REST API 示例 (Decision Optimization)

您可以使用 Watson Machine Learning REST API来部署 Decision Optimization 模型，创建和监视作业以及获取解决方案。

开始之前

您必须创建部署空间，并使用以下任一方法获取 空间标识 :

在Watson Machine Learning 中创建部署空间。然后查看它并从 "设置" 选项卡复制您的空间标识。更多信息，请参阅创建部署空间。
使用 REST API。请参阅使用 REST API 创建部署空间。

关于此任务

以下步骤显示如何使用 Watson Machine Learning REST API部署 Decision Optimization 模型。 REST API 示例使用 curl，这是一种命令行工具和库，用于以 URL 语法传输数据。您可以从以下网址下载 curl 并阅读更多相关信息 http://curl.haxx.se. 有关与 Decision Optimization 相关的 REST API 的更多信息，请参阅以下章节：

对于 Windows 用户，请使用 ^ 而不是 \ 作为多行分隔符和双引号 " 在这些代码示例中。 Windows 用户还需要在标题行中使用至少一个字符空间的缩进。

为了清晰起见，此过程中的一些代码示例已放在 json 文件中，以使命令更易读且更易于使用。

使用 REST API 创建部署后，还可以从 "部署空间 "页面查看部署并向其发送作业。

过程

使用主机集群进行认证，如下所示。
更多信息，请参阅生成 API 密钥。

可选: 如果未如前所述从用户界面获取 SPACE-ID ，那么可以使用 REST API 创建空间，如下所示。在所有 API 调用中，使用 Authorization 头中以词 ZenApiKey 开头的先前获取的令牌。

curl --request POST \
  "https://HOST-CLUSTER-HERE/v2/spaces" \
    -H "Authorization: ZenApiKey <token>" \
    -H "Content-Type: application/json" \
    -d "{"name": "SPACE-NAME-HERE","description": "optional description here"}"

对于 Windows 用户，将 --data-raw 命令放在一行上，并将此命令中的所有 " 替换为 \" ，如下所示:

curl --request POST ^
  "https://HOST-CLUSTER-HERE/v2/spaces" ^
    -H "Authorization: ZenApiKey <token>" ^
    -H "Content-Type: application/json" ^
    -d "{\"name\": \"SPACE-NAME-HERE\",\"description\": \"optional description here\"}"

或者，将数据放在单独的文件中。

SPACE-ID 在 metadata 部分的 id 字段中返回。

输出示例:

{
  "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": "SPACE-NAME",
    "scope": {
      "bss_account_id": "cpdaccount"
    },
    "status": {
      "state": "active"
    }
  },
  "metadata": {
    "created_at": "2025-10-30T16:16:44.121Z",
    "creator_id": "XXXXXXX",
    "id": "SPACE-ID",
    "url": "/v2/spaces/SPACE-ID"
  }
}

您必须等待直到部署空间状态为 "active" ，然后才能继续。您可以按如下所示进行轮询以进行检查。

curl --request GET "https://HOST-CLUSTER-HERE/v2/spaces/SPACE-ID-HERE" \
  -H "Authorization: ZenApiKey <token>" \
  -H "Content-Type: application/json"

创建 新 Decision Optimization 模型

所有 API 请求都需要采用格式为 version=YYYY-MM-DD的日期的版本参数。此代码示例发布使用文件 create_model.json的模型。 URL 将根据您为机器学习服务选择的地区/位置而有所不同。

curl --request POST \
  "https://HOST-CLUSTER-HERE/ml/v4/models?version=2025-08-01" \
  -H "Authorization: ZenApiKey <token>" \
  -H "Content-Type: application/json" \
  -d @create_model.json

create_model.json 文件包含以下代码:

{
  "name": "ModelName",
  "description": "ModelDescription",
  "type": "do-docplex_22.1",
  "software_spec": {
    "name": "do_22.1"
  },
  "custom": {
      "decision_optimization": {
        "oaas.docplex.python": "3.12"
      }
  },
  "space_id": "SPACE-ID-HERE"
}

这里的 "custom块明确说明了Python版本。这是可选的。若未指定，您的模型将使用默认版本，当前默认版本为 Python 3.12。由于默认版本会随着时间的推移而变化，明确说明Python版本可以让您以后轻松更改，或者在默认版本更新时继续使用支持的旧版本。目前支持 Python、 3.12 以及 3.11 （已弃用）。

如果您希望能够 从用户界面运行此模型的作业，而不是仅使用 REST API ，那么必须为输入和输出数据定义模式。如果在创建模型时未定义模式，那么只能使用 REST API 而不是从用户界面运行作业。

您还可以使用为优化模型中的输入和输出指定的模式:

{
  "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"
}

发布模型时，提供有关其 模型类型 和要使用的 软件规范 的信息。

模型类型 可以是，例如:

OPL 模型的 do-opl_22.1
do-cplex_22.1 (对于 CPLEX 模型)
针对 CP 模型的 do-cpo_22.1
do-docplex_22.1 for Python 模型

版本 20.1 也可用于这些模型类型。

对于 软件规范，您可以使用缺省规范的名称 do_22.1 或 do_20.1 (不推荐使用 do_20.1 运行时，并且将很快除去)。另请参阅扩展软件规范笔记本，其中显示了如何扩展 Decision Optimization 软件规范 (带有 DOcplex 模型的其他 Python 库的运行时)。

在 metadata的 id 字段中返回 MODEL-ID 。

输出示例:

{
  "entity": {
    "software_spec": {
      "id": "SOFTWARE-SPEC-ID"
    },
    "type": "do-docplex_22.1"
  },
  "metadata": {
    "created_at": "2025-07-17T08:37:22.992Z",
    "description": "ModelDescription",
    "id": "MODEL-ID",
    "modified_at": "2025-07-17T08:37:22.992Z",
    "name": "ModelName",
    "owner": "***********",
    "space_id": "SPACE-ID"
  },
  "system": {
    "warnings": []
  }
}

上载可供部署的 Decision Optimization 模型阐述 。
首先将模型压缩到 (tar.gz, .zip or .jar) 文件中，然后上载该文件以由 Watson Machine Learning 服务部署。
以下代码示例上载名为 diet.zip 的模型，该模型包含 Python 模型并且没有公共数据:
```
curl --request PUT \
  "https://HOST-CLUSTER-HERE/ml/v4/models/MODEL-ID-HERE/content?version=2025-08-01&space_id=SPACE-ID-HERE&content_format=native" \
  -H "Authorization: ZenApiKey <token>" \
  -H "Content-Type: application/gzip" \
  --data-binary "@diet.zip"
```
您可以从 DO-samples下载此示例和其他模型。选择相关产品和版本子文件夹。对于 watsonx on-premises，请使用 Cloud Pak for Data v5.2.x 子文件夹中的样本。

部署模型

创建对模型的引用。使用 SPACE-ID，在创建可供部署的模型时获取的 MODEL-ID 以及 硬件规范。例如：

curl --request POST "https://HOST-CLUSTER-HERE/ml/v4/deployments?version=2025-08-01" \
  -H "Authorization: ZenApiKey <token>" \
  -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": {}
}

DEPLOYMENT-ID 在 metadata中的 id 字段中返回。输出示例:

{
  "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": "2025-07-17T09:10:50.661Z",
    "description": "",
    "id": "DEPLOYMENT-ID",
    "modified_at": "2025-07-17T09:10:50.661Z",
    "name": "test-Diet-deploy",
    "owner": "**************",
    "space_id": "SPACE-ID"
  }
}

部署后，您可以 监视模型的部署状态。 使用 DEPLOYMENT-ID。

例如：

curl --request GET "https://HOST-CLUSTER-HERE/ml/v4/deployments/DEPLOYMENT-ID-HERE?version=2025-08-01&space_id=SPACE-ID-HERE" \
  -H "Authorization: ZenApiKey <token>" \
  -H "Content-Type: application/json"

输出示例:

{
  "entity": {
    "asset": {
      "id": "MODEL-ID"
    },
    "batch": {

    },
    "custom": {

    },
    "deployed_asset_type": "do",
    "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": "2023-10-25T09:44:19.943Z",
    "id": "DEPLOYMENT-ID",
    "modified_at": "2023-10-25T09:44:19.943Z",
    "name": "Test-Diet-deploy",
    "owner": "**************",
    "space_id": "SPACE-ID"
  }
}

然后，您可以针对定义输入数据和输出 (优化求解的结果) 以及日志文件的已部署模型 提交作业 。

例如，下面显示了名为 myjob.json的文件的内容。它包含（内联）输入数据、一些求解参数，并指定输出为 ".csv文件。有关其他类型输入数据引用的示例，请参阅Decision Optimization批量部署和模型执行。

{
	"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"
			}
		]
	}
}

此代码示例发布使用此文件 myjob.json的作业。

curl --request POST "https://HOST-CLUSTER-HERE/ml/v4/deployment_jobs?version=2025-08-01&space_id=SPACE-ID-HERE" \
  -H "Authorization: ZenApiKey <token>" \
  -H "Content-Type: application/json" \
  -H "cache-control: no-cache" \
  -d @myjob.json

返回 JOB-ID 。输出示例: (作业已排队)

{
  "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": "2025-07-17T10:42:42.783Z",
    "id": "JOB-ID",
    "name": "test-job-diet",
    "space_id": "SPACE-ID"
  }
}

您还可以 监视作业状态。使用 JOB-ID

例如：

curl --request GET \
  "https://HOST-CLUSTER-HERE/ml/v4/deployment_jobs/JOB-ID-HERE?version=2025-08-01&space_id=SPACE-ID-HERE" \
  -H "Authorization: ZenApiKey <token>" \
  -H "Content-Type: application/json"

输出示例: (作业已完成)

{
  "entity": {
    "decision_optimization": {
      "input_data": [{
        "fields": ["name", "unit_cost", "qmin", "qmax"],
        "id": "diet_food.csv",
        "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]]
      }, {
        "fields": ["Food", "Calories", "Calcium", "Iron", "Vit_A", "Dietary_Fiber", "Carbohydrates", "Protein"],
        "id": "diet_food_nutrients.csv",
        "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]]
      }, {
        "fields": ["name", "qmin", "qmax"],
        "id": "diet_nutrients.csv",
        "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_OBJECTIVE_SENSE": "minimize",
          "MODEL_DETAIL_TYPE": "LP",
          "PROGRESS_CURRENT_OBJECTIVE": "2.6904091716962637",
          "STAT.cplex.modelType": "LP",
          "STAT.cplex.size.booleanVariables": "0",
          "STAT.cplex.size.constraints": "7",
          "STAT.cplex.size.continousVariables": "9",
          "STAT.cplex.size.integerVariables": "0",
          "STAT.cplex.size.linearConstraints": "7",
          "STAT.cplex.size.quadraticConstraints": "0",
          "STAT.cplex.size.variables": "9"
        },
        "latest_engine_activity": ["[2025-04-04T16:34:51Z, INFO] Model: diet", 
                                   "[2025-04-04T16:34:51Z, INFO]  - number of variables: 9", 
                                   "[2025-04-04T16:34:51Z, INFO]  - binary=0, integer=0, continuous=9", 
                                   "[2025-04-04T16:34:51Z, INFO]  - number of constraints: 7", 
                                   "[2025-04-04T16:34:51Z, INFO]  - linear=7",
                                   "[2025-04-04T16:34:51Z, INFO]  - parameters: defaults", 
                                   "[2025-04-04T16:34:51Z, INFO]  - objective: minimize", 
                                   "[2025-04-04T16:34:51Z, INFO]  - problem type is: LP",     
                                   "[2025-04-04T16:34:51Z, INFO] Warning: Model: \"diet\" is not a MIP problem, progress listeners are disabled", 
                                   "[2025-04-04T16:34:51Z, INFO] objective: 2.690", 
                                   "[2025-04-04T16:34:51Z, INFO]   \"Spaghetti W/ Sauce\"=2.155", 
                                   "[2025-04-04T16:34:51Z, INFO]   \"Chocolate Chip Cookies\"=10.000", 
                                   "[2025-04-04T16:34:51Z, INFO]   \"Lowfat Milk\"=1.831", 
                                   "[2025-04-04T16:34:51Z, INFO]   \"Hotdog\"=0.930", 
                                   "[2025-04-04T16:34:51Z, INFO] solution.csv"
                                   ],
        "solve_status": "optimal_solution"
      },
      "status": {
        "completed_at": "2025-04-04T16:34:51.796Z",
        "running_at": "2025-04-04T16:34:50.592Z",
        "state": "completed"
      }
    },
    "deployment": {
      "id": "DEPLOYMENT ID"
    },
    "platform_job": {
      "job_id": "PLATFORM JOB ID",
      "run_id": "PLATFORM RUN ID"
    }
  },
  "metadata": {
    "created_at": "2025-04-04T16:34:30.603Z",
    "id": "JOB-ID",
    "modified_at": "2025-04-04T16:34:51.881Z",
    "name": "test-job-diet",
    "space_id": "SPACE-ID"
  }
}

可选: 您可以 删除作业 ，如下所示:

curl --request DELETE "https://HOST-CLUSTER-HERE/ml/v4/deployment_jobs/JOB-ID-HERE?version=2025-08-01&space_id=SPACE-ID-HERE&hard_delete=true" \
  -H "Authorization: ZenApiKey <token>"

如果使用 API 删除作业，那么该作业仍将显示在用户界面中。

可选: 您可以 删除部署 ，如下所示:

curl --request DELETE "https://HOST-CLUSTER-HERE/ml/v4/deployments/DEPLOYMENT-ID-HERE?version=2025-08-01&space_id=SPACE-ID-HERE" \
  -H "Authorization: ZenApiKey <token>"

如果使用 API 删除包含作业的部署，那么这些作业仍将显示在用户界面的部署空间中。

结果

部署模型并执行作业后，将以内联方式或在您指定的文件和位置 (例如，使用 S3 引用) 中提供解决方案结果。您可以使用 deployment-ID 发布新作业，而不必重新部署模型。