available MetaNames:

MetaName

Type

Required

Example value

Schema

NAME

str

Y

my_lib

DESCRIPTION

str

N

my lib

PLATFORM

dict

Y

{'name': 'python', 'versions': ['3.5']}

{'name(required)': 'string', 'version(required)': 'version'}

VERSION

str

Y

1.0

FILEPATH

str

Y

/home/user/my_lib_1_0.zip

TAGS

dict

N

[{'value': '<project-guid>', 'description': 'Unique ID of project'}]

[{'value(required)': 'string', 'description(optional)': 'string'}]

SPACE_UID

str

N

3c1ce536-20dc-426e-aac7-7284cf3befc6

MODEL_DEFINITION

bool

N

False

COMMAND

str

N

command

CUSTOM

dict

N

{'field1': 'value1'}

Changelog

Latest

  • Added support for async scoring

v1.0.10

  • Improved documentation

  • V4 support

  • Added feature_names and label_column_names for Scikit/XGBoost model creation.