# -----------------------------------------------------------------------------------------
# (C) Copyright IBM Corp. 2017- 2023.
# https://opensource.org/licenses/BSD-3-Clause
# -----------------------------------------------------------------------------------------
from __future__ import print_function
import ibm_watson_machine_learning._wrappers.requests as requests
from ibm_watson_machine_learning.utils import get_url, INSTANCE_DETAILS_TYPE, is_python_2, inherited_docstring
from ibm_watson_machine_learning.metanames import ModelMetaNames, ExperimentMetaNames, FunctionMetaNames, PipelineMetanames, SpacesMetaNames, MemberMetaNames, FunctionNewMetaNames
from ibm_watson_machine_learning.wml_client_error import WMLClientError
from ibm_watson_machine_learning.wml_resource import WMLResource
from ibm_watson_machine_learning.models import Models
from ibm_watson_machine_learning.experiments import Experiments
from ibm_watson_machine_learning.functions import Functions
from ibm_watson_machine_learning.pipelines import Pipelines
from ibm_watson_machine_learning.spaces import Spaces
from multiprocessing import Pool
from ibm_watson_machine_learning.libs.repo.mlrepositoryclient import MLRepositoryClient
from ibm_watson_machine_learning.href_definitions import API_VERSION, SPACES
import os
from warnings import warn
_DEFAULT_LIST_LENGTH = 50
[docs]class Repository(WMLResource):
"""Store and manage models, functions, spaces, pipelines and experiments
using Watson Machine Learning Repository.
To view ModelMetaNames, use:
.. code-block:: python
client.repository.ModelMetaNames.show()
To view ExperimentMetaNames, use:
.. code-block:: python
client.repository.ExperimentMetaNames.show()
To view FunctionMetaNames, use:
.. code-block:: python
client.repository.FunctionMetaNames.show()
To view PipelineMetaNames, use:
.. code-block:: python
client.repository.PipelineMetaNames.show()
"""
cloud_platform_spaces = False
icp_platform_spaces = False
def __init__(self, client):
WMLResource.__init__(self, __name__, client)
if not client.ICP and not client.WSD and not client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES:
Repository._validate_type(client.service_instance.details, u'instance_details', dict, True)
Repository._validate_type_of_details(client.service_instance.details, INSTANCE_DETAILS_TYPE)
self._ICP = client.ICP
self._WSD = client.WSD
self._ml_repository_client = None
Repository.cloud_platform_spaces = client.CLOUD_PLATFORM_SPACES
Repository.icp_platform_spaces = client.ICP_PLATFORM_SPACES
self.ExperimentMetaNames = ExperimentMetaNames()
if not client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES:
self.FunctionMetaNames = FunctionMetaNames()
else:
self.FunctionMetaNames = FunctionNewMetaNames()
self.PipelineMetaNames = PipelineMetanames()
self.SpacesMetaNames = SpacesMetaNames()
self.ModelMetaNames = ModelMetaNames()
self.MemberMetaNames = MemberMetaNames()
self._refresh_repo_client() # regular token is initialized in service_instance
def _refresh_repo_client(self):
# If apiKey is passed in credentials then refresh repoclient with IAM token else MLToken
self._ml_repository_client = MLRepositoryClient(self._wml_credentials[u'url'])
if self._client.proceed is True:
if self._client.service_instance._is_iam() is not None:
self._ml_repository_client.authorize_with_token(self._client.wml_token)
self._ml_repository_client._add_header('X-WML-User-Client', 'PythonClient')
if self._client.project_id is not None:
self._ml_repository_client._add_header('X-Watson-Project-ID', self._client.project_id)
else:
if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES:
platform_spaces = True
else:
platform_spaces = False
self._ml_repository_client.authorize_with_iamtoken(self._client.wml_token,
self._wml_credentials[u'instance_id'],
platform_spaces)
self._ml_repository_client._add_header('X-WML-User-Client', 'PythonClient')
# Cloud Convergence
if not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES:
self._ml_repository_client._add_header('ML-Instance-ID', self._wml_credentials[u'instance_id'])
if self._client.project_id is not None:
self._ml_repository_client._add_header('X-Watson-Project-ID', self._client.project_id)
else:
if self._client._is_IAM():
if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES:
platform_spaces = True
else:
platform_spaces = False
self._ml_repository_client.authorize_with_iamtoken(self._client.wml_token,
self._wml_credentials[u'instance_id'],
platform_spaces)
self._ml_repository_client._add_header('X-WML-User-Client', 'PythonClient')
# Cloud Convergence
if not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES:
self._ml_repository_client._add_header('ML-Instance-ID', self._wml_credentials[u'instance_id'])
if self._client.project_id is not None:
self._ml_repository_client._add_header('X-Watson-Project-ID', self._client.project_id)
else:
if self._ICP:
self._repotoken = self._client._get_icptoken()
self._ml_repository_token = self._repotoken.replace('Bearer', '')
self._ml_repository_client.authorize_with_token(self._ml_repository_token)
else:
if not self._client.WSD:
self._ml_repository_client.authorize(self._wml_credentials[u'username'], self._wml_credentials[u'password'])
self._ml_repository_client._add_header('X-WML-User-Client', 'PythonClient')
if self._client.project_id is not None:
self._ml_repository_client._add_header('X-Watson-Project-ID', self._client.project_id)
[docs] @inherited_docstring(Experiments.store, {'experiments.get_href': 'repository.get_experiment_href'})
def store_experiment(self, meta_props):
if self._client.WSD:
raise WMLClientError(u'Experiment APIs are not supported in Watson Studio Desktop.')
return self._client.experiments.store(meta_props)
[docs] @inherited_docstring(Spaces.store)
def store_space(self, meta_props):
if self._client.WSD or self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES:
raise WMLClientError(u"Not supported in this release. Use methods in 'client.spaces' instead")
return self._client.spaces.store(meta_props)
[docs] @inherited_docstring(Spaces.create_member)
def create_member(self, space_uid, meta_props):
if self._client.WSD or self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES:
raise WMLClientError(u"Not supported in this release. Use methods in 'client.spaces' instead")
return self._client.spaces.create_member(space_uid, meta_props)
@staticmethod
def _meta_props_to_repository_v3_style(meta_props):
if is_python_2():
new_meta_props = meta_props.copy()
for key in new_meta_props:
if type(new_meta_props[key]) is unicode:
new_meta_props[key] = str(new_meta_props[key])
return new_meta_props
else:
return meta_props
[docs] @inherited_docstring(Pipelines.store)
def store_pipeline(self, meta_props):
return self._client.pipelines.store(meta_props)
[docs] @inherited_docstring(Models.store, {'store()': 'store_model()'})
def store_model(self, model, meta_props=None, training_data=None, training_target=None, pipeline=None,
feature_names=None, label_column_names=None,subtrainingId=None, round_number=None,
experiment_metadata=None, training_id=None):
return self._client._models.store(model, meta_props=meta_props, training_data=training_data,
training_target=training_target, pipeline=pipeline,
feature_names=feature_names, label_column_names=label_column_names,
subtrainingId=subtrainingId, round_number=round_number,
experiment_metadata=experiment_metadata, training_id=training_id)
def clone(self, artifact_id, space_id=None, action="copy", rev_id=None):
# """ it is not supported in v4ga
# Create a new resource(models, runtimes, libraries, experiments, functions, pipelines) identical with the model either in the same space or in a new space. All dependent assets will be cloned too.
#
# **Parameters**
#
# .. important::
# #. **model_id**: Guid of the artifact to be cloned:\n
#
# **type**: str\n
#
# #. **space_id**: Guid of the space to which the model needs to be cloned. (optional)
#
# **type**: str\n
#
# #. **action**: Action specifying "copy" or "move". (optional)
#
# **type**: str\n
#
# #. **rev_id**: Revision ID of the artifact. (optional)
#
# **type**: str\n
#
# **Output**
#
# .. important::
#
# **returns**: Metadata of the model cloned.\n
# **return type**: dict\n
#
# **Example**
#
# >>> client.repository.clone(artifact_id=artifact_id,space_id=space_uid,action="copy")
#
# .. note::
# * If revision id is not specified, all revisions of the artifact are cloned\n
#
# * Default value of the parameter action is copy\n
#
# * Space guid is mandatory for move action\n
#
# """
if self._client.WSD or self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES:
raise WMLClientError('Cloning is not supported.')
Models._validate_type(artifact_id, 'artifact_id', str, True)
clone_meta = {}
if space_id is not None:
clone_meta["space"] = {"href": API_VERSION + SPACES + "/" + space_id}
if action is not None:
clone_meta["action"] = action
if rev_id is not None:
clone_meta["rev"] = rev_id
res = self._check_artifact_type(artifact_id)
url = ""
type = ""
if res['model'] is True:
url = self._client.service_instance._href_definitions.get_published_model_href(artifact_id)
type = "model"
elif res['library'] is True:
url = self._client.service_instance._href_definitions.get_custom_library_href(artifact_id)
type = "library"
elif res['runtime'] is True:
url = self._client.service_instance._href_definitions.get_runtime_href(artifact_id)
type = "runtime"
elif res['function'] is True:
url = self._client.service_instance._href_definitions.get_function_href(artifact_id)
type = "function"
elif res['pipeline'] is True:
url = self._client.service_instance._href_definitions.get_pipeline_href(artifact_id)
type = "pipeline"
elif res['experiment'] is True:
url = self._client.service_instance._href_definitions.get_experiment_href(artifact_id)
type = "experiment"
if type == "":
raise WMLClientError('Unsupported artifact type. Supported artifact types are models, libraries, runtimes, experiments, pipelines and functions')
response_post = requests.post(url, json=clone_meta,
headers=self._client._get_headers())
details = self._handle_response(expected_status_code=200, operationName=u'cloning '+ type,
response=response_post)
return details
[docs] @inherited_docstring(Functions.store)
def store_function(self, function, meta_props):
return self._client._functions.store(function, meta_props)
[docs] @inherited_docstring(Models.create_revision)
def create_model_revision(self, model_uid):
return self._client._models.create_revision(model_uid=model_uid)
[docs] @inherited_docstring(Pipelines.create_revision)
def create_pipeline_revision(self, pipeline_uid):
return self._client.pipelines.create_revision(pipeline_uid=pipeline_uid)
[docs] @inherited_docstring(Functions.create_revision)
def create_function_revision(self, function_uid):
return self._client._functions.create_revision(function_uid=function_uid)
[docs] @inherited_docstring(Experiments.create_revision, {'experiment_id': 'experiment_uid'})
def create_experiment_revision(self, experiment_uid):
return self._client.experiments.create_revision(experiment_id=experiment_uid)
[docs] @inherited_docstring(Models.update, {'meta_props': 'updated_meta_props'})
def update_model(self, model_uid, updated_meta_props=None, update_model=None):
return self._client._models.update(model_uid, updated_meta_props, update_model)
[docs] @inherited_docstring(Experiments.update)
def update_experiment(self, experiment_uid, changes):
if self._client.WSD:
raise WMLClientError('Experiments APIs are not supported in IBM Watson Studio Desktop.')
return self._client.experiments.update(experiment_uid, changes)
[docs] @inherited_docstring(Functions.update)
def update_function(self, function_uid, changes, update_function=None):
return self._client._functions.update(function_uid, changes, update_function)
[docs] @inherited_docstring(Pipelines.update)
def update_pipeline(self, pipeline_uid, changes):
return self._client.pipelines.update(pipeline_uid, changes)
[docs] @inherited_docstring(Spaces.update)
def update_space(self, space_uid, changes):
if self._client.WSD or self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES:
raise WMLClientError(u"Not supported in this release. Use methods in 'client.spaces' instead")
return self._client.spaces.update(space_uid, changes)
[docs] @inherited_docstring(Models.load)
def load(self, artifact_uid):
return self._client._models.load(artifact_uid)
[docs] def download(self, artifact_uid, filename='downloaded_artifact.tar.gz', rev_uid=None, format=None):
"""Downloads configuration file for artifact with specified uid.
:param artifact_uid: Unique Id of model, function, runtime or library
:type artifact_uid: str
:param filename: name of the file to which the artifact content has to be downloaded
:type filename: str, optional
:return: path to the downloaded artifact content
:rtype: str
**Examples**
.. code-block:: python
client.repository.download(model_uid, 'my_model.tar.gz')
client.repository.download(model_uid, 'my_model.json') # if original model was saved as json, works only for xgboost 1.3
"""
self._validate_type(artifact_uid, 'artifact_uid', str, True)
self._validate_type(filename, 'filename', str, True)
res = self._check_artifact_type(artifact_uid)
if res['model'] is True:
return self._client._models.download(artifact_uid, filename, rev_uid,format)
elif res['function'] is True:
return self._client._functions.download(artifact_uid, filename, rev_uid)
elif not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_35 and not self._client.ICP_40 \
and not self._client.ICP_45 and not self._client.ICP_46 and not self._client.ICP_47 and res['library'] is True:
return self._client.runtimes.download_library(artifact_uid, filename)
elif not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_35 and not self._client.ICP_40 \
and not self._client.ICP_45 and not self._client.ICP_46 and not self._client.ICP_47 and res['runtime'] is True:
return self._client.runtimes.download_configuration(artifact_uid, filename)
else:
raise WMLClientError('Unexpected type of artifact to download or Artifact with artifact_uid: \'{}\' does not exist.'.format(artifact_uid) )
[docs] def delete(self, artifact_uid):
"""Delete model, experiment, pipeline, space, runtime, library or function from repository.
:param artifact_uid: Unique id of stored model, experiment, function, pipeline, space, library or runtime
:type artifact_uid: str
:return: status ("SUCCESS" or "FAILED")
:rtype: str
**Example**
.. code-block:: python
client.repository.delete(artifact_uid)
"""
Repository._validate_type(artifact_uid, u'artifact_uid', str, True)
if (self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES) and self._if_deployment_exist_for_asset(artifact_uid):
raise WMLClientError(
u'Cannot delete artifact that has existing deployments. Please delete all associated deployments and try again')
params = self._client._params()
if Repository.cloud_platform_spaces or self._client.ICP_PLATFORM_SPACES:
# ideally purge_on_delete=true query param has to be provided for deletion of cams assets
# This doesn't seem to be done for CP4D 3.0.1 and before. We should do this for CP4D 3.5
params.update({'purge_on_delete': 'true'})
response = requests.delete(self._client.service_instance._href_definitions.get_asset_href(artifact_uid),
params=params,
headers=self._client._get_headers())
if response.status_code == 200 or response.status_code == 204:
if response.status_code == 200:
response = self._handle_response(200, u'delete assets', response)
return response
else:
response = self._handle_response(204, u'delete assets', response)
return response
else:
if Repository.cloud_platform_spaces or self._client.ICP_PLATFORM_SPACES:
# Since we are using /v2/assets for deletion, don't need all the logic
# in the following else block. The else block is applicable only for cloud beta
# and has to be kept till then. For 3.5, move logic to same as cloud convergence
# for deletion
if response.status_code == 404:
raise WMLClientError(u'Artifact with artifact_uid: \'{}\' does not exist.'.format(artifact_uid))
else:
raise WMLClientError("Deletion error for the given id : ", response.text)
else:
artifact_type = self._check_artifact_type(artifact_uid)
self._logger.debug(u'Attempting deletion of artifact with type: \'{}\''.format(str(artifact_type)))
if self._client.WSD:
if artifact_type[u'model'] is True:
return self._client._models.delete(artifact_uid)
elif artifact_type[u'pipeline'] is True:
return self._client.pipelines.delete(artifact_uid)
elif artifact_type[u'function'] is True:
return self._client._functions.delete(artifact_uid)
else:
raise WMLClientError(u'Artifact with artifact_uid: \'{}\' does not exist.'.format(artifact_uid))
else:
if artifact_type[u'model'] is True:
return self._client._models.delete(artifact_uid)
elif artifact_type[u'experiment'] is True:
return self._client.experiments.delete(artifact_uid)
elif artifact_type[u'pipeline'] is True:
return self._client.pipelines.delete(artifact_uid)
elif artifact_type[u'function'] is True:
return self._client._functions.delete(artifact_uid)
elif artifact_type[u'space'] is True:
return self._client.spaces.delete(artifact_uid)
elif artifact_type[u'runtime'] is True:
return self._client.runtimes.delete(artifact_uid)
elif artifact_type[u'library'] is True:
return self._client.runtimes.delete_library(artifact_uid)
else:
raise WMLClientError(u'Artifact with artifact_uid: \'{}\' does not exist.'.format(artifact_uid))
[docs] def get_details(self, artifact_uid=None, spec_state=None):
"""Get metadata of stored artifacts. If `artifact_uid` is not specified returns all models, experiments,
functions, pipelines, spaces, libraries and runtimes metadata.
:param artifact_uid: Unique Id of stored model, experiment, function, pipeline, space, library or runtime
:type artifact_uid: str, optional
:param spec_state: software specification state, can be used only when `artifact_uid` is None
:type spec_state: SpecStates, optional
:return: stored artifact(s) metadata
:rtype: dict (if artifact_uid is not None) or {"resources": [dict]} (if artifact_uid is None)
**Examples**
.. code-block:: python
details = client.repository.get_details(artifact_uid)
details = client.repository.get_details()
Example of getting all repository assets with deprecated software specifications:
.. code-block:: python
from ibm_watson_machine_learning.lifecycle import SpecStates
details = client.repository.get_details(spec_state=SpecStates.DEPRECATED)
"""
Repository._validate_type(artifact_uid, u'artifact_uid', str, False)
if artifact_uid is None and self._client.WSD is None:
model_details = self._client._models.get_details(spec_state=spec_state)
experiment_details = self.get_experiment_details() if not spec_state else {'resources': []}
pipeline_details = self.get_pipeline_details() if not spec_state else {'resources': []}
function_details = self._client._functions.get_details(spec_state=spec_state)
if not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES:
space_details = self._client.spaces.get_details() if not spec_state else {'resources': []}
library_details = self._client.runtimes.get_library_details() if not spec_state else {'resources': []}
runtime_details = self._client.runtimes.get_details() if not spec_state else {'resources': []}
details = {
u'models': model_details,
u'experiments': experiment_details,
u'pipeline': pipeline_details,
u'runtimes': runtime_details,
u'libraries': library_details,
u'spaces': space_details,
u'functions': function_details
}
else:
details = {
u'models': model_details,
u'experiments': experiment_details,
u'pipeline': pipeline_details,
u'functions': function_details
}
else:
if self._client.WSD and artifact_uid is None:
raise WMLClientError(
u' artifiact_uid is mandatory for get_details() in IBM Watson Studio Desktop.')
uid_type = self._check_artifact_type(artifact_uid)
if self._client.WSD:
if uid_type[u'model'] is True:
details = self._client._models.get_details(artifact_uid)
elif uid_type[u'pipeline'] is True:
details = self.get_pipeline_details(artifact_uid)
elif uid_type[u'function'] is True:
details = self._client._functions.get_details(artifact_uid)
else:
raise WMLClientError(
u'Getting artifact details failed. Artifact uid: \'{}\' not found.'.format(artifact_uid))
else:
if uid_type[u'model'] is True:
details = self._client._models.get_details(artifact_uid)
elif uid_type[u'experiment'] is True:
details = self.get_experiment_details(artifact_uid)
elif uid_type[u'pipeline'] is True:
details = self.get_pipeline_details(artifact_uid)
elif uid_type[u'function'] is True:
details = self._client._functions.get_details(artifact_uid)
elif not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES and uid_type[u'runtime'] is True:
details = self._client.runtimes.get_details(artifact_uid)
elif not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES and uid_type[u'library'] is True:
details = self._client.runtimes.get_library_details(artifact_uid)
elif not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES and uid_type[u'space'] is True:
details = self._client.spaces.get_details(artifact_uid)
else:
raise WMLClientError(u'Getting artifact details failed. Artifact uid: \'{}\' not found.'.format(artifact_uid))
return details
[docs] @inherited_docstring(Models.get_details)
def get_model_details(self, model_uid=None, limit=None, asynchronous=False, get_all=False, spec_state=None):
return self._client._models.get_details(model_uid, limit,
asynchronous=asynchronous, get_all=get_all, spec_state=spec_state)
[docs] @inherited_docstring(Models.get_revision_details)
def get_model_revision_details(self, model_uid, rev_uid):
if not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES:
raise WMLClientError('Not supported. Revisions APIs are supported only for IBM Cloud Pak® for Data 3.0 and above.')
return self._client._models.get_revision_details(model_uid, rev_uid)
[docs] @inherited_docstring(Experiments.get_details)
def get_experiment_details(self, experiment_uid=None, limit=None, asynchronous=False, get_all=False):
if self._client.WSD:
raise WMLClientError('Experiment APIs are not supported in IBM Watson Studio Desktop.')
Repository._validate_type(experiment_uid, u'experiment_uid', str, False)
Repository._validate_type(limit, u'limit', int, False)
Repository._validate_type(asynchronous, u'asynchronous', bool, False)
Repository._validate_type(get_all, u'get_all', bool, False)
return self._client.experiments.get_details(experiment_uid, limit, asynchronous, get_all)
[docs] @inherited_docstring(Experiments.get_revision_details, {'rev_uid': 'rev_id'})
def get_experiment_revision_details(self, experiment_uid, rev_id):
if not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES:
raise WMLClientError(
'Not supported. Revisions APIs are supported only for IBM Cloud Pak® for Data 3.0 and above.')
return self._client.experiments.get_revision_details(experiment_uid, rev_id)
[docs] @inherited_docstring(Functions.get_details)
def get_function_details(self, function_uid=None, limit=None, asynchronous=False, get_all=False, spec_state=None):
Repository._validate_type(function_uid, u'function_uid', str, False)
Repository._validate_type(limit, u'limit', int, False)
Repository._validate_type(asynchronous, u'asynchronous', bool, False)
Repository._validate_type(get_all, u'get_all', bool, False)
Repository._validate_type(spec_state, u'spec_state', object, False)
return self._client._functions.get_details(function_uid, limit, asynchronous, get_all, spec_state)
[docs] @inherited_docstring(Functions.get_revision_details, {'rev_uid': 'rev_id'})
def get_function_revision_details(self, function_uid, rev_id):
if not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES:
raise WMLClientError('Not supported in this release')
return self._client._functions.get_revision_details(function_uid, rev_id)
[docs] @inherited_docstring(Pipelines.get_details)
def get_pipeline_details(self, pipeline_uid=None, limit=None, asynchronous=False, get_all=False):
Repository._validate_type(pipeline_uid, u'pipeline_uid', str, False)
Repository._validate_type(limit, u'limit', int, False)
Repository._validate_type(asynchronous, u'asynchronous', bool, False)
Repository._validate_type(get_all, u'get_all', bool, False)
return self._client.pipelines.get_details(pipeline_uid, limit, asynchronous, get_all)
[docs] @inherited_docstring(Pipelines.get_revision_details, {'rev_uid': 'rev_id'})
def get_pipeline_revision_details(self, pipeline_uid, rev_id):
if not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES:
raise WMLClientError(
'Not supported. Revisions APIs are supported only for IBM Cloud Pak® for Data 3.0 and above.')
return self._client.pipelines.get_revision_details(pipeline_uid, rev_id)
[docs] @inherited_docstring(Spaces.get_details)
def get_space_details(self, space_uid=None, limit=None):
if self._client.WSD or self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES:
raise WMLClientError(u"Not supported in this release. Use methods in 'client.spaces' instead")
Repository._validate_type(space_uid, u'space_uid', str, False)
Repository._validate_type(limit, u'limit', int, False)
return self._client.spaces.get_details(space_uid, limit)
[docs] @inherited_docstring(Spaces.get_members_details)
def get_members_details(self, space_uid, member_id=None, limit=None):
if self._client.WSD or self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES:
raise WMLClientError(u"Not supported in this release. Use methods in 'client.spaces' instead")
return self._client.spaces.get_members_details(space_uid, member_id, limit)
[docs] @staticmethod
@inherited_docstring(Models.get_href)
def get_model_href(model_details):
return Models.get_href(model_details)
[docs] @staticmethod
def get_model_uid(model_details):
"""
This method is deprecated, please use ``get_id()`` instead."
"""
warn("This method is deprecated, please use get_model_id()")
print("This method is deprecated, please use get_model_id()")
return Models.get_id(model_details)
[docs] @staticmethod
@inherited_docstring(Models.get_id)
def get_model_id(model_details):
return Models.get_id(model_details)
[docs] @staticmethod
@inherited_docstring(Experiments.get_uid, {'experiments.get_details': 'repository.get_experiment_details'})
def get_experiment_uid(experiment_details):
if 'WSD_PLATFORM' in os.environ and os.environ['WSD_PLATFORM'] == 'True':
raise WMLClientError(u'Experiment APIs are not supported for Watson Studio Desktop.')
return Experiments.get_uid(experiment_details)
[docs] @staticmethod
@inherited_docstring(Experiments.get_id, {'experiments.get_details': 'repository.get_experiment_details'})
def get_experiment_id(experiment_details):
if 'WSD_PLATFORM' in os.environ and os.environ['WSD_PLATFORM'] == 'True':
raise WMLClientError(u'Experiment APIs are not supported for Watson Studio Desktop.')
return Experiments.get_id(experiment_details)
[docs] @staticmethod
@inherited_docstring(Experiments.get_href, {'experiments.get_details': 'repository.get_experiment_details'})
def get_experiment_href(experiment_details):
if 'WSD_PLATFORM' in os.environ and os.environ['WSD_PLATFORM'] == 'True':
raise WMLClientError(u'Experiment APIs are not supported for Watson Studio Desktop.')
return Experiments.get_href(experiment_details)
[docs] @staticmethod
@inherited_docstring(Functions.get_id)
def get_function_id(function_details):
return Functions.get_id(function_details)
[docs] @staticmethod
@inherited_docstring(Functions.get_uid)
def get_function_uid(function_details):
return Functions.get_uid(function_details)
[docs] @staticmethod
@inherited_docstring(Pipelines.get_uid)
def get_pipeline_uid(pipeline_details):
return Pipelines.get_uid(pipeline_details)
[docs] @staticmethod
@inherited_docstring(Functions.get_href)
def get_function_href(function_details):
return Functions.get_href(function_details)
[docs] @staticmethod
@inherited_docstring(Pipelines.get_href, {'pipelines.get_details': 'repository.get_pipeline_details'})
def get_pipeline_href(pipeline_details):
return Pipelines.get_href(pipeline_details)
[docs] @staticmethod
@inherited_docstring(Pipelines.get_id)
def get_pipeline_id(pipeline_details):
return Pipelines.get_id(pipeline_details)
[docs] @staticmethod
@inherited_docstring(Spaces.get_uid, {'spaces.get_details': 'repository.get_space_details'})
def get_space_uid(space_details):
if 'WSD_PLATFORM' in os.environ and os.environ['WSD_PLATFORM'] == 'True':
raise WMLClientError(u'Spaces APIs are not supported for Watson Studio Desktop.')
if Repository.cloud_platform_spaces or Repository.icp_platform_spaces:
raise WMLClientError(u"Not supported in this release. Use methods in 'client.spaces' instead")
return Spaces.get_uid(space_details)
[docs] @staticmethod
@inherited_docstring(Spaces.get_member_uid, {'spaces.get_member_details': 'repository.get_member_details'})
def get_member_uid(member_details):
if 'WSD_PLATFORM' in os.environ and os.environ['WSD_PLATFORM'] == 'True':
raise WMLClientError(u'Spaces APIs are not supported for Watson Studio Desktop.')
if Repository.cloud_platform_spaces or Repository.icp_platform_spaces:
raise WMLClientError(u"Not supported in this release. Use methods in 'client.spaces' instead")
return Spaces.get_member_uid(member_details)
[docs] @staticmethod
@inherited_docstring(Spaces.get_href, {'spaces.get_details': 'repository.get_space_details'})
def get_space_href(space_details):
if 'WSD_PLATFORM' in os.environ and os.environ['WSD_PLATFORM'] == 'True':
raise WMLClientError(u'Spaces APIs are not supported for Watson Studio Desktop.')
if Repository.cloud_platform_spaces or Repository.icp_platform_spaces:
raise WMLClientError(u"Not supported in this release. Use methods in 'client.spaces' instead")
return Spaces.get_href(space_details)
[docs] @staticmethod
@inherited_docstring(Spaces.get_member_href, {'spaces.get_member_details': 'repository.get_member_details'})
def get_member_href(member_details):
if 'WSD_PLATFORM' in os.environ and os.environ['WSD_PLATFORM'] == 'True':
raise WMLClientError(u'Spaces APIs are not supported for Watson Studio Desktop.')
if Repository.cloud_platform_spaces or Repository.icp_platform_spaces:
raise WMLClientError(u"Not supported in this release. Use methods in 'client.spaces' instead")
return Spaces.get_member_href(member_details)
[docs] def list(self):
"""Print stored models, pipelines, runtimes, libraries, functions, spaces and experiments in a table format.
If limit is set to None there will be only first 50 records shown.
**Example**
.. code-block:: python
client.repository.list()
"""
from tabulate import tabulate
headers = self._client._get_headers()
params = self._client._params()
params.update({u'limit': 1000})
#params = {u'limit': 1000} # TODO - should be unlimited, if results not sorted
pool = Pool(processes=4)
isIcp = self._ICP
if self._client.WSD:
raise WMLClientError(
u'list() - Listing all artifact is not supported for IBM Watson Studio Desktop. '
u'Use list method of specific artifact.')
if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES:
endpoints = {
u'model': self._client.service_instance._href_definitions.get_published_models_href(),
u'experiment': self._client.service_instance._href_definitions.get_experiments_href(),
u'pipeline': self._client.service_instance._href_definitions.get_pipelines_href(),
u'function': self._client.service_instance._href_definitions.get_functions_href()
}
else:
endpoints = {
u'model': self._client.service_instance._href_definitions.get_published_models_href(),
u'experiment': self._client.service_instance._href_definitions.get_experiments_href(),
u'pipeline': self._client.service_instance._href_definitions.get_pipelines_href(),
u'function': self._client.service_instance._href_definitions.get_functions_href(),
u'runtime': self._client.service_instance._href_definitions.get_runtimes_href(),
u'library': self._client.service_instance._href_definitions.get_custom_libraries_href()
}
artifact_get = {}
for artifact in endpoints:
if (artifact=="library" or artifact=="runtime" or artifact=="space"):
params = None
else:
params = self._client._params()
artifact_get[artifact] = pool.apply_async(get_url,
(endpoints[artifact], self._client._get_headers(), params, isIcp))
# artifact_get = {artifact: pool.apply_async(get_url, (endpoints[artifact], headers, self._client._params(), isIcp)) for
# artifact in endpoints if (artifact != "library" or artifact != "runtime" or artifact != "space")}
# artifact_no_space = {artifact: pool.apply_async(get_url, (endpoints[artifact], headers, None, isIcp)) for artifact
# in endpoints if (artifact == "library" or artifact == "runtime")}
# artifact_get.update(artifact_no_space)
resources = {artifact: [] for artifact in endpoints}
for artifact in endpoints:
try:
response = artifact_get[artifact].get()
response_text = self._handle_response(200, u'getting all {}s'.format(artifact), response)
resources[artifact] = response_text[u'resources']
except Exception as e:
self._logger.error(e)
pool.close()
if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES:
sw_spec_info = {s['id']: s
for s in self._client.software_specifications.get_details(state_info=True)['resources']}
def get_spec_info(spec_id, prop):
if spec_id and spec_id in sw_spec_info:
return sw_spec_info[spec_id].get(prop, '')
else:
return ''
values = []
for t in endpoints.keys():
values += [
(m['metadata']['id'],
m['metadata']['name'],
m['metadata']['created_at'],
m['entity']['type'] if t == 'model' else '-',
t if t != 'function' else m['entity']['type'] + ' function',
get_spec_info(m['entity'].get('software_spec', {}).get('id'), 'state'),
get_spec_info(m['entity'].get('software_spec', {}).get('id'), 'replacement'))
for m in resources[t]]
values = sorted(sorted(list(set(values)), key=lambda x: x[2], reverse=True), key=lambda x: x[4])
table = tabulate([['GUID', 'NAME', 'CREATED', 'FRAMEWORK', 'TYPE', 'SPEC_STATE', 'SPEC_REPLACEMENT']]
+ values[:_DEFAULT_LIST_LENGTH])
else:
model_values = [(m[u'metadata'][u'guid'], m[u'entity'][u'name'], m[u'metadata'][u'created_at'], m[u'entity'][u'type'], u'model') for m in resources[u'model']]
experiment_values = [(m[u'metadata'][u'guid'], m[u'entity'][u'name'],m['metadata']['created_at'], u'-', u'experiment') for m in resources[u'experiment']]
pipeline_values = [(m[u'metadata'][u'guid'], m[u'entity'][u'name'], m[u'metadata'][u'created_at'], u'-', u'pipeline')for m in self._client.pipelines.get_details()[u'resources']]
function_values = [(m[u'metadata'][u'guid'], m[u'entity'][u'name'], m[u'metadata'][u'created_at'], u'-', m[u'entity'][u'type'] + u' function') for m in resources[u'function']]
runtime_values = [(m[u'metadata'][u'guid'], m[u'entity'][u'name'], m[u'metadata'][u'created_at'], u'-', m[u'entity'][u'platform'][u'name'] + u' runtime') for m in resources[u'runtime']]
library_values = [(m[u'metadata'][u'guid'], m[u'entity'][u'name'], m[u'metadata'][u'created_at'], u'-', m[u'entity'][u'platform'][u'name'] + u' library') for m in resources[u'library']]
values = list(set(model_values + experiment_values + pipeline_values + function_values + runtime_values + library_values))
values = sorted(sorted(values, key=lambda x: x[2], reverse=True), key=lambda x: x[4])
table = tabulate([[u'GUID', u'NAME', u'CREATED', u'FRAMEWORK', u'TYPE']] + values[:_DEFAULT_LIST_LENGTH])
print(table)
if len(values) > _DEFAULT_LIST_LENGTH:
print('Note: Only first {} records were displayed. To display more use more specific list functions.'.format(_DEFAULT_LIST_LENGTH))
[docs] @inherited_docstring(Models.list)
def list_models(self, limit=None, asynchronous=False, get_all=False, return_as_df=True):
return self._client._models.list(limit=limit, asynchronous=asynchronous, get_all=get_all, return_as_df=return_as_df)
[docs] @inherited_docstring(Experiments.list)
def list_experiments(self, limit=None, return_as_df=True):
if self._client.WSD:
raise WMLClientError(u'Experiment APIs are not supported for Watson Studio Desktop.')
return self._client.experiments.list(limit=limit, return_as_df=return_as_df)
[docs] @inherited_docstring(Spaces.list)
def list_spaces(self, limit=None, return_as_df=True):
if self._client.WSD:
raise WMLClientError('list_spaces - Listing spaces is not supported for Watson Studio Desktop.')
if Repository.cloud_platform_spaces or Repository.icp_platform_spaces:
raise WMLClientError(u"Not supported in this release. Use methods in 'client.spaces' instead")
return self._client.spaces.list(limit=limit, return_as_df=return_as_df)
[docs] @inherited_docstring(Functions.list)
def list_functions(self, limit=None, return_as_df=True):
return self._client._functions.list(limit=limit, return_as_df=return_as_df)
[docs] @inherited_docstring(Pipelines.list)
def list_pipelines(self, limit=None, return_as_df=True):
return self._client.pipelines.list(limit=limit, return_as_df=return_as_df)
[docs] @inherited_docstring(Spaces.list_members)
def list_members(self, space_uid, limit=None, return_as_df=True):
if self._client.WSD:
raise WMLClientError('list_members - Listing members is not supported for Watson Studio Desktop.')
if Repository.cloud_platform_spaces or Repository.icp_platform_spaces:
raise WMLClientError(u"Not supported in this release. Use methods in 'client.spaces' instead")
return self._client.spaces.list_members(space_uid=space_uid, limit=limit, return_as_df=return_as_df)
def _check_artifact_type(self, artifact_uid):
Repository._validate_type(artifact_uid, u'artifact_uid', str, True)
def _artifact_exists(response):
return (response is not None) and (u'status_code' in dir(response)) and (response.status_code == 200)
pool = Pool(processes=4)
#headers =
isIcp=self._ICP
if self._client.WSD:
endpoint = self._client.service_instance._href_definitions.get_model_definition_assets_href() + "/" + artifact_uid
response = requests.get(
endpoint,
params=self._client._params()
)
# requestsget_url, (endpoint, self._client._get_headers(), self._client._params(), True))
response_get = _artifact_exists(response)
artifact_type = artifact_uid.rsplit(".")[0]
artifact_list = ['wml_model', 'wml_pipeline', 'wml_function']
artifact_type_exists = {artifact.rsplit('_')[-1]: (response_get and artifact == artifact_type) for artifact in artifact_list}
return artifact_type_exists
else:
if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES:
endpoints = {
u'model': self._client.service_instance._href_definitions.get_model_last_version_href(artifact_uid),
u'pipeline': self._client.service_instance._href_definitions.get_pipeline_href(artifact_uid),
u'experiment': self._client.service_instance._href_definitions.get_experiment_href(artifact_uid),
u'function': self._client.service_instance._href_definitions.get_function_href(artifact_uid)
}
else:
endpoints = {
u'model': self._client.service_instance._href_definitions.get_model_last_version_href(artifact_uid),
u'pipeline': self._client.service_instance._href_definitions.get_pipeline_href(artifact_uid),
u'experiment': self._client.service_instance._href_definitions.get_experiment_href(artifact_uid),
u'function': self._client.service_instance._href_definitions.get_function_href(artifact_uid),
u'runtime': self._client.service_instance._href_definitions.get_runtime_href(artifact_uid),
u'library': self._client.service_instance._href_definitions.get_custom_library_href(artifact_uid),
u'space': self._client.service_instance._href_definitions.get_space_href(artifact_uid)
}
future = {}
for artifact in endpoints:
if (artifact=="library" or artifact=="runtime" or artifact=="space"):
params = None
else:
params = self._client._params()
future[artifact] = pool.apply_async(get_url, (endpoints[artifact], self._client._get_headers(), params , isIcp))
# future_no_space = {artifact: pool.apply_async(get_url, (endpoints[artifact], headers, None, isIcp)) for artifact in endpoints if (artifact=="library" or artifact=="runtime" or artifact=="space")}
# future.update(future_no_space)
response_get = {artifact: None for artifact in endpoints}
for artifact in endpoints:
try:
response_get[artifact] = future[artifact].get(timeout=180)
self._logger.debug(u'Response({})[{}]: {}'.format(endpoints[artifact], response_get[artifact].status_code, response_get[artifact].text))
except Exception as e:
self._logger.debug(u'Error during checking artifact type: ' + str(e))
pool.close()
artifact_type = {artifact: _artifact_exists(response_get[artifact]) for artifact in response_get}
return artifact_type
[docs] def create_revision(self, artifact_uid):
"""Create revision for passed `artifact_uid`.
:param artifact_uid: Unique id of stored model, experiment, function or pipelines
:type artifact_uid: str
:return: artifact new revision metadata
:rtype: dict
**Example**
.. code-block:: python
details = client.repository.create_revision(artifact_uid)
"""
Repository._validate_type(artifact_uid, u'artifact_uid', str, True)
uid_type = self._check_artifact_type(artifact_uid)
if uid_type[u'experiment'] is True:
return self._client.experiments.create_revision(artifact_uid)
if uid_type[u'pipeline'] is True:
return self._client.pipelines.create_revision(artifact_uid)
else:
raise WMLClientError(u'Getting artifact details failed. Artifact uid: \'{}\' not found.'.format(artifact_uid))
return details
def _get_revision_details(self, artifact_uid):
"""Get metadata of stored artifacts revisions.
:param artifact_uid: unique id of stored model or experiment or function or pipelines
:type artifact_uid: str
:return: stored artifacts metadata
:rtype: dict
**Example**
.. code-block:: python
details = client.repository.get_revision_details(artifact_uid)
"""
Repository._validate_type(artifact_uid, u'artifact_uid', str, True)
uid_type = self._check_artifact_type(artifact_uid)
if uid_type[u'experiment'] is True:
details = self._client.experiments.get_revision_details(artifact_uid)
if uid_type[u'pipeline'] is True:
details = self._client.pipelines.get_revisions(artifact_uid)
else:
raise WMLClientError(u'Getting artifact details failed. Artifact uid: \'{}\' not found.'.format(artifact_uid))
return details
[docs] @inherited_docstring(Models.list_revisions)
def list_models_revisions(self, model_uid, limit=None, return_as_df=True):
return self._client._models.list_revisions(model_uid, limit=limit, return_as_df=return_as_df)
[docs] @inherited_docstring(Pipelines.list_revisions)
def list_pipelines_revisions(self, pipeline_uid, limit=None, return_as_df=True):
return self._client.pipelines.list_revisions(pipeline_uid, limit=limit, return_as_df=return_as_df)
[docs] @inherited_docstring(Functions.list_revisions)
def list_functions_revisions(self, function_uid, limit=None, return_as_df=True):
return self._client._functions.list_revisions(function_uid, limit=limit, return_as_df=return_as_df)
[docs] @inherited_docstring(Experiments.list_revisions)
def list_experiments_revisions(self, experiment_uid, limit=None, return_as_df=True):
return self._client.experiments.list_revisions(experiment_uid, limit=limit, return_as_df=return_as_df)
