Machine Learning artifacts management.ipynb

Kernel: Python 3

Machine Learning artifacts management

This notebook contains steps and code to demonstrate how to manage and clean up Watson Machine Learning instance. This notebook contains steps and code to work with Watson Machine Learning API. This notebook introduces API calls for listing artifacts, getting artifacts details and deleting them.

Some familiarity with Python and REST API is helpful. This notebook uses Python 3.

Learning goals

The learning goals of this notebook are:

• List Watson Machine Learning artifacts.

• Get artifacts details.

• Delete artifacts.

Contents

This notebook contains the following parts:

1. Setup

2. Manage pipelines

3. Manage model definitions

4. Manage models

5. Manage functions

6. Manage experiments

7. Manage trainings

8. Manage deployments

9. Summary and next steps

1. Set up the environment

Before you use the sample code in this notebook, you must perform the following setup tasks:

• Contact with your Cloud Pack for Data administrator and ask him for your account credentials

Connection to WML

Authenticate the Watson Machine Learning service on IBM Cloud Pack for Data. You need to provide platform url, your username and password.

USERNAME=""
PASSWORD=""
DATAPLATFORM_URL=""

space_id="fill out only if you have space already created"

In order to work with REST API in python you need to import requests package.

import requests

Generate WML authorization token for further REST API calls

Request params preparation.

token_creation_params = (USERNAME, PASSWORD)

Token generation.

response = requests.get(f'{DATAPLATFORM_URL}/v1/preauth/validateAuth', auth=token_creation_params, verify=False)
token = response.json()['accessToken']
print(token)

eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZSI6ImFkbWluIiwicm9sZSI6IkFkbWluIiwicGVybWlzc2lvbnMiOlsiYWRtaW5pc3RyYXRvciIsImNhbl9wcm92aXNpb24iLCJtYW5hZ2VfY2F0YWxvZyIsImFjY2Vzc19jYXRhbG9nIl0sImdyb3VwcyI6WzEwMDAwXSwic3ViIjoiYWRtaW4iLCJpc3MiOiJLTk9YU1NPIiwiYXVkIjoiRFNYIiwidWlkIjoiMTAwMDMzMDk5OSIsImF1dGhlbnRpY2F0b3IiOiJkZWZhdWx0IiwiaWF0IjoxNjA3NDM1NTc3LCJleHAiOjE2MDc0Nzg3NDF9.S3jebZyPe3guKOBc-q8UvLyLRJiLRsxnkzls3ugUJJ4VEB6n-1Kk7WEhO6-FecCE7PE3OF5fjZAnkTCkHHkW2vYD5flZ5p-HD2whbivrNRX-2cOr5lyZo_e24gffrYDA631f2U_L5sC4RqfrFTj5ewjcM8X9rJYcVLc3-W5siu1FlQ2oQlHRg4QDe39avVUghuP2idKZKaEM-TLB3zLvH7eiRXyqUQhRaVrxsnjm4Lu_e9p2eTcpx-xH4Vi2GD7c2U7wkoY9Nmst2WB6siv5b88CYaviRNL0iGTnwquUv4efCSXqJ4fwzJUpnvVlOuUjntHwFR7J5XlJnVaVOEtcAw

Define requests header for futher REST API calls.

header = {
        'Authorization': f'Bearer {token}',
        'Content-Type': 'application/json',
        'Accept': 'application/json'
}

Space creation

Tip: If you do not have space already created, please convert below three Raw NBConvert cells to code and run them.

Prepare payload json for space creation.

space_json={
  "name": "my_space2",
  "description": "my_description2",
}

Create new space.

space_details = requests.post(
    url = f"{DATAPLATFORM_URL}/v2/spaces",
    headers = header,
    json = space_json,
    verify=False
)

Get created space id.

space_id = space_details.json()['metadata']['id']
print(space_id)

Space creation is asynchronous. This means that you need to check space creation status after creation call. Make sure that your newly created space is active.

Get space details.

space_details = requests.get(
    url = f"{DATAPLATFORM_URL}/v2/spaces/{space_id}?version=2020-08-01",
    headers = header,
    verify=False
)

space_details.json()

{'entity': {'compute': [{'crn': 'crn:v1:cpd:private:pm-20:private:a/cpduser:99999999-9999-9999-9999-999999999999::',
    'guid': '99999999-9999-9999-9999-999999999999',
    'name': 'Watson Machine Learning',
    'type': 'machine_learning'}],
  'description': 'space for wml samples tests',
  'name': 'wml_samples',
  'scope': {'bss_account_id': 'cpdaccount'},
  'status': {'state': 'active'}},
 'metadata': {'created_at': '2020-12-08T13:39:33.366Z',
  'creator_id': '1000330999',
  'id': 'cc59083a-923f-4c35-8947-10c126343bd0',
  'updated_at': '2020-12-08T13:39:40.385Z',
  'url': '/v2/spaces/cc59083a-923f-4c35-8947-10c126343bd0'}}

Managing spaces.

If you want to get all spaces details you can run following REST API call. If you want to get part of spaces please change the limit variable.

limit=2
spaces_details = requests.get(
    url = f"{DATAPLATFORM_URL}/v2/spaces?limit={limit}",
    headers = header,
    verify=False
)

Print spaces details json.

spaces_details.json()

{'first': {'href': 'https://wmlgmc-cpd-wmlgmc.apps.wmlautoai.cp.fyre.ibm.com/v2/spaces?limit=2'},
 'limit': 2,
 'next': {'href': 'https://wmlgmc-cpd-wmlgmc.apps.wmlautoai.cp.fyre.ibm.com/v2/spaces?start=g2wAAAACaAJkAA5zdGFydGtleV9kb2NpZG0AAAAkY2M1OTA4M2EtOTIzZi00YzM1LTg5NDctMTBjMTI2MzQzYmQwaAJkAAhzdGFydGtleWwAAAABbQAAABgyMDIwLTEyLTA4VDEzOjM5OjMzLjM2Nlpqag&limit=2'},
 'resources': [{'entity': {'compute': [{'crn': 'crn:v1:cpd:private:pm-20:private:a/cpduser:99999999-9999-9999-9999-999999999999::',
      'guid': '99999999-9999-9999-9999-999999999999',
      'name': 'Watson Machine Learning',
      'type': 'machine_learning'}],
    'name': 'tests_sdk_space',
    'scope': {'bss_account_id': 'cpdaccount'},
    'status': {'state': 'active'}},
   'metadata': {'created_at': '2020-12-08T09:18:07.312Z',
    'creator_id': '1000330999',
    'id': 'ac3eb395-7c85-4a69-90ca-82f853ff6391',
    'updated_at': '2020-12-08T09:18:14.953Z',
    'url': '/v2/spaces/ac3eb395-7c85-4a69-90ca-82f853ff6391'}},
  {'entity': {'compute': [{'crn': 'crn:v1:cpd:private:pm-20:private:a/cpduser:99999999-9999-9999-9999-999999999999::',
      'guid': '99999999-9999-9999-9999-999999999999',
      'name': 'Watson Machine Learning',
      'type': 'machine_learning'}],
    'description': 'space for wml samples tests',
    'name': 'wml_samples',
    'scope': {'bss_account_id': 'cpdaccount'},
    'status': {'state': 'active'}},
   'metadata': {'created_at': '2020-12-08T13:39:33.366Z',
    'creator_id': '1000330999',
    'id': 'cc59083a-923f-4c35-8947-10c126343bd0',
    'updated_at': '2020-12-08T13:39:40.385Z',
    'url': '/v2/spaces/cc59083a-923f-4c35-8947-10c126343bd0'}}]}

If you want to list existing spaces names and ids run next cell.

for space in spaces_details.json()['resources']:
    print(f"{space['entity']['name']} \t Id: {space['metadata']['id']}")

tests_sdk_space 	 Id: ac3eb395-7c85-4a69-90ca-82f853ff6391
wml_samples 	 Id: cc59083a-923f-4c35-8947-10c126343bd0

If you want to delete one of existing spaces you can change next cell Format to code and use following REST API call.

space_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/v2/spaces/{space_id}?version=2020-08-01",
    headers = header,
    verify=False
)

2. Manage pipelines

If you want to get all pipelines details you can run following REST API call. If you want to get part of pipelines please change the limit variable.

limit=2
pipelines_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/pipelines?version=2020-08-01&space_id={space_id}&limit={limit}",
    headers = header,
    verify=False
)

If you want to list existing pipelines names and ids run next cell.

for pipeline in pipelines_details.json()['resources']:
    print(f"{pipeline['metadata']['name']} \t Id: {pipeline['metadata']['id']}")

Get pipeline 0 id.

pipeline_id = pipelines_details.json()['resources'][0]['metadata']['id']
print(pipeline_id)

If you want to get pipeline details you must provide pipeline_id and run following REST API call.

pipeline_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/pipelines/{pipeline_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)

Print pipeline details.

pipelines_details.json()

{'first': {'href': '/ml/v4/pipelines?space_id=cc59083a-923f-4c35-8947-10c126343bd0&limit=2'},
 'resources': [],
 'total_count': 0}

You can delete pipeline by next cell API CALL.

pipeline_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/pipelines/{pipeline_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)
if pipeline_delete.status_code in [200,202,204]:
    print('SUCCES')
else:
    print(f'Failed to delete pipeline: \n {pipeline_delete.json()}')

If you want to delete more pipelines run following cell.

for pipeline in pipelines_details.json()['resources']:
    pipeline_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/pipelines/{pipeline['metadata']['id']}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
    )
    if pipeline_delete.status_code in [200,202,204]:
        print('SUCCES')
    else:
        print(f'Failed to delete pipeline: \n {pipeline_delete.json()}')

3. Manage model definitions

If you want to get all model definitions details you can run following REST API call. If you want to get part of model definitions please change the limit variable.

limit=2
model_definitions_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/model_definitions?version=2020-08-01&space_id={space_id}&limit={limit}",
    headers = header,
    verify=False
)

If you want to list existing model definitions names and ids run next cell.

for model_definition in model_definitions_details.json()['resources']:
    print(f"{model_definition['metadata']['name']} \t Id: {model_definition['metadata']['id']}")

Get model definition 0 id.

model_definition_id = model_definitions_details.json()['resources'][0]['metadata']['id']
print(model_definition_id)

If you want to get model definition details you must provide model_definition_id and run following REST API call.

model_definition_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/model_definitions/{model_definition_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)

Print model definition details.

model_definition_details.json()

You can delete model definition by next cell API CALL.

model_definition_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/model_definitions/{model_definition_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)
if model_definition_delete.status_code in [200,202,204]:
    print('SUCCES')
else:
    print(f'Failed to delete model definition: \n {model_definition_delete.json()}')

If you want to delete more model definitions run following cell.

for model_definition in model_definitions_details.json()['resources']:
    model_definition_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/model_definitions/{model_definition['metadata']['id']}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
    )
    if model_definition_delete.status_code in [200,202,204]:
        print('SUCCES')
    else:
        print(f'Failed to delete model definition: \n {model_definition_delete.json()}')

4. Manage models

If you want to get all models details you can run following REST API call. If you want to get part of models please change the limit variable.

limit=2
models_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/models?version=2020-08-01&space_id={space_id}&limit={limit}",
    headers = header,
    verify=False
)

In order to filter models by software specification you can paste software specification id to the software_spec field.

limit=2
software_spec="63dc4cf1-252f-424b-b52d-5cdd9814987f"
models_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/models?version=2020-08-01&space_id={space_id}&limit={limit}&software_spec={software_spec}",
    headers = header,
    verify=False
)

If you want to list existing models names and ids run next cell.

for model in models_details.json()['resources']:
    print(f"{model['metadata']['name']} \t Id: {model['metadata']['id']}")

External scikit model - updated 	 Id: adf70e8b-d060-4f50-b37d-5760797e5052

Get model 0 id.

model_id = models_details.json()['resources'][0]['metadata']['id']
print(model_id)

adf70e8b-d060-4f50-b37d-5760797e5052

If you want to get model details you must provide model_id and run following REST API call.

model_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/models/{model_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)

Print model details.

model_details.json()

{'entity': {'software_spec': {'id': 'e4429883-c883-42b6-87a8-f419d64088cd',
   'name': 'default_py3.7'},
  'type': 'scikit-learn_0.23'},
 'metadata': {'commit_info': {'committed_at': '2020-12-08T13:54:41.974Z'},
  'created_at': '2020-12-08T13:40:52.523Z',
  'id': 'adf70e8b-d060-4f50-b37d-5760797e5052',
  'modified_at': '2020-12-08T13:41:37.662Z',
  'name': 'External scikit model - updated',
  'owner': '1000330999',
  'space_id': 'cc59083a-923f-4c35-8947-10c126343bd0'},
 'system': {'warnings': []}}

If you want to get model revision you must provide model_id and run following REST API call.

model_revisons = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/models/{model_id}/revisions?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)

Print model revision.

model_revisons.json()

{'first': {'href': '/ml/v4/models/adf70e8b-d060-4f50-b37d-5760797e5052/revisions?space_id=cc59083a-923f-4c35-8947-10c126343bd0&limit=100'},
 'limit': 100,
 'resources': [{'metadata': {'commit_info': {'committed_at': '2020-12-08T13:41:37.002Z'},
    'created_at': '2020-12-08T13:40:52.523Z',
    'id': 'adf70e8b-d060-4f50-b37d-5760797e5052',
    'modified_at': '2020-12-08T13:41:35.250Z',
    'name': 'External scikit model - updated',
    'owner': '1000330999',
    'rev': '2',
    'space_id': 'cc59083a-923f-4c35-8947-10c126343bd0'}},
  {'metadata': {'commit_info': {'committed_at': '2020-12-08T13:41:27.002Z'},
    'created_at': '2020-12-08T13:40:52.523Z',
    'id': 'adf70e8b-d060-4f50-b37d-5760797e5052',
    'modified_at': '2020-12-08T13:40:55.065Z',
    'name': 'External scikit model',
    'owner': '1000330999',
    'rev': '1',
    'space_id': 'cc59083a-923f-4c35-8947-10c126343bd0'}}]}

You can delete model by next cell API CALL.

model_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/models/{model_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)
if model_delete.status_code in [200,202,204]:
    print('SUCCES')
else:
    print(f'Failed to delete model: \n {model_delete.json()}')

If you want to delete more models run following cell.

for model in models_details.json()['resources']:
    model_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/models/{model['metadata']['id']}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
    )
    if model_delete.status_code in [200,202,204]:
        print('SUCCES')
    else:
        print(f'Failed to delete model: \n {model_delete.json()}')

5. Manage functions

If you want to get all functions details you can run following REST API call. If you want to get part of functions please change the limit variable.

limit=2
functions_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/functions?version=2020-08-01&space_id={space_id}&limit={limit}",
    headers = header,
    verify=False
)

If you want to list existing functions names and ids run next cell.

for function in functions_details.json()['resources']:
    print(f"{function['metadata']['name']} \t Id: {function['metadata']['id']}")

Get function 0 id.

function_id = functions_details.json()['resources'][0]['metadata']['id']
print(function_id)

If you want to get function details you must provide function_id and run following REST API call.

function_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/functions/{function_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)

Print function details.

function_details.json()

You can delete function by next cell API CALL.

function_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/functions/{function_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)
if function_delete.status_code in [200,202,204]:
    print('SUCCES')
else:
    print(f'Failed to delete function: \n {function_delete.json()}')

If you want to delete more functions run following cell.

for function in functions_details.json()['resources']:
    function_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/functions/{function['metadata']['id']}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
    )
    if function_delete.status_code in [200,202,204]:
        print('SUCCES')
    else:
        print(f'Failed to delete function: \n {function_delete.json()}')

6. Manage experiments

If you want to get all experiments details you can run following REST API call. If you want to get part of experiments please change the limit variable.

limit=2
experiments_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/experiments?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)

If you want to list existing experiments names and ids run next cell.

for experiment in experiments_details.json()['resources']:
    print(f"{experiment['metadata']['name']} \t Id: {experiment['metadata']['id']}")

Get experiment 0 id.

experiment_id = experiments_details.json()['resources'][0]['metadata']['id']
print(experiment_id)

If you want to get experiment details you must provide experiment_id and run following REST API call.

experiment_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/experiments/{experiment_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)

Print experiment details.

experiment_details.json()

You can delete experiment by next cell API CALL.

experiment_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/experiments/{experiment_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)
if experiment_delete.status_code in [200,202,204]:
    print('SUCCES')
else:
    print(f'Failed to delete experiment: \n {experiment_delete.json()}')

If you want to delete more experiments change run following cell.

for experiment in experiments_details.json()['resources']:
    experiment_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/experiments/{experiment['metadata']['id']}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
    )
    if experiment_delete.status_code in [200,202,204]:
        print('SUCCES')
    else:
        print(f'Failed to delete experiment: \n {experiment_delete.json()}')

7. Manage trainings

If you want to get all trainings details you can run following REST API call. If you want to get part of trainings please change the limit variable.

limit=2
trainings_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/trainings?version=2020-08-01&space_id={space_id}&limit={limit}",
    headers = header,
    verify=False
)

In order to filter trainings by the training type (e.g. pipeline, experiment) please change training_type variable and run next cell.

limit=2
training_type='pipeline'
trainings_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/trainings?version=2020-08-01&space_id={space_id}&limit={limit}&type={training_type}",
    headers = header,
    verify=False
)

If you want to list existing trainings tags and ids run next cell.

for training in trainings_details.json()['resources']:
    print(f"{training['metadata']['tags']} \t Id: {training['metadata']['id']}")

Get training 0 id.

training_id = trainings_details.json()['resources'][0]['metadata']['id']
print(training_id)

If you want to get training details you must provide training_id and run following REST API call.

training_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/trainings/{training_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)

Print training details.

training_details.json()

You can delete training by next cell API CALL.

Note: DELETE CALL has parameter hard_delete, please change it as fallows:

• 'true' - to delete the completed or canceled training runs.

• 'false' - to cancel the currently running training run.

hard_delete = 'true'
training_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/trainings/{training_id}?version=2020-08-01&space_id={space_id}&hard_delete={hard_delete}",
    headers = header,
    verify=False
)
if training_delete.status_code in [200,202,204]:
    print('SUCCES')
else:
    print(f'Failed to delete training: \n {training_delete.json()}')

8. Manage deployments

If you want to get all deployments details you can run following REST API call. If you want to get part of deployments please change the limit variable.

limit=2
deployments_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/deployments?version=2020-08-01&space_id={space_id}&limit={limit}",
    headers = header,
    verify=False
)

If you want to list existing deployments names and ids run next cell.

for deployment in deployments_details.json()['resources']:
    print(f"{deployment['metadata']['name']} \t Id: {deployment['metadata']['id']}")

Deployment of external scikit model - scaling 	 Id: 47fce40d-2e2c-43da-af37-1cd8c131a18d

Get deployment 0 id.

deployment_id = deployments_details.json()['resources'][0]['metadata']['id']
print(deployment_id)

47fce40d-2e2c-43da-af37-1cd8c131a18d

If you want to get deployment details you must provide deployment_id and run following REST API call.

deployment_details = requests.get(
    url = f"{DATAPLATFORM_URL}/ml/v4/deployments/{deployment_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)

Print deployment details.

deployment_details.json()

{'entity': {'asset': {'id': 'adf70e8b-d060-4f50-b37d-5760797e5052',
   'rev': '2'},
  'custom': {},
  'deployed_asset_type': 'model',
  'hardware_spec': {'id': 'Not_Applicable', 'name': 'S', 'num_nodes': 2},
  'name': 'Deployment of external scikit model - scaling',
  'online': {},
  'space_id': 'cc59083a-923f-4c35-8947-10c126343bd0',
  'status': {'message': {'text': 'scaling_status: completed;requested_copies: 2;deployed_copies: 2;more_info: Successfully patched the asset ',
    'level': 'warning'},
   'online_url': {'url': 'https://wmlgmc-cpd-wmlgmc.apps.wmlautoai.cp.fyre.ibm.com/ml/v4/deployments/47fce40d-2e2c-43da-af37-1cd8c131a18d/predictions'},
   'state': 'ready'}},
 'metadata': {'created_at': '2020-12-08T13:41:07.003Z',
  'id': '47fce40d-2e2c-43da-af37-1cd8c131a18d',
  'modified_at': '2020-12-08T13:42:27.361Z',
  'name': 'Deployment of external scikit model - scaling',
  'owner': '1000330999',
  'space_id': 'cc59083a-923f-4c35-8947-10c126343bd0'}}

You can delete deployment by next cell API CALL.

deployment_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/deployments/{deployment_id}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
)
if deployment_delete.status_code in [200,202,204]:
    print('SUCCES')
else:
    print(f'Failed to delete deployment: \n {deployment_delete.json()}')

If you want to delete more deployments run following cell.

for deployment in deployments_details.json()['resources']:
    deployment_delete = requests.delete(
    url = f"{DATAPLATFORM_URL}/ml/v4/deployments/{deployment['metadata']['id']}?version=2020-08-01&space_id={space_id}",
    headers = header,
    verify=False
    )
    if deployment_delete.status_code in [200,202,204]:
        print('SUCCES')
    else:
        print(f'Failed to delete deployment: \n {deployment_delete.json()}')

9. Summary and next steps

You successfully completed this notebook! You learned how to use REST API client for Watson Machine Learning instance management and clean up.

Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Authors

Szymon Kucharczyk, Software Engineer at IBM.

Copyright © 2020-2025 IBM. This notebook and its source code are released under the terms of the MIT License.