# (C) Copyright IBM Corp. 2020.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import time
from typing import TYPE_CHECKING, Any, Dict, Union, List, Optional
from pandas import DataFrame, concat
from .base_deployment import BaseDeployment
from ..helpers import DataConnection
from ..utils import StatusLogger, print_text_header_h1
from ..utils.autoai.connection import validate_source_data_connections, validate_deployment_output_connection
from ..utils.autoai.utils import init_cos_client, try_load_dataset
from ..utils.autoai.errors import NoneDataConnection
from ..utils.deployment.errors import BatchJobFailed, MissingScoringResults
from ..wml_client_error import WMLClientError
from ibm_watson_machine_learning.utils.autoai.enums import DataConnectionTypes
if TYPE_CHECKING:
from sklearn.pipeline import Pipeline
from pandas import DataFrame
from numpy import ndarray
from ..workspace import WorkSpace
__all__ = [
"Batch"
]
[docs]class Batch(BaseDeployment):
"""
The Batch Deployment class.
With this class object you can manage any batch deployment.
Parameters
----------
source_wml_credentials: dictionary, required
Credentials to Watson Machine Learning instance where training was performed.
source_project_id: str, optional
ID of the Watson Studio project where training was performed.
source_space_id: str, optional
ID of the Watson Studio Space where training was performed.
target_wml_credentials: dictionary, required
Credentials to Watson Machine Learning instance where you want to deploy.
target_project_id: str, optional
ID of the Watson Studio project where you want to deploy.
target_space_id: str, optional
ID of the Watson Studio Space where you want to deploy.
"""
def __init__(self,
source_wml_credentials: Union[dict, 'WorkSpace'] = None,
source_project_id: str = None,
source_space_id: str = None,
target_wml_credentials: Union[dict, 'WorkSpace'] = None,
target_project_id: str = None,
target_space_id: str = None,
wml_credentials: Union[dict, 'WorkSpace'] = None,
project_id: str = None,
space_id: str = None):
super().__init__(
deployment_type='batch',
source_wml_credentials=source_wml_credentials,
source_project_id=source_project_id,
source_space_id=source_space_id,
target_wml_credentials=target_wml_credentials,
target_project_id=target_project_id,
target_space_id=target_space_id,
wml_credentials=wml_credentials,
project_id=project_id,
space_id=space_id
)
self.name = None
self.id = None
self.asset_id = None
def __repr__(self):
return f"name: {self.name}, id: {self.id}, asset_id: {self.asset_id}"
def __str__(self):
return f"name: {self.name}, id: {self.id}, asset_id: {self.asset_id}"
[docs] def score(self, **kwargs):
raise NotImplementedError("Batch deployment supports only job runs.")
[docs] def create(self,
model: str,
deployment_name: str,
metadata: Optional[Dict] = None,
training_data: Optional[Union['DataFrame', 'ndarray']] = None,
training_target: Optional[Union['DataFrame', 'ndarray']] = None,
experiment_run_id: Optional[str] = None) -> None:
"""
Create deployment from a model.
Parameters
----------
model: str, required
AutoAI model name.
deployment_name: str, required
Name of the deployment
training_data: Union['pandas.DataFrame', 'numpy.ndarray'], optional
Training data for the model
training_target: Union['pandas.DataFrame', 'numpy.ndarray'], optional
Target/label data for the model
metadata: dictionary, optional
Model meta properties.
experiment_run_id: str, optional
ID of a training/experiment (only applicable for AutoAI deployments)
Example
-------
>>> from ibm_watson_machine_learning.deployment import Batch
>>>
>>> deployment = Batch(
>>> wml_credentials={
>>> "apikey": "...",
>>> "iam_apikey_description": "...",
>>> "iam_apikey_name": "...",
>>> "iam_role_crn": "...",
>>> "iam_serviceid_crn": "...",
>>> "instance_id": "...",
>>> "url": "https://us-south.ml.cloud.ibm.com"
>>> },
>>> project_id="...",
>>> space_id="...")
>>>
>>> deployment.create(
>>> experiment_run_id="...",
>>> model=model,
>>> deployment_name='My new deployment'
>>> )
"""
return super().create(model=model,
deployment_name=deployment_name,
metadata=metadata,
training_data=training_data,
training_target=training_target,
experiment_run_id=experiment_run_id,
deployment_type='batch')
[docs] @BaseDeployment._project_to_space_to_project
def get_params(self) -> Dict:
"""Get deployment parameters."""
return super().get_params()
[docs] @BaseDeployment._project_to_space_to_project
def run_job(self,
payload: Union[DataFrame, List[DataConnection]],
output_data_reference: 'DataConnection' = None,
transaction_id: str = None,
background_mode: 'bool' = True) -> Union[Dict, Dict[str, List], DataConnection]:
"""
Batch scoring job on WML. Payload or Payload data reference is required. It is passed to the WML where model have been deployed.
Parameters
----------
payload: pandas.DataFrame or List[DataConnection], required
DataFrame with data to test the model or data storage connection details to inform where payload data is stored.
output_data_reference: DataConnection, optional
DataConnection to the output COS for storing predictions.
Required nly when DataConnections are as a payload.
transaction_id: str, optional
Can be used to indicate under which id the records will be saved into payload table in IBM OpenScale.
background_mode: bool, optional
Indicator if score() method will run in background (async) or (sync).
Returns
-------
Dictionary with scoring job details.
Example
-------
>>> score_details = deployment.score(payload=test_data)
>>> print(score_details['entity']['scoring'])
{'input_data': [{'fields': ['sepal_length',
'sepal_width',
'petal_length',
'petal_width'],
'values': [[4.9, 3.0, 1.4, 0.2]]}],
'predictions': [{'fields': ['prediction', 'probability'],
'values': [['setosa',
[0.9999320742502246,
5.1519823540224506e-05,
1.6405926235405522e-05]]]}]
>>>
>>> payload_reference = DataConnection(location=DSLocation(asset_id=asset_id))
>>> score_details = deployment.score(payload=payload_reference, output_data_filename = "scoring_output.csv")
"""
# note: support for DataFrame payload
if isinstance(payload, DataFrame):
scoring_payload = {
self._target_workspace.wml_client.deployments.ScoringMetaNames.INPUT_DATA: [{'values': payload}]
}
# note: support for DataConnections and dictionaries payload
elif isinstance(payload, list):
if isinstance(payload[0], DataConnection):
if None in payload:
raise NoneDataConnection('payload')
payload = [new_conn for conn in payload for new_conn in conn._subdivide_connection()]
payload = validate_source_data_connections(source_data_connections=payload,
workspace=self._target_workspace,
deployment=True)
payload = [data_connection._to_dict() for data_connection in payload]
elif isinstance(payload[0], dict):
pass
else:
raise ValueError(f"Current payload type: list of {type(payload[0])} is not supported.")
if output_data_reference is None:
raise ValueError("\"output_data_reference\" should be provided.")
if isinstance(output_data_reference, DataConnection):
output_data_reference = validate_deployment_output_connection(
results_data_connection=output_data_reference,
workspace=self._target_workspace,
source_data_connections=payload)
output_data_reference = output_data_reference._to_dict()
scoring_payload = {
self._target_workspace.wml_client.deployments.ScoringMetaNames.INPUT_DATA_REFERENCES: payload,
self._target_workspace.wml_client.deployments.ScoringMetaNames.OUTPUT_DATA_REFERENCE:
output_data_reference}
else:
raise ValueError(
f"Incorrect payload type. Required: DataFrame or List[DataConnection], Passed: {type(payload)}")
scoring_payload['hybrid_pipeline_hardware_specs'] = [
{
'node_runtime_id': 'auto_ai.kb',
'hardware_spec': {
'name': 'M'
}
}
]
if self._obm:
scoring_payload['hybrid_pipeline_hardware_specs'].insert(
0,
{
"node_runtime_id": "auto_ai.obm",
"hardware_spec": {
"name": "M-Spark",
"num_nodes": 2
}
}
)
job_details = self._target_workspace.wml_client.deployments.create_job(self.id, scoring_payload)
if background_mode:
return job_details
else:
# note: monitor scoring job
job_id = self._target_workspace.wml_client.deployments.get_job_uid(job_details)
print_text_header_h1(u'Synchronous scoring for id: \'{}\' started'.format(job_id))
status = self.get_job_status(job_id)['state']
with StatusLogger(status) as status_logger:
while status not in ['failed', 'error', 'completed', 'canceled']:
time.sleep(10)
status = self.get_job_status(job_id)['state']
status_logger.log_state(status)
# --- end note
if u'completed' in status:
print(u'\nScoring job \'{}\' finished successfully.'.format(job_id))
else:
raise BatchJobFailed(job_id, f"Scoring job failed with status: {self.get_job_status(job_id)}")
return self.get_job_params(job_id)
[docs] @BaseDeployment._project_to_space_to_project
def rerun_job(self,
scoring_job_id: str,
background_mode: bool = True) -> Union[dict, 'DataFrame', 'DataConnection']:
"""
Rerun scoring job with the same parameters as job described by 'scoring_job_id'.
Parameters
----------
scoring_job_id: str
Id described scoring job.
background_mode: bool, optional
Indicator if score_rerun() method will run in background (async) or (sync).
Returns
-------
Dictionary with scoring job details.
Example
-------
>>> scoring_details = deployment.score_rerun(scoring_job_id)
"""
scoring_params = self.get_job_params(scoring_job_id)['entity']['scoring']
input_data_references = self._target_workspace.wml_client.deployments.ScoringMetaNames.INPUT_DATA_REFERENCES
output_data_reference = self._target_workspace.wml_client.deployments.ScoringMetaNames.OUTPUT_DATA_REFERENCE
if input_data_references in scoring_params:
payload_ref = [input_ref for input_ref in scoring_params[input_data_references]]
if 'href' in scoring_params[output_data_reference]['location']:
del scoring_params[output_data_reference]['location']['href']
return self.run_job(payload=payload_ref, output_data_reference=scoring_params['output_data_reference'],
background_mode=background_mode)
else:
raise NotImplementedError("'rerun_job' method supports only jobs with "
"payload passed as a list of DataConnections. If you want to rerun job "
"with payload passed directly, please use 'run_job' one more time.")
[docs] @BaseDeployment._project_to_space_to_project
def delete(self, deployment_id: str = None) -> None:
"""
Delete deployment on WML.
Parameters
----------
deployment_id: str, optional
ID of the deployment to delete. If empty, current deployment will be deleted.
Example
-------
>>> deployment = Batch(workspace=...)
>>> # Delete current deployment
>>> deployment.delete()
>>> # Or delete a specific deployment
>>> deployment.delete(deployment_id='...')
"""
super().delete(deployment_id=deployment_id, deployment_type='batch')
[docs] @BaseDeployment._project_to_space_to_project
def list(self, limit=None) -> 'DataFrame':
"""
List WML deployments.
Parameters
----------
limit: int, optional
Set the limit of how many deployments to list. Default is None (all deployments should be fetched)
Returns
-------
Pandas DataFrame with information about deployments.
Example
-------
>>> deployment = Batch(workspace=...)
>>> deployments_list = deployment.list()
>>> print(deployments_list)
created_at ... status
0 2020-03-06T10:50:49.401Z ... ready
1 2020-03-06T13:16:09.789Z ... ready
4 2020-03-11T14:46:36.035Z ... failed
3 2020-03-11T14:49:55.052Z ... failed
2 2020-03-11T15:13:53.708Z ... ready
"""
return super().list(limit=limit, deployment_type='batch')
[docs] @BaseDeployment._project_to_space_to_project
def get(self, deployment_id: str) -> None:
"""
Get WML deployment.
Parameters
----------
deployment_id: str, required
ID of the deployment to work with.
Returns
-------
WebService deployment object
Example
-------
>>> deployment = Batch(workspace=...)
>>> deployment.get(deployment_id="...")
"""
super().get(deployment_id=deployment_id, deployment_type='batch')
[docs] @BaseDeployment._project_to_space_to_project
def get_job_params(self, scoring_job_id: str = None) -> Dict:
"""Get batch deployment job parameters.
Parameters
----------
scoring_job_id: str
Id of scoring job.
Returns
-------
Dictionary with parameters of the scoring job.
"""
return self._target_workspace.wml_client.deployments.get_job_details(scoring_job_id)
[docs] @BaseDeployment._project_to_space_to_project
def get_job_status(self, scoring_job_id: str) -> Dict:
"""Get status of scoring job.
Parameters
----------
scoring_job_id: str
Id of scoring job.
Returns
-------
Dictionary with state of scoring job (one of: [completed, failed, starting, queued])
and additional details if they exist.
"""
return self._target_workspace.wml_client.deployments.get_job_status(scoring_job_id)
[docs] @BaseDeployment._project_to_space_to_project
def get_job_result(self, scoring_job_id: str) -> 'DataFrame':
"""Get batch deployment results of job with id `scoring_job_id`.
Parameters
----------
scoring_job_id: str
Id of scoring job which results will be returned.
Returns
-------
DataFrame with result.
In case of incompleted or failed `MissingScoringResults` scoring exception is raised.
"""
scoring_params = self.get_job_params(scoring_job_id)['entity']['scoring']
if scoring_params['status']['state'] == 'completed':
if 'predictions' in scoring_params:
data = DataFrame(scoring_params['predictions'][0]['values'], columns=scoring_params['predictions'][0]['fields'])
return data
else:
conn = DataConnection._from_dict(scoring_params['output_data_reference'])
conn._wml_client = self._target_workspace.wml_client
if len(scoring_params['input_data_references']) > 1 and not self._target_workspace.wml_client.ICP:
conn._obm = True
# TODO: remove S3 implementation
if conn.type == DataConnectionTypes.S3:
conn._obm_cos_path = scoring_params['output_data_reference']['location']['path']
elif conn.type == DataConnectionTypes.CA and conn._check_if_connection_asset_is_s3():
conn._obm_cos_path = scoring_params['output_data_reference']['location']['path']
return conn.read() # if in future output may be excel file or with custom separator, here it should be recognized
else:
raise MissingScoringResults(scoring_job_id, reason="Scoring is not completed.")
[docs] @BaseDeployment._project_to_space_to_project
def get_job_id(self, batch_scoring_details):
"""Get id from batch scoring details."""
return self._target_workspace.wml_client.deployments.get_job_uid(batch_scoring_details)
[docs] @BaseDeployment._project_to_space_to_project
def list_jobs(self):
"""Returns pandas DataFrame with list of deployment jobs"""
resources = self._target_workspace.wml_client.deployments.get_job_details()['resources']
columns = [u'job id', u'state', u'creted', u'deployment id']
values = []
for scoring_details in resources:
if 'scoring' in scoring_details['entity']:
state = scoring_details['entity']['scoring']['status']['state']
score_values = (scoring_details[u'metadata'][u'id'], state,
scoring_details[u'metadata'][u'created_at'],
scoring_details['entity']['deployment']['id'])
if self.id:
if self.id == scoring_details['entity']['deployment']['id']:
values.append(score_values)
else:
values.append(score_values)
return DataFrame(values, columns=columns)
@BaseDeployment._project_to_space_to_project
def _deploy(self,
pipeline_model: 'Pipeline',
deployment_name: str,
meta_props: Dict,
result_client=None) -> Dict:
"""
Deploy model into WML.
Parameters
----------
pipeline_model: Union['Pipeline', str], required
Model of the pipeline to deploy
deployment_name: str, required
Name of the deployment
meta_props: dictionary, required
Model meta properties.
result_client: Tuple['DataConnection', 'resource'] required
Tuple with Result DataConnection object and initialized COS client.
"""
deployment_details = {}
asset_uid = self._publish_model(pipeline_model=pipeline_model,
meta_props=meta_props)
self.asset_id = asset_uid
deployment_props = {
self._target_workspace.wml_client.deployments.ConfigurationMetaNames.NAME: deployment_name,
self._target_workspace.wml_client.deployments.ConfigurationMetaNames.BATCH: {}
}
deployment_props[self._target_workspace.wml_client.deployments.ConfigurationMetaNames.ASSET] = {
"id": asset_uid
}
deployment_props[
self._target_workspace.wml_client.deployments.ConfigurationMetaNames.HYBRID_PIPELINE_HARDWARE_SPECS] = [
{
'node_runtime_id': 'autoai.kb',
'hardware_spec': {
'name': 'M'
}
}
]
if self._obm:
deployment_props[
self._target_workspace.wml_client.deployments.ConfigurationMetaNames.HYBRID_PIPELINE_HARDWARE_SPECS
].insert(0, {
"node_runtime_id": "auto_ai.obm",
"hardware_spec": {
"name": "M-Spark",
"num_nodes": 2
}
})
print("Deploying model {} using V4 client.".format(asset_uid))
try:
deployment_details = self._target_workspace.wml_client.deployments.create(
artifact_uid=asset_uid,
meta_props=deployment_props)
self.deployment_id = self._target_workspace.wml_client.deployments.get_uid(deployment_details)
except WMLClientError as e:
raise e
return deployment_details
