Using PyTorch to Create a Training Job (New-Version Training)

This section describes how to train a model by calling ModelArts APIs.

Overview

The process for creating a training job using PyTorch is as follows:

  1. , which will be added in a request header for authentication.
  2. Call the API for obtaining general flavors supported by a training job to obtain the required flavors.
  3. Call the API for obtaining the preset AI frameworks supported by a training job to view the engines and their versions supported by a training job.
  4. Call the API for creating an algorithm to create an algorithm and record the algorithm ID.
  5. Call the API for creating a training job to create a training job using the UUID returned by the created algorithm and record the job ID.
  6. Call the API for querying details about a training job to query the job status using the job ID.
  7. Call the API for querying the logs of a specified task in a training job (OBS link) to obtain the OBS path of the training job logs.
  8. Call the API for querying the running metrics of a specified task in a training job to view detailed metrics of the job.
  9. Call the API for deleting a training job to delete the job if it is no longer needed.

Prerequisites

Procedure

  1. Call the API for obtaining general flavors supported by a training job to obtain the required flavors.
    1. Request body:

      URI: GET https://{ma_endpoint}/v2/{project_id}/ training-job-flavors? flavor_type=CPU

      Request header: X-Auth-Token →MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX...

      Set the following parameters based on site requirements:

      • ma_endpoint: ModelArts endpoint
      • project_id: user's project ID
      • X-auth-Token: token obtained in the previous step
    2. Status code 200 is returned. The response body is as follows:
      {
  "total_count": 2,
  "flavors": [
    {
      "flavor_id": "modelarts.vm.cpu.2u",
      "flavor_name": "Computing CPU(2U) instance",
      "flavor_type": "CPU",
      "billing": {
        "code": "modelarts.vm.cpu.2u",
        "unit_num": 1
      },
      "flavor_info": {
        "max_num": 1,
        "cpu": {
          "arch": "x86",
          "core_num": 2
        },
        "memory": {
          "size": 8,
          "unit": "GB"
        },
        "disk": {
          "size": 50,
          "unit": "GB"
        }
      }
    },
    {
      "flavor_id": "modelarts.vm.cpu.8u",
      "flavor_name": "Computing CPU(8U) instance",
      "flavor_type": "CPU",
      "billing": {
        "code": "modelarts.vm.cpu.8u",
        "unit_num": 1
      },
      "flavor_info": {
        "max_num": 16,
        "cpu": {
          "arch": "x86",
          "core_num": 8
        },
        "memory": {
          "size": 32,
          "unit": "GB"
        },
        "disk": {
          "size": 50,
          "unit": "GB"
        }
      }
    }
  ]
}
      • Select and record the flavor required for creating the training job based on the flavor_id value. This section uses flavor modelarts.vm.cpu.8u with its max_num set to 16 as an example.
  2. Call the API for obtaining the preset AI frameworks supported by a training job to view the engines and their versions supported by a training job.
    1. Request body:

      URI: GET https://{ma_endpoint}/v2/{project_id}/job/ training-job-engines

      Request header:

      X-Auth-Token→MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX...

      Content-Type →application/json

      Set the bold parameters based on site requirements.

    2. Status code 200 is returned. The response body is as follows (only part of the response body is displayed because there are many engines):
      {
    "total": 28,
    "items": [
        ......
        {
            "engine_id": "mindspore_1.6.0-cann_5.0.3.6-py_3.7-euler_2.8.3-aarch64",
            "engine_name": "Ascend-Powered-Engine",
            "engine_version": "mindspore_1.6.0-cann_5.0.3.6-py_3.7-euler_2.8.3-aarch64",
            "v1_compatible": false,
            "run_user": "1000",
            "image_info": {
                "cpu_image_url": "",
                "gpu_image_url": "atelier/mindspore_1_6_0:train",
                "image_version": "mindspore_1.6.0-cann_5.0.3.6-py_3.7-euler_2.8.3-aarch64-snt9-roma-20211231193205-33131ee"
            }
        },
		......
        {
            "engine_id": "pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64",
            "engine_name": "PyTorch",
            "engine_version": "pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64",
            "tags": [
                {
                    "key": "auto_search",
                    "value": "True"
                }
            ],
            "v1_compatible": false,
            "run_user": "1102",
            "image_info": {
                "cpu_image_url": "aip/pytorch_1_8:train",
                "gpu_image_url": "aip/pytorch_1_8:train",
                "image_version": "pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64-20210912152543-1e0838d"
            }
        },
        ......
        {
            "engine_id": "tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64",
            "engine_name": "TensorFlow",
            "engine_version": "tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64",
            "tags": [
                {
                    "key": "auto_search",
                    "value": "True"
                }
            ],
            "v1_compatible": false,
            "run_user": "1102",
            "image_info": {
                "cpu_image_url": "aip/tensorflow_2_1:train",
                "gpu_image_url": "aip/tensorflow_2_1:train",
                "image_version": "tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64-20210912152543-1e0838d"
            }
        },
        ......
    ]
}

      Select the engine flavor required for creating a training job based on the engine_name and engine_version fields, and record the field values. This section uses the PyTorch engine as an example to describe how to create a job. In this example, the engine_name value is PyTorch, and the engine_version value is pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64.

  3. Call the API for creating an algorithm to create an algorithm and record the algorithm ID.
    1. Request body:

      URI: POST https://{ma_endpoint}/v2/{project_id}/ algorithms

      Request header:

      X-Auth-Token→MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX...

      Content-Type →application/json

      Set the bold parameters based on site requirements.

      Request body:

      {
	"metadata": {
		"name": "test-pytorch-cpu",
		"description": "test pytorch job in cpu in mode gloo"
	},
	"job_config": {
		"boot_file": "/xxxxxx-job-test-v2/pytorch/fast_example/code/cpu/test-pytorch.py",
		"code_dir": "/xxxxxx-job-test-v2/pytorch/fast_example/code/cpu/",
		"engine": {
			"engine_name": "PyTorch",
			"engine_version": "pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64"
		},
		"inputs": [{
			"name": "data_url",
			"description": "Data source 1"
		}],
		"outputs": [{
			"name": "train_url",
			"description": "Output data 1"
		}],
		"parameters": [{
				"name": "dist",
				"description": "",
				"value": "False",
				"constraint": {
					"editable": true,
					"required": false,
					"sensitive": false,
					"type": "Boolean",
					"valid_range": [],
					"valid_type": "None"
				}
			},
			{
				"name": "world_size",
				"description": "",
				"value": "1",
				"constraint": {
					"editable": true,
					"required": false,
					"sensitive": false,
					"type": "Integer",
					"valid_range": [],
					"valid_type": "None"
				}
			}
		],
		"parameters_customization": true
	},
	"resource_requirements": []
}

      Set the following parameters based on site requirements:

      • name and description in the metadata field indicate the algorithm name and description, respectively.
      • code_dir and boot_file in the job_config field indicate the code directory and code startup file of the algorithm, respectively. The code directory is the level-1 directory of the code startup file.
      • inputs and outputs in the job_config field indicate the input and output of the algorithm, respectively. You can specify data_url and train_url based on the instance, and parse hyperparameters in the code to specify the local path of the data file required for training and the local output path of the model generated during training.
      • parameters_customization in the job_config field indicates whether to support custom hyperparameters. Set this parameter to true.
      • parameters in the job_config field indicates the hyperparameters of the algorithm. Set name to the hyperparameter name (a maximum of 64 characters, including uppercase letters, lowercase letters, digits, underscores (_), and hyphens (-)). Set value to the default value of the hyperparameter. Set constraint to the constraints of the hyperparameter. For example, set type to String (String, Integer, Float, and Boolean are supported), set editable to true, and set required to false.
      • engine in the job_config field indicates the engine on which the algorithm depends. Use the engine_name and engine_version values recorded in 2.
    2. Status code 200 OK is returned, indicating that the algorithm is successfully created. The response body is as follows:
      {
    "metadata": {
        "id": "01c399ae-8593-4ef5-9e4d-085950aacde1",
        "name": "test-pytorch-cpu",
        "description": "test pytorch job in cpu in mode gloo",
        "create_time": 1641890623262,
        "workspace_id": "0",
        "ai_project": "default-ai-project",
        "user_name": "",
        "domain_id": "0659fbf6de00109b0ff1c01fc037d240",
        "source": "custom",
        "api_version": "",
        "is_valid": true,
        "state": "",
        "size": 4790,
        "tags": null,
        "attr_list": null,
        "version_num": 0,
        "update_time": 0
    },
    "share_info": {},
    "job_config": {
        "code_dir": "/xxxxxx-job-test-v2/pytorch/fast_example/code/cpu/",
        "boot_file": "/xxxxxx-job-test-v2/pytorch/fast_example/code/cpu/test-pytorch.py",
        "parameters": [
            {
                "name": "dist",
                "description": "",
                "i18n_description": null,
                "value": "False",
                "constraint": {
                    "type": "Boolean",
                    "editable": true,
                    "required": false,
                    "sensitive": false,
                    "valid_type": "None",
                    "valid_range": []
                }
            },
            {
                "name": "world_size",
                "description": "",
                "i18n_description": null,
                "value": "1",
                "constraint": {
                    "type": "Integer",
                    "editable": true,
                    "required": false,
                    "sensitive": false,
                    "valid_type": "None",
                    "valid_range": []
                }
            }
        ],
        "parameters_customization": true,
        "inputs": [
            {
                "name": "data_url",
                "description": "Data source 1"
            }
        ],
        "outputs": [
            {
                "name": "train_url",
                "description": "Output data 1"
            }
        ],
        "engine": {
            "engine_id": "pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64",
            "engine_name": "PyTorch",
            "engine_version": "pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64",
            "tags": [
                {
                    "key": "auto_search",
                    "value": "True"
                }
            ],
            "v1_compatible": false,
            "run_user": "1102",
            "image_info": {
                "cpu_image_url": "aip/pytorch_1_8:train",
                "gpu_image_url": "aip/pytorch_1_8:train",
                "image_version": "pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64-20210912152543-1e0838d"
            }
        },
        "code_tree": {
            "name": "cpu/",
            "children": [
                {
                    "name": "test-pytorch.py"
                }
            ]
        }
    },
    "resource_requirements": [],
    "advanced_config": {}
}

      Record the value of id (algorithm ID, 32-bit UUID) in the metadata field for subsequent steps.

  4. Call the API for creating a training job to create a training job using the UUID returned by the created algorithm and record the job ID.
    1. Request body:

      URI: POST https://{ma_endpoint}/v2/{project_id}/training-jobs

      Request header:

      • X-Auth-Token →MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX...
      • Content-Type →application/json

      Set the bold parameters based on site requirements.

      Request body:

      {
	"kind": "job",
	"metadata": {
		"name": "test-pytorch-cpu01",
		"description": "test pytorch work cpu in mode gloo"
	},
	"algorithm": {
		"id": "01c399ae-8593-4ef5-9e4d-085950aacde1",
		"parameters": [{
				"name": "dist",
				"value": "False"
			},
			{
				"name": "world_size",
				"value": "1"
			}
		],
		"inputs": [{
			"name": "data_url",
			"remote": {
				"obs": {
					"obs_url": "/xxxxxx-job-test-v2/pytorch/fast_example/data/"
				}
			}
		}],
		"outputs": [{
			"name": "train_url",
			"remote": {
				"obs": {
					"obs_url": "/xxxxxx-job-test-v2/pytorch/fast_example/outputs/"
				}
			}
		}]
	},
	"spec": {
		"resource": {
			"flavor_id": "modelarts.vm.cpu.8u",
			"node_count": 1
		},
		"log_export_path": {
			"obs_url": "/xxxxxx-job-test-v2/pytorch/fast_example/log/"
		}
	}
}

      Set the following parameters based on site requirements:

      • Set kind to the type of the training job. The default value is job.
      • Set name and description in the metadata field to the name and description of the training job.
      • Set id in the algorithm field to the algorithm ID obtained in 4.
      • Set inputs and outputs in the algorithm field to the information about the input and output URLs of the training job. In this example, obs_url in remote of the inputs parameter indicates the OBS path for selecting the training data from the OBS bucket. obs_url in remote of the outputs parameter indicates the OBS path for storing the training output.
      • Set flavor_id in the spec field to the flavor on which the training job depends. Use the flavor_id recorded in 1. node_count indicates whether to use multi-node training (distributed training). Set it to 1 for a single-node training by default. log_export_path specifies the OBS path to which logs are uploaded.
    2. Status code 201 Created is returned, indicating that the training job has been created. The response body is as follows:
      {
    "kind": "job",
    "metadata": {
        "id": "66ff6991-fd66-40b6-8101-0829a46d3731",
        "name": "test-pytorch-cpu01",
        "description": "test pytorch work cpu in mode gloo",
        "create_time": 1641892642625,
        "workspace_id": "0",
        "ai_project": "default-ai-project",
        "user_name": "",
        "annotations": {
            "job_template": "Template DL",
            "key_task": "worker"
        }
    },
    "status": {
        "phase": "Creating",
        "secondary_phase": "Creating",
        "duration": 0,
        "start_time": 0,
        "node_count_metrics": null,
        "tasks": [
            "worker-0"
        ]
    },
    "algorithm": {
        "id": "01c399ae-8593-4ef5-9e4d-085950aacde1",
        "name": "test-pytorch-cpu",
        "code_dir": "/xxxxxx-job-test-v2/pytorch/fast_example/code/cpu/",
        "boot_file": "/xxxxxx-job-test-v2/pytorch/fast_example/code/cpu/test-pytorch.py",
        "parameters": [
            {
                "name": "dist",
                "description": "",
                "i18n_description": null,
                "value": "False",
                "constraint": {
                    "type": "Boolean",
                    "editable": true,
                    "required": false,
                    "sensitive": false,
                    "valid_type": "None",
                    "valid_range": []
                }
            },
            {
                "name": "world_size",
                "description": "",
                "i18n_description": null,
                "value": "1",
                "constraint": {
                    "type": "Integer",
                    "editable": true,
                    "required": false,
                    "sensitive": false,
                    "valid_type": "None",
                    "valid_range": []
                }
            }
        ],
        "parameters_customization": true,
        "inputs": [
            {
                "name": "data_url",
                "description": "Data source 1",
                "local_dir": "/home/ma-user/modelarts/inputs/data_url_0",
                "remote": {
                    "obs": {
                        "obs_url": "/xxxxxx-job-test-v2/pytorch/fast_example/data/"
                    }
                }
            }
        ],
        "outputs": [
            {
                "name": "train_url",
                "description": "Output data 1",
                "local_dir": "/home/ma-user/modelarts/outputs/train_url_0",
                "remote": {
                    "obs": {
                        "obs_url": "/xxxxxx-job-test-v2/pytorch/fast_example/outputs/"
                    }
                },
                "mode": "upload_periodically",
                "period": 30
            }
        ],
        "engine": {
            "engine_id": "pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64",
            "engine_name": "PyTorch",
            "engine_version": "pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64",
            "usage": "training",
            "support_groups": "public",
            "tags": [
                {
                    "key": "auto_search",
                    "value": "True"
                }
            ],
            "v1_compatible": false,
            "run_user": "1102"
        }
    },
    "spec": {
        "resource": {
            "flavor_id": "modelarts.vm.cpu.8u",
            "flavor_name": "Computing CPU(8U) instance",
            "node_count": 1,
            "flavor_detail": {
                "flavor_type": "CPU",
                "billing": {
                    "code": "modelarts.vm.cpu.8u",
                    "unit_num": 1
                },
                "flavor_info": {
                    "cpu": {
                        "arch": "x86",
                        "core_num": 8
                    },
                    "memory": {
                        "size": 32,
                        "unit": "GB"
                    },
                    "disk": {
                        "size": 50,
                        "unit": "GB"
                    }
                }
            }
        },
        "log_export_path": {
            "obs_url": "/xxxxxx-job-test-v2/pytorch/fast_example/log/"
        },
        "is_hosted_log": true
    }
}
      • Record the id value (training job ID) in the metadata field for subsequent steps.
      • phase and secondary_phase under Status indicate the status and next status of the training job, respectively. In the example, Creating indicates that the training job is being created.
  5. Call the API for querying details about a training job to query the job status using the job ID.
    1. Request body:

      URI: GET https://{ma_endpoint}/v2/{project_id}/training-jobs/{training_job_id}

      Request header: X-Auth-Token →MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX...

      Set the following parameter based on site requirements:

      Set training_job_id to the training job ID recorded in 5.

    2. Status code 200 OK is returned. The response body is as follows:
      {
    "kind": "job",
    "metadata": {
        "id": "66ff6991-fd66-40b6-8101-0829a46d3731",
        "name": "test-pytorch-cpu01",
        "description": "test pytorch work cpu in mode gloo",
        "create_time": 1641892642625,
        "workspace_id": "0",
        "ai_project": "default-ai-project",
        "user_name": "hwstaff_z00424192",
        "annotations": {
            "job_template": "Template DL",
            "key_task": "worker"
        }
    },
    "status": {
        "phase": "Running",
        "secondary_phase": "Running",
        "duration": 268000,
        "start_time": 1641892655000,
        "node_count_metrics": [
            [
                1641892645000,
                0
            ],
            [
                1641892654000,
                0
            ],
            [
                1641892655000,
                1
            ],
            [
                1641892922000,
                1
            ],
            [
                1641892923000,
                1
            ]
        ],
        "tasks": [
            "worker-0"
        ]
    },
    "algorithm": {
        "id": "01c399ae-8593-4ef5-9e4d-085950aacde1",
        "name": "test-pytorch-cpu",
        "code_dir": "/xxxxxx-job-test-v2/pytorch/fast_example/code/cpu/",
        "boot_file": "/xxxxxx-job-test-v2/pytorch/fast_example/code/cpu/test-pytorch.py",
        "parameters": [
            {
                "name": "dist",
                "description": "",
                "i18n_description": null,
                "value": "False",
                "constraint": {
                    "type": "Boolean",
                    "editable": true,
                    "required": false,
                    "sensitive": false,
                    "valid_type": "None",
                    "valid_range": []
                }
            },
            {
                "name": "world_size",
                "description": "",
                "i18n_description": null,
                "value": "1",
                "constraint": {
                    "type": "Integer",
                    "editable": true,
                    "required": false,
                    "sensitive": false,
                    "valid_type": "None",
                    "valid_range": []
                }
            }
        ],
        "parameters_customization": true,
        "inputs": [
            {
                "name": "data_url",
                "description": "Data source 1",
                "local_dir": "/home/ma-user/modelarts/inputs/data_url_0",
                "remote": {
                    "obs": {
                        "obs_url": "/xxxxxx-job-test-v2/pytorch/fast_example/data/"
                    }
                }
            }
        ],
        "outputs": [
            {
                "name": "train_url",
                "description": "Output data 1",
                "local_dir": "/home/ma-user/modelarts/outputs/train_url_0",
                "remote": {
                    "obs": {
                        "obs_url": "/xxxxxx-job-test-v2/pytorch/fast_example/outputs/"
                    }
                },
                "mode": "upload_periodically",
                "period": 30
            }
        ],
        "engine": {
            "engine_id": "pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64",
            "engine_name": "PyTorch",
            "engine_version": "pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64",
            "usage": "training",
            "support_groups": "public",
            "tags": [
                {
                    "key": "auto_search",
                    "value": "True"
                }
            ],
            "v1_compatible": false,
            "run_user": "1102"
        }
    },
    "spec": {
        "resource": {
            "flavor_id": "modelarts.vm.cpu.8u",
            "flavor_name": "Computing CPU(8U) instance",
            "node_count": 1,
            "flavor_detail": {
                "flavor_type": "CPU",
                "billing": {
                    "code": "modelarts.vm.cpu.8u",
                    "unit_num": 1
                },
                "flavor_info": {
                    "cpu": {
                        "arch": "x86",
                        "core_num": 8
                    },
                    "memory": {
                        "size": 32,
                        "unit": "GB"
                    },
                    "disk": {
                        "size": 50,
                        "unit": "GB"
                    }
                }
            }
        },
        "log_export_path": {
            "obs_url": "/xxxxxx-job-test-v2/pytorch/fast_example/log/"
        },
        "is_hosted_log": true
    }
}

      You can learn about the version details of the training job based on the response. The status value is Running, indicating that the training job is running.

  6. Call the API for querying the logs of a specified task in a training job (OBS link) to obtain the OBS path of the training job logs.
    1. Request body:

      URI format: GET https://{ma_endpoint}/v2/{project_id}/training-jobs/{training_job_id}/tasks/{task_id}/logs/url

      Request header:

      X-Auth-Token→MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX...

      Content-Type→text/plain

      Set the following parameters based on site requirements:

      • task_id indicates the name of the training job. Generally, set it to work-0.
      • Content-Type can be set either to text/plain or application/octet-stream. text/plain indicates that a temporary OBS preview URL is returned. application/octet-stream indicates that a temporary OBS download URL is returned.
    2. Status code 200 OK is returned. The response body is as follows:
      {
    "obs_url": "https://modelarts-training-log.com:443/66ff6991-fd66-40b6-8101-0829a46d3731/worker-0/modelarts-job-66ff6991-fd66-40b6-8101-0829a46d3731-worker-0.log?AWSAccessKeyId=GFGTBKOZENDD83QEMZMV&Expires=1641896599&Signature=BedFZHEU1oCmqlI912UL9mXlhkg%3D"
}

      The returned field indicates the OBS path of logs. You can copy the value to the browser to view the result.

  7. Call the API for querying the running metrics of a specified task in a training job to view detailed metrics of the job.
    1. Request body:

      URI format: GET https://{ma_endpoint}/v2/{project_id}/training-jobs/{training_job_id}/metrics/{task_id}

      Request header: X-Auth-Token →MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX...

      Set the bold parameters based on site requirements.

    2. Status code 200 OK is returned. The response body is as follows:
      {
    "metrics": [
        {
            "metric": "cpuUsage",
            "value": [
                -1,
                -1,
                28.622,
                35.053,
                39.988,
                40.069,
                40.082,
                40.094
            ]
        },
        {
            "metric": "memUsage",
            "value": [
                -1,
                -1,
                0.544,
                0.641,
                0.736,
                0.737,
                0.738,
                0.739
            ]
        },
        {
            "metric": "npuUtil",
            "value": [
                -1,
                -1,
                -1,
                -1,
                -1,
                -1,
                -1,
                -1
            ]
        },
        {
            "metric": "npuMemUsage",
            "value": [
                -1,
                -1,
                -1,
                -1,
                -1,
                -1,
                -1,
                -1
            ]
        },
        {
            "metric": "gpuUtil",
            "value": [
                -1,
                -1,
                -1,
                -1,
                -1,
                -1,
                -1,
                -1
            ]
        },
        {
            "metric": "gpuMemUsage",
            "value": [
                -1,
                -1,
                -1,
                -1,
                -1,
                -1,
                -1,
                -1
            ]
        }
    ]
}

      You can view the metrics such as the CPU usage.

  8. Call the API for deleting a training job to delete the job if it is no longer needed.
    1. Request body:

      URI: DELETE https://{ma_endpoint}/v2/{project_id}/training-jobs/{training_job_id}

      Request header: X-Auth-Token →MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX...

      Set the bold parameters based on site requirements.

    2. Status code 202 No Content is returned, indicating that the job is successfully deleted.
Parent topic: Use Cases