Decision Optimization REST API deployment

REST API example

You can deploy a Decision Optimization model, create and monitor jobs and get solutions using the Watson Machine Learning REST API.

Before you begin

You must have an IBM Cloud account. See https://www.ibm.com/cloud/.

Log in to IBM Cloud.
Create your API key. Copy or download it from the API key successfully created open window (you cannot access it again once you close this pane).
Create or select a Machine Learning service. Copy the service instance name, GUID and CRN from the information pane for your instance in the Resource List>Services view on IBM Cloud. (Expand the list of services in the Resource List window. Click anywhere in the row next to your Machine Learning Service name, but not on the name itself. This opens the information pane in the same window.)
Create or select a Cloud Object Storage. Copy the Cloud Object Storage instance name and CRN from the information pane from the information pane for your instance in the Resource List>Storage view on IBM Cloud.

You must also create a deployment space and obtain the Space ID using any one of these methods:

From the https://dataplatform.cloud.ibm.com user interface, create a deployment space. Then view it and copy your Space ID from the settings tab. For more information see Creating a deployment space.
With the REST API. See Creating a deployment space using the REST API.

About this task

The following steps show you how deploy a Decision Optimization model using the Watson Machine Learning REST API. The REST API example uses curl, a command line tool and library for transferring data with URL syntax. You can download curl and read more about it at http://curl.haxx.se. For more information about the REST APIs relevant for Decision Optimization, see the following sections:

For Windows users, use ^ instead of \ for the multi-line separator and double quotation marks " throughout these code examples. Windows users also need to use indentation of at least one character space in the header lines.

For clarity, some code examples in this procedure have been placed in a json file to make the commands more readable and easier to use.

Once you have created a deployment using the REST API, you can also view it and send jobs to it from the Deployment spaces page in the https://dataplatform.cloud.ibm.com user interface.

Procedure

Generate an IAM token using your IBM Cloud API key as follows.

curl "https://iam.bluemix.net/identity/token" \
  -d "apikey=YOUR_API_KEY_HERE&grant_type=urn%3Aibm%3Aparams%3Aoauth%3Agrant-type%3Aapikey" \
  -H "Content-Type: application/x-www-form-urlencoded" \
  -H "Authorization: Basic Yng6Yng="

Output example:

{
   "access_token": "****** obtained IAM token ******************************",
   "refresh_token": "**************************************",
   "token_type": "Bearer",
   "expires_in": 3600,
   "expiration": 1554117649,
   "scope": "ibm openid"
}

Use the obtained token (access_token value) prepended by the word Bearer in the Authorization header, and the Machine Learning service GUID in the ML-Instance-ID header, in all API calls.

Optional: If you have not obtained your SPACE-ID from the user interface as described previously, you can create a space using the REST API as follows. Use the previously obtained token prepended by the word bearer in the Authorization header in all API calls.

curl --location --request POST \
  "https://api.dataplatform.cloud.ibm.com/v2/spaces" \
  -H "Authorization: Bearer TOKEN-HERE" \
  -H "ML-Instance-ID: MACHINE-LEARNING-SERVICE-GUID-HERE" \
  -H "Content-Type: application/json" \
  --data-raw "{
  "name": "SPACE-NAME-HERE",
  "description": "optional description here",
  "storage": {
      "resource_crn": "COS-CRN-ID-HERE"
  },
  "compute": [{
    "name": "MACHINE-LEARNING-SERVICE-NAME-HERE",
    "crn": "MACHINE-LEARNING-SERVICE-CRN-ID-HERE"
  }]
}"

For Windows users, put the --data-raw command on one line and replace all " with \" inside this command as follows:

curl --location --request POST ^
  "https://api.dataplatform.cloud.ibm.com/v2/spaces" ^
  -H "Authorization: Bearer TOKEN-HERE" ^
  -H "ML-Instance-ID: MACHINE-LEARNING-SERVICE-GUID-HERE" ^
  -H "Content-Type: application/json" ^
  --data-raw "{\"name\": "SPACE-NAME-HERE",\"description\": \"optional description here\",\"storage\": {\"resource_crn\": \"COS-CRN-ID-HERE\"  },\"compute\": [{\"name\": "MACHINE-LEARNING-SERVICE-NAME-HERE\",\"crn\": \"MACHINE-LEARNING-SERVICE-CRN-ID-HERE\"  }]}"

Alternatively put the data in a separate file.

A SPACE-ID is returned in id field of the metadata section.

Output example:

{
  "entity": {
    "compute": [
      {
        "crn": "MACHINE-LEARNING-SERVICE-CRN",
        "guid": "MACHINE-LEARNING-SERVICE-GUID",
        "name": "MACHINE-LEARNING-SERVICE-NAME",
        "type": "machine_learning"
      }
    ],
    "description": "string",
    "members": [
      {
        "id": "XXXXXXX",
        "role": "admin",
        "state": "active",
        "type": "user"
      }
    ],
    "name": "name",
    "scope": {
      "bss_account_id": "account_id"
    },
    "status": {
      "state": "active"
    }
  },
  "metadata": {
    "created_at": "2020-07-17T08:36:57.611Z",
    "creator_id": "XXXXXXX",
    "id": "SPACE-ID",
    "url": "/v2/spaces/SPACE-ID"
  }
}

You must wait until your deployment space status is "active" before continuing. You can poll to check for this as follows.

curl --location --request GET "https://api.dataplatform.cloud.ibm.com/v2/spaces/SPACE-ID-HERE" \
  -H "Authorization: bearer TOKEN-HERE" \
  -H "Content-Type: application/json"

Create a new Decision Optimization model

All API requests require a version parameter that takes a date in the format version=YYYY-MM-DD. This code example posts a model that uses the file create_model.json. The URL will vary according to the chosen region/location for your machine learning service. See Endpoint URLs.

curl --location --request POST \
  "https://us-south.ml.cloud.ibm.com/ml/v4/models?version=2020-08-01" \
  -H "Authorization: bearer TOKEN-HERE" \
  -H "Content-Type: application/json" \
  -d @create_model.json

The create_model.json file contains the following code:

{
  "name": "ModelName",
  "description": "ModelDescription",
  "type": "do-docplex_22.1",
  "software_spec": {
    "name": "do_22.1"
  },
  "custom": {
      "decision_optimization": {
        "oaas.docplex.python": "3.11"
      }
  },
  "space_id": "SPACE-ID-HERE"
}

The Python version is stated explicitly here in a custom block. This is optional. Without it your model will use the default version which is currently Python 3.11. As the default version will evolve over time, stating the Python version explicitly enables you to easily change it later or to keep using an older supported version when the default version is updated. Currently supported versions are 3.11 and 3.10 (deprecated).

If you want to be able to run jobs for this model from the user interface, instead of only using the REST API , you must define the schema for the input and output data. If you do not define the schema when you create the model, you can only run jobs using the REST API and not from the user interface.

You can also use the schema specified for input and output in your optimization model:

{
  "name": "Diet-Model-schema",
  "description": "Diet",
  "type": "do-docplex_22.1",
  "schemas": {
    "input": [
      {
        "id": "diet_food_nutrients",
        "fields": [
          { "name": "Food",  "type": "string" },
          { "name": "Calories", "type": "double" },
          { "name": "Calcium", "type": "double" },
          { "name": "Iron", "type": "double" },
          { "name": "Vit_A", "type": "double" },
          { "name": "Dietary_Fiber", "type": "double" },
          { "name": "Carbohydrates", "type": "double" },
          { "name": "Protein", "type": "double" }
        ]
      },
      {
        "id": "diet_food",
        "fields": [
          { "name": "name", "type": "string" },
          { "name": "unit_cost", "type": "double" },
          { "name": "qmin", "type": "double" },
          { "name": "qmax", "type": "double" }
        ]
      },
      {
        "id": "diet_nutrients",
        "fields": [
          { "name": "name", "type": "string" },
          { "name": "qmin", "type": "double" },
          { "name": "qmax", "type": "double" }
        ]
      }
    ],
    "output": [
      {
        "id": "solution",
        "fields": [
          { "name": "name", "type": "string" },
          { "name": "value", "type": "double" }
        ]
      }
    ]
  },
  "software_spec": {
    "name": "do_22.1"
  },
  "space_id": "SPACE-ID-HERE"
}

When you post a model you provide information about its model type and the software specification to be used.

Model types can be, for example:

do-opl_22.1 for OPL models
do-cplex_22.1 for CPLEX models
do-cpo_22.1 for CP models
do-docplex_22.1 for Python models

Version 20.1 can also be used for these model types.

For the software specification, you can use the default specifications using their names do_22.1 or do_20.1. See also Extend software specification notebook which shows you how to extend the Decision Optimization software specification (runtimes with additional Python libraries for DOcplex models).

A MODEL-ID is returned in id field in the metadata.

Output example:

{
  "entity": {
    "software_spec": {
      "id": "SOFTWARE-SPEC-ID"
    },
    "type": "do-docplex_20.1"
  },
  "metadata": {
    "created_at": "2020-07-17T08:37:22.992Z",
    "description": "ModelDescription",
    "id": "MODEL-ID",
    "modified_at": "2020-07-17T08:37:22.992Z",
    "name": "ModelName",
    "owner": "***********",
    "space_id": "SPACE-ID"
  }
}

Upload a Decision Optimization model formulation ready for deployment.
First compress your model into a (tar.gz, .zip or .jar) file and upload it to be deployed by the Watson Machine Learning service.
This code example uploads a model called diet.zip that contains a Python model and no master data:
```
curl --location --request PUT \
  "https://us-south.ml.cloud.ibm.com/ml/v4/models/MODEL-ID-HERE/content?version=2020-08-01&space_id=SPACE-ID-HERE&content_format=native" \
  -H "Authorization: bearer TOKEN-HERE" \
  -H "Content-Type: application/gzip" \
  --data-binary "@diet.zip"
```
You can download this example and other models from the DO-samples. Select the relevant product and version subfolder.

Deploy your model

Create a reference to your model. Use the SPACE-ID, the MODEL-ID obtained when you created your model ready for deployment and the hardware specification. For example:

curl --location --request POST "https://us-south.ml.cloud.ibm.com/ml/v4/deployments?version=2020-08-01" \
  -H "Authorization: bearer TOKEN-HERE" \
  -H "Content-Type: application/json" \
  -d @deploy_model.json

The deploy_model.json file contains the following
code:

{
  "name": "Test-Diet-deploy",
  "space_id": "SPACE-ID-HERE",
  "asset": {
    "id": "MODEL-ID-HERE"
  },
  "hardware_spec": {
    "name": "S"
  },
  "batch": {}
}

The DEPLOYMENT-ID is returned in id field in the metadata. Output example:

{
  "entity": {
    "asset": {
      "id": "MODEL-ID"
    },
    "custom": {},
    "description": "",
    "hardware_spec": {
      "id": "HARDWARE-SPEC-ID",
      "name": "S",
      "num_nodes": 1
    },
    "name": "Test-Diet-deploy",
    "space_id": "SPACE-ID",
    "status": {
      "state": "ready"
    }
  },
  "metadata": {
    "created_at": "2020-07-17T09:10:50.661Z",
    "description": "",
    "id": "DEPLOYMENT-ID",
    "modified_at": "2020-07-17T09:10:50.661Z",
    "name": "test-Diet-deploy",
    "owner": "**************",
    "space_id": "SPACE-ID"
  }
}

Once deployed, you can monitor your model's deployment state. Use the DEPLOYMENT-ID.

For example:

curl --location --request GET "https://us-south.ml.cloud.ibm.com/ml/v4/deployments/DEPLOYMENT-ID-HERE?version=2020-08-01&space_id=SPACE-ID-HERE" \
  -H "Authorization: bearer TOKEN-HERE" \
  -H "Content-Type: application/json"

Output example:

{
  "entity": {
    "asset": {
      "id": "MODEL-ID"
    },
    "custom": {},
    "description": "",
    "hardware_spec": {
      "id": "HARDWARE-SPEC-ID",
      "name": "S",
      "num_nodes": 1
    },
    "name": "Test-Diet-deploy",
    "space_id": "SPACE-ID",
    "status": {
      "state": "ready"
    }
  },
  "metadata": {
    "created_at": "2020-11-04T16:22:14.442Z",
    "description": "",
    "id": "DEPLOYMENT-ID",
    "modified_at": "2020-11-04T16:22:14.442Z",
    "name": "test-Diet-deploy",
    "owner": "**************",
    "space_id": "SPACE-ID"
  }
}

You can then Submit jobs for your deployed model defining the input data and the output (results of the optimization solve) and the log file.

For example, the following shows the contents of a file called myjob.json. It contains (inline) input data, some solve parameters, and specifies that the output will be a .csv file. For more information see Decision Optimization batch deployment and model execution.

{
	"name":"test-job-diet",
	"space_id": "SPACE-ID-HERE",
	"deployment": {
		"id": "DEPLOYMENT-ID-HERE"
	},
	"decision_optimization" : {
		"solve_parameters" : {
			"oaas.logAttachmentName":"log.txt",
			"oaas.logTailEnabled":"true"
		},
		"input_data": [
			{
				"id":"diet_food.csv",
				"fields" : ["name","unit_cost","qmin","qmax"],
				"values" : [
					["Roasted Chicken", 0.84, 0, 10],
					["Spaghetti W/ Sauce", 0.78, 0, 10],
					["Tomato,Red,Ripe,Raw", 0.27, 0, 10],
					["Apple,Raw,W/Skin", 0.24, 0, 10],
					["Grapes", 0.32, 0, 10],
					["Chocolate Chip Cookies", 0.03, 0, 10],
					["Lowfat Milk", 0.23, 0, 10],
					["Raisin Brn", 0.34, 0, 10],
					["Hotdog", 0.31, 0, 10]
				]
			},
			{
				"id":"diet_food_nutrients.csv",
				"fields" : ["Food","Calories","Calcium","Iron","Vit_A","Dietary_Fiber","Carbohydrates","Protein"],
				"values" : [
					["Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2],
					["Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2],
					["Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1],
					["Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3],
					["Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2],
					["Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9],
					["Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1],
					["Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4],
					["Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4]
				]
			},
			{
				"id":"diet_nutrients.csv",
				"fields" : ["name","qmin","qmax"],
				"values" : [
					["Calories", 2000, 2500],
					["Calcium", 800, 1600],
					["Iron", 10, 30],
					["Vit_A", 5000, 50000],
					["Dietary_Fiber", 25, 100],
					["Carbohydrates", 0, 300],
					["Protein", 50, 100]
				]
			}
		],
		"output_data": [
			{
				"id":".*\\.csv"
			}
		]
	}
}

This code example posts a job that uses this file myjob.json.

curl --location --request POST "https://us-south.ml.cloud.ibm.com/ml/v4/deployment_jobs?version=2020-08-01&space_id=SPACE-ID-HERE" \
  -H "Authorization: bearer TOKEN-HERE" \
  -H "Content-Type: application/json" \
  -H "cache-control: no-cache" \
  -d @myjob.json

A JOB-ID is returned. Output example: (the job is queued)

{
  "entity": {
    "decision_optimization": {
      "input_data": [{
        "id": "diet_food.csv",
        "fields": ["name", "unit_cost", "qmin", "qmax"],
        "values": [["Roasted Chicken", 0.84, 0, 10], ["Spaghetti W/ Sauce", 0.78, 0, 10], ["Tomato,Red,Ripe,Raw", 0.27, 0, 10], ["Apple,Raw,W/Skin", 0.24, 0, 10], ["Grapes", 0.32, 0, 10], ["Chocolate Chip Cookies", 0.03, 0, 10], ["Lowfat Milk", 0.23, 0, 10], ["Raisin Brn", 0.34, 0, 10], ["Hotdog", 0.31, 0, 10]]
      }, {
        "id": "diet_food_nutrients.csv",
        "fields": ["Food", "Calories", "Calcium", "Iron", "Vit_A", "Dietary_Fiber", "Carbohydrates", "Protein"],
        "values": [["Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2], ["Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2], ["Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1], ["Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3], ["Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2], ["Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9], ["Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1], ["Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4], ["Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4]]
      }, {
        "id": "diet_nutrients.csv",
        "fields": ["name", "qmin", "qmax"],
        "values": [["Calories", 2000, 2500], ["Calcium", 800, 1600], ["Iron", 10, 30], ["Vit_A", 5000, 50000], ["Dietary_Fiber", 25, 100], ["Carbohydrates", 0, 300], ["Protein", 50, 100]]
      }],
      "output_data": [
        {
          "id": ".*\\.csv"
        }
      ],
      "solve_parameters": {
        "oaas.logAttachmentName": "log.txt",
        "oaas.logTailEnabled": "true"
      },
      "status": {
        "state": "queued"
      }
    },
    "deployment": {
      "id": "DEPLOYMENT-ID"
    },
    "platform_job": {
      "job_id": "",
      "run_id": ""
    }
  },
  "metadata": {
    "created_at": "2020-07-17T10:42:42.783Z",
    "id": "JOB-ID",
    "name": "test-job-diet",
    "space_id": "SPACE-ID"
  }
}

You can also monitor job states. Use the JOB-ID

For example:

curl --location --request GET \
  "https://us-south.ml.cloud.ibm.com/ml/v4/deployment_jobs/JOB-ID-HERE?version=2020-08-01&space_id=SPACE-ID-HERE" \
  -H "Authorization: bearer TOKEN-HERE" \
  -H "Content-Type: application/json"

Output example: (job has completed)

{
  "entity": {
    "decision_optimization": {
      "input_data": [{
        "id": "diet_food.csv",
        "fields": ["name", "unit_cost", "qmin", "qmax"],
        "values": [["Roasted Chicken", 0.84, 0, 10], ["Spaghetti W/ Sauce", 0.78, 0, 10], ["Tomato,Red,Ripe,Raw", 0.27, 0, 10], ["Apple,Raw,W/Skin", 0.24, 0, 10], ["Grapes", 0.32, 0, 10], ["Chocolate Chip Cookies", 0.03, 0, 10], ["Lowfat Milk", 0.23, 0, 10], ["Raisin Brn", 0.34, 0, 10], ["Hotdog", 0.31, 0, 10]]
      }, {
        "id": "diet_food_nutrients.csv",
        "fields": ["Food", "Calories", "Calcium", "Iron", "Vit_A", "Dietary_Fiber", "Carbohydrates", "Protein"],
        "values": [["Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2], ["Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2], ["Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1], ["Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3], ["Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2], ["Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9], ["Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1], ["Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4], ["Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4]]
      }, {
        "id": "diet_nutrients.csv",
        "fields": ["name", "qmin", "qmax"],
        "values": [["Calories", 2000, 2500], ["Calcium", 800, 1600], ["Iron", 10, 30], ["Vit_A", 5000, 50000], ["Dietary_Fiber", 25, 100], ["Carbohydrates", 0, 300], ["Protein", 50, 100]]
      }],
      "output_data": [{
        "fields": ["Name", "Value"],
        "id": "kpis.csv",
        "values": [["Total Calories", 2000], ["Total Calcium", 800.0000000000001], ["Total Iron", 11.278317739831891], ["Total Vit_A", 8518.432542485823], ["Total Dietary_Fiber", 25], ["Total Carbohydrates", 256.80576358904455], ["Total Protein", 51.17372234135308], ["Minimal cost", 2.690409171696264]]
      }, {
        "fields": ["name", "value"],
        "id": "solution.csv",
        "values": [["Spaghetti W/ Sauce", 2.1551724137931036], ["Chocolate Chip Cookies", 10], ["Lowfat Milk", 1.8311671008899097], ["Hotdog", 0.9296975991385925]]
      }],
      "output_data_references": [],
      "solve_parameters": {
        "oaas.logAttachmentName": "log.txt",
        "oaas.logTailEnabled": "true"
      },
      "solve_state": {
        "details": {
          "KPI.Minimal cost": "2.690409171696264",
          "KPI.Total Calcium": "800.0000000000001",
          "KPI.Total Calories": "2000.0",
          "KPI.Total Carbohydrates": "256.80576358904455",
          "KPI.Total Dietary_Fiber": "25.0",
          "KPI.Total Iron": "11.278317739831891",
          "KPI.Total Protein": "51.17372234135308",
          "KPI.Total Vit_A": "8518.432542485823",
          "MODEL_DETAIL_BOOLEAN_VARS": "0",
          "MODEL_DETAIL_CONSTRAINTS": "7",
          "MODEL_DETAIL_CONTINUOUS_VARS": "9",
          "MODEL_DETAIL_INTEGER_VARS": "0",
          "MODEL_DETAIL_KPIS": "[\"Total Calories\", \"Total Calcium\", \"Total Iron\", \"Total Vit_A\", \"Total Dietary_Fiber\", \"Total Carbohydrates\", \"Total Protein\", \"Minimal cost\"]",
          "MODEL_DETAIL_NONZEROS": "57",
          "MODEL_DETAIL_TYPE": "LP",
          "PROGRESS_CURRENT_OBJECTIVE": "2.6904091716962637"
        },
        "latest_engine_activity": [
          "[2020-07-21T16:37:36Z, INFO] Model: diet",
          "[2020-07-21T16:37:36Z, INFO]  - number of variables: 9",
          "[2020-07-21T16:37:36Z, INFO]    - binary=0, integer=0, continuous=9",
          "[2020-07-21T16:37:36Z, INFO]  - number of constraints: 7",
          "[2020-07-21T16:37:36Z, INFO]    - linear=7",
          "[2020-07-21T16:37:36Z, INFO]  - parameters: defaults",
          "[2020-07-21T16:37:36Z, INFO]  - problem type is: LP",
          "[2020-07-21T16:37:36Z, INFO] Warning: Model: \"diet\" is not a MIP problem, progress listeners are disabled",
          "[2020-07-21T16:37:36Z, INFO] objective: 2.690",
          "[2020-07-21T16:37:36Z, INFO]   \"Spaghetti W/ Sauce\"=2.155",
          "[2020-07-21T16:37:36Z, INFO]   \"Chocolate Chip Cookies\"=10.000",
          "[2020-07-21T16:37:36Z, INFO]   \"Lowfat Milk\"=1.831",
          "[2020-07-21T16:37:36Z, INFO]   \"Hotdog\"=0.930",
          "[2020-07-21T16:37:36Z, INFO] solution.csv"
        ],
        "solve_status": "optimal_solution"
      },
      "status": {
        "completed_at": "2020-07-21T16:37:36.989Z",
        "running_at": "2020-07-21T16:37:35.622Z",
        "state": "completed"
      }
    },
    "deployment": {
      "id": "DEPLOYMENT-ID"
    }
  },
  "metadata": {
    "created_at": "2020-07-21T16:37:09.130Z",
    "id": "JOB-ID",
    "modified_at": "2020-07-21T16:37:37.268Z",
    "name": "test-job-diet",
    "space_id": "SPACE-ID"
  }
}

Optional: You can delete jobs as follows:

curl --location --request DELETE "https://us-south.ml.cloud.ibm.com/ml/v4/deployment_jobs/JOB-ID-HERE?version=2020-08-01&space_id=SPACE-ID-HERE&hard_delete=true" \
  -H "Authorization: bearer TOKEN-HERE"

If you delete a job using the API, it will still be displayed in the user interface.

Optional: You can delete deployments as follows:

curl --location --request DELETE "https://us-south.ml.cloud.ibm.com/ml/v4/deployments/DEPLOYMENT-ID-HERE?version=2020-08-01&space_id=SPACE-ID-HERE" \
  -H "Authorization: bearer TOKEN-HERE"

If you delete a deployment that contains jobs using the API, the jobs will still be displayed in the deployment space in the user interface.

Results

Once your model has been deployed and job executed, the solution results are provided either inline or in the file and location that you specified, for example using an S3 reference. You can post new jobs using the deployment-ID without having to redeploy your model.