使用 Vertex AI API 训练 AutoML Edge 模型

您可以在 Google Cloud 控制台中直接创建 AutoML 模型，也可以通过使用 API 或某个 Vertex AI 客户端库以编程方式创建训练流水线。

此模型是使用您通过控制台或 API 提供且准备好的数据集创建的。Vertex AI API 使用数据集中的项来训练、测试模型并评估模型性能。查看评估结果，根据需要调整训练数据集，并使用改进的数据集创建新的训练流水线。

模型训练可能需要几个小时才能完成。通过 Vertex AI API，您可以获取训练状态。

创建 AutoML Edge 训练流水线

如果您有一个包含一组代表性训练项的数据集，就可以创建 AutoML Edge 训练流水线了。

选择数据类型。

图片

在下面选择您的目标对应的标签页：

REST

在使用任何请求数据之前，请先进行以下替换：

LOCATION：数据集所在且模型在其中创建的区域。例如 us-central1。
PROJECT：您的项目 ID。
TRAININGPIPELINE_DISPLAYNAME：必填。trainingPipeline 的显示名。
DATASET_ID：用于训练的数据集的 ID 编号。
fractionSplit：可选。数据的多个可能的机器学习用途拆分选项之一。对于 fractionSplit，值的总和必须为 1。例如：
- {"trainingFraction": "0.7","validationFraction": "0.15","testFraction": "0.15"}
MODEL_DISPLAYNAME^*：TrainingPipeline 上传（创建）的模型的显示名。
MODEL_DESCRIPTION^*：模型的说明。
modelToUpload.labels^*：用于组织模型的任何键值对。例如：
- "env": "prod"
- "tier": "backend"
EDGE_MODELTYPE^†：要训练的 Edge 模型的类型。选项包括：
- MOBILE_TF_LOW_LATENCY_1
- MOBILE_TF_VERSATILE_1
- MOBILE_TF_HIGH_ACCURACY_1
NODE_HOUR_BUDGET^†：实际训练费用将等于或小于此值。对于 Edge 模型，预算必须为 1,000 至 10 万毫节点时（含边界值）。
PROJECT_NUMBER：自动生成的项目编号

HTTP 方法和网址：

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines

请求 JSON 正文：

{
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": "DECIMAL",
      "validationFraction": "DECIMAL",
      "testFraction": "DECIMAL"
    }
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAYNAME",
    "description": "MODEL_DESCRIPTION",
    "labels": {
      "KEY": "VALUE"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "multiLabel": "false",
    "modelType": ["EDGE_MODELTYPE"],
    "budgetMilliNodeHours": NODE_HOUR_BUDGET
  }
}

如需发送请求，请选择以下方式之一：

curl

注意：以下命令假定您已使用您的用户账号通过运行 gcloud init 或 gcloud auth login 登录 gcloud CLI，或者使用了 Cloud Shell，这会使您自动登录 gcloud CLI。您可以运行 gcloud auth list 来检查当前活跃的账号。

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines"

PowerShell

注意：以下命令假定您已登录gcloud请使用您的用户账号来运行以下命令：gcloud init 或 gcloud auth login . 您可以运行 gcloud auth list 来检查当前活跃的账号。

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

响应包含有关规范的信息以及 TRAININGPIPELINE_ID。

响应

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.7,
      "validationFraction": 0.15,
      "testFraction": 0.15
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": "EDGE_MODELTYPE",
    "budgetMilliNodeHours": "NODE_HOUR_BUDGET"
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAYNAME",
    "labels": {
      "KEY1": "VALUE1",
      "KEY2": "VALUE2"
    }
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-07-14T17:16:55.098953Z",
  "updateTime": "2020-07-14T17:16:55.098953Z"
}

您可以使用 TRAININGPIPELINE_ID 获取 trainingPipeline 作业的状态。

REST

在使用任何请求数据之前，请先进行以下替换：

LOCATION：数据集所在且模型在其中创建的区域。例如 us-central1。
PROJECT：您的项目 ID。
TRAININGPIPELINE_DISPLAYNAME：必填。trainingPipeline 的显示名。
DATASET_ID：用于训练的数据集的 ID 编号。
fractionSplit：可选。数据的多个可能的机器学习用途拆分选项之一。对于 fractionSplit，值的总和必须为 1。例如：
- {"trainingFraction": "0.7","validationFraction": "0.15","testFraction": "0.15"}
MODEL_DISPLAYNAME^*：TrainingPipeline 上传（创建）的模型的显示名。
MODEL_DESCRIPTION^*：模型的说明。
modelToUpload.labels^*：用于组织模型的任何键值对。例如：
- "env": "prod"
- "tier": "backend"
EDGE_MODELTYPE^†：要训练的 Edge 模型的类型。选项包括：
- MOBILE_TF_LOW_LATENCY_1
- MOBILE_TF_VERSATILE_1
- MOBILE_TF_HIGH_ACCURACY_1
NODE_HOUR_BUDGET^†：实际训练费用将等于或小于此值。对于 Edge 模型，预算必须为 1,000 至 10 万毫节点时（含边界值）。
PROJECT_NUMBER：自动生成的项目编号

HTTP 方法和网址：

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines

请求 JSON 正文：

{
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": "DECIMAL",
      "validationFraction": "DECIMAL",
      "testFraction": "DECIMAL"
    }
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAYNAME",
    "description": "MODEL_DESCRIPTION",
    "labels": {
      "KEY": "VALUE"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "multiLabel": "true",
    "modelType": ["EDGE_MODELTYPE"],
    "budgetMilliNodeHours": NODE_HOUR_BUDGET
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines"

PowerShell

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

响应包含有关规范的信息以及 TRAININGPIPELINE_ID。

响应

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.7,
      "validationFraction": 0.15,
      "testFraction": 0.15
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": "EDGE_MODELTYPE",
    "budgetMilliNodeHours": "NODE_HOUR_BUDGET"
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAYNAME",
    "labels": {
      "KEY1": "VALUE1",
      "KEY2": "VALUE2"
    }
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-07-14T17:16:55.098953Z",
  "updateTime": "2020-07-14T17:16:55.098953Z"
}

您可以使用 TRAININGPIPELINE_ID 获取 trainingPipeline 作业的状态。

对象检测

训练时，您可以根据具体使用场景选择所需的 AutoML Edge 模型类型：

低延时 (MOBILE_TF_LOW_LATENCY_1)
通用目的 (MOBILE_TF_VERSATILE_1)
更高预测质量 (MOBILE_TF_HIGH_ACCURACY_1)

在下面选择您的语言或环境对应的标签页：

REST

在使用任何请求数据之前，请先进行以下替换：

LOCATION：数据集所在且模型在其中创建的区域。例如 us-central1。
PROJECT：您的项目 ID。
TRAININGPIPELINE_DISPLAYNAME：必填。trainingPipeline 的显示名。
DATASET_ID：用于训练的数据集的 ID 编号。
fractionSplit：可选。数据的多个可能的机器学习用途拆分选项之一。对于 fractionSplit，值的总和必须为 1。例如：
- {"trainingFraction": "0.7","validationFraction": "0.15","testFraction": "0.15"}
MODEL_DISPLAYNAME^*：TrainingPipeline 上传（创建）的模型的显示名。
MODEL_DESCRIPTION^*：模型的说明。
modelToUpload.labels^*：用于组织模型的任何键值对。例如：
- "env": "prod"
- "tier": "backend"
EDGE_MODELTYPE^†：要训练的 Edge 模型的类型。选项包括：
- MOBILE_TF_LOW_LATENCY_1
- MOBILE_TF_VERSATILE_1
- MOBILE_TF_HIGH_ACCURACY_1
NODE_HOUR_BUDGET^†：实际训练费用将等于或小于此值。对于 Cloud 模型，预算必须为 2 万至 90 万毫节点时（含边界值）。默认值为 216,000，代表实际用时一天（假设使用 9 个节点）。
PROJECT_NUMBER：自动生成的项目编号

HTTP 方法和网址：

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines

请求 JSON 正文：

{
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": "DECIMAL",
      "validationFraction": "DECIMAL",
      "testFraction": "DECIMAL"
    }
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAYNAME",
    "description": "MODEL_DESCRIPTION",
    "labels": {
      "KEY": "VALUE"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_object_detection_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": ["EDGE_MODELTYPE"],
    "budgetMilliNodeHours": NODE_HOUR_BUDGET
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines"

PowerShell

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

响应包含有关规范的信息以及 TRAININGPIPELINE_ID。

响应

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.7,
      "validationFraction": 0.15,
      "testFraction": 0.15
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_object_detection_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": "EDGE_MODELTYPE",
    "budgetMilliNodeHours": "NODE_HOUR_BUDGET"
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAYNAME",
    "labels": {
      "KEY1": "VALUE1",
      "KEY2": "VALUE2"
    }
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-07-14T17:16:55.098953Z",
  "updateTime": "2020-07-14T17:16:55.098953Z"
}

您可以使用 TRAININGPIPELINE_ID 获取 trainingPipeline 作业的状态。

视频

在下面选择您的目标对应的标签页：

动作识别

在训练时，选择以下 AutoML Edge 类型：

MOBILE_VERSATILE_1：通用目的

REST

在使用任何请求数据之前，请先进行以下替换：

PROJECT：您的项目 ID。
LOCATION：数据集所在且模型在其中创建的区域。例如 us-central1。
TRAINING_PIPELINE_DISPLAY_NAME：必填。TrainingPipeline 的显示名。
DATASET_ID：训练数据集的 ID。
TRAINING_FRACTION、TEST_FRACTION：fractionSplit 对象是可选的；您使用它来控制数据拆分。如需详细了解如何控制数据拆分，请参阅 AutoML 模型的数据拆分简介。例如：
- {"trainingFraction": "0.8","validationFraction": "0","testFraction": "0.2"}
MODEL_DISPLAY_NAME：经过训练的模型的显示名。
MODEL_DESCRIPTION：模型的说明。
MODEL_LABELS：用于组织模型的任何键值对。例如：
- "env": "prod"
- "tier": "backend"
EDGE_MODEL_TYPE：
- MOBILE_VERSATILE_1：通用目的
PROJECT_NUMBER：您项目的自动生成的项目编号

HTTP 方法和网址：

POST https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines

请求 JSON 正文：

{
  "displayName": "TRAINING_PIPELINE_DISPLAY_NAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": "TRAINING_FRACTION",
      "validationFraction": "0",
      "testFraction": "TEST_FRACTION"
    }
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAY_NAME",
    "description": "MODEL_DESCRIPTION",
    "labels": {
      "KEY": "VALUE"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_action_recognition_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": ["EDGE_MODEL_TYPE"],
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines"

PowerShell

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

响应包含有关规范的信息以及 TRAININGPIPELINE_ID。

响应

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.8,
      "validationFraction": 0,
      "testFraction": 0.2
    }
  },
  "trainingTaskDefinition": OBJECTIVE,
  "trainingTaskInputs": {
    "modelType": "EDGE_MODEL_TYPE",
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAY_NAME",
    "labels": {
      "KEY1": "VALUE1",
      "KEY2": "VALUE2"
    }
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-11-14T17:16:55.098953Z",
  "updateTime": "2020-11-14T17:16:55.098953Z"
}

您可以获取 trainingPipeline 进度的状态，以了解训练完成时间。

REST

在使用任何请求数据之前，请先进行以下替换：

PROJECT：您的项目 ID。
LOCATION：数据集所在且模型在其中创建的区域。例如 us-central1。
TRAINING_PIPELINE_DISPLAY_NAME：必填。TrainingPipeline 的显示名。
DATASET_ID：训练数据集的 ID。
TRAINING_FRACTION、TEST_FRACTION：fractionSplit 对象是可选的；您使用它来控制数据拆分。如需详细了解如何控制数据拆分，请参阅 AutoML 模型的数据拆分简介。例如：
- {"trainingFraction": "0.8","validationFraction": "0","testFraction": "0.2"}
MODEL_DISPLAY_NAME：经过训练的模型的显示名。
MODEL_DESCRIPTION：模型的说明。
MODEL_LABELS：用于组织模型的任何键值对。例如：
- "env": "prod"
- "tier": "backend"
EDGE_MODEL_TYPE：
- MOBILE_VERSATILE_1：通用目的
PROJECT_NUMBER：您项目的自动生成的项目编号

HTTP 方法和网址：

POST https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines

请求 JSON 正文：

{
  "displayName": "TRAINING_PIPELINE_DISPLAY_NAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": "TRAINING_FRACTION",
      "validationFraction": "0",
      "testFraction": "TEST_FRACTION"
    }
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAY_NAME",
    "description": "MODEL_DESCRIPTION",
    "labels": {
      "KEY": "VALUE"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": ["EDGE_MODEL_TYPE"],
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines"

PowerShell

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

响应包含有关规范的信息以及 TRAININGPIPELINE_ID。

响应

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.8,
      "validationFraction": 0,
      "testFraction": 0.2
    }
  },
  "trainingTaskDefinition": OBJECTIVE,
  "trainingTaskInputs": {
    "modelType": "EDGE_MODEL_TYPE",
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAY_NAME",
    "labels": {
      "KEY1": "VALUE1",
      "KEY2": "VALUE2"
    }
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-10-14T17:16:55.098953Z",
  "updateTime": "2020-10-14T17:16:55.098953Z"
}

您可以获取 trainingPipeline 进度的状态，以了解训练完成时间。

对象跟踪

在训练时，选择 AutoML Edge 类型：

MOBILE_VERSATILE_1：通用目的
MOBILE_CORAL_VERSATILE_1：提高 Google Coral 的预测质量
MOBILE_CORAL_LOW_LATENCY_1：降低 Google Coral 的延迟
MOBILE_JETSON_VERSATILE_1：提高 NVIDIA Jetson 的预测质量
MOBILE_JETSON_LOW_LATENCY_1：降低 NVIDIA Jetson 的延迟

REST

在使用任何请求数据之前，请先进行以下替换：

PROJECT：您的项目 ID。
LOCATION：数据集所在且模型在其中创建的区域。例如 us-central1。
TRAINING_PIPELINE_DISPLAY_NAME：必填。TrainingPipeline 的显示名。
DATASET_ID：训练数据集的 ID。
TRAINING_FRACTION、TEST_FRACTION：fractionSplit 对象是可选的；您使用它来控制数据拆分。如需详细了解如何控制数据拆分，请参阅 AutoML 模型的数据拆分简介。例如：
- {"trainingFraction": "0.8","validationFraction": "0","testFraction": "0.2"}
MODEL_DISPLAY_NAME：经过训练的模型的显示名。
MODEL_DESCRIPTION：模型的说明。
MODEL_LABELS：用于组织模型的任何键值对。例如：
- "env": "prod"
- "tier": "backend"
EDGE_MODEL_TYPE：以下之一：
- MOBILE_VERSATILE_1：通用目的
- MOBILE_CORAL_VERSATILE_1：提高 Google Coral 的预测质量
- MOBILE_CORAL_LOW_LATENCY_1：降低 Google Coral 的延迟
- MOBILE_JETSON_VERSATILE_1：提高 NVIDIA Jetson 的预测质量
- MOBILE_JETSON_LOW_LATENCY_1：降低 NVIDIA Jetson 的延迟
PROJECT_NUMBER：自动生成的项目编号

HTTP 方法和网址：

POST https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines

请求 JSON 正文：

{
  "displayName": "TRAINING_PIPELINE_DISPLAY_NAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": "TRAINING_FRACTION",
      "validationFraction": "0",
      "testFraction": "TEST_FRACTION"
    }
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAY_NAME",
    "description": "MODEL_DESCRIPTION",
    "labels": {
      "KEY": "VALUE"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_object_tracking_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": ["EDGE_MODEL_TYPE"],
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines"

PowerShell

将请求正文保存在名为 request.json 的文件中，然后执行以下命令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/beta1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

响应包含有关规范的信息以及 TRAININGPIPELINE_ID。

响应

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "TRAININGPIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.8,
      "validationFraction": 0,
      "testFraction": 0.2
    }
  },
  "trainingTaskDefinition": OBJECTIVE,
  "trainingTaskInputs": {
    "modelType": "EDGE_MODEL_TYPE",
  },
  "modelToUpload": {
    "displayName": "MODEL_DISPLAY_NAME",
    "labels": {
      "KEY1": "VALUE1",
      "KEY2": "VALUE2"
    }
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-10-14T17:16:55.098953Z",
  "updateTime": "2020-10-14T17:16:55.098953Z"
}

您可以获取 trainingPipeline 进度的状态，以了解训练完成时间。

获取 trainingPipeline 状态

使用以下代码以编程方式获取 trainingPipeline 创建的状态。

REST

在使用任何请求数据之前，请先进行以下替换：

LOCATION：TrainingPipeline 所在的区域。
PROJECT：您的项目 ID。
TRAININGPIPELINE_ID：特定 TrainingPipeline 的 ID。
PROJECT_NUMBER：自动生成的项目编号

HTTP 方法和网址：

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID

如需发送请求，请选择以下方式之一：

curl

执行以下命令：

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID"

PowerShell

执行以下命令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID" | Select-Object -Expand Content

"state" 字段显示操作的当前状态。完成的 trainingPipeline 将显示

创建 trainingPipeline 操作完成后，您应会看到如下输出：

响应

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
  "displayName": "PIPELINE_DISPLAYNAME",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "fractionSplit": {
      "trainingFraction": 0.7,
      "validationFraction": 0.15,
      "testFraction": 0.15
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "modelType": "MOBILE_TF_VERSATILE_1",
    "budgetMilliNodeHours": "1000"
  },
  "modelToUpload": {
    "name": "projects/PROJECT_NUMBER/locations/LOCATION/models/4030440191519883264",
    "displayName": "image_classification_edge_model1",
    "labels": {
      "env": "prod",
      "tier": "backend"
    }
  },
  "state": "PIPELINE_STATE_SUCCEEDED",
  "createTime": "2020-07-14T17:16:55.098953Z",
  "startTime": "2020-07-14T17:16:55.360691Z",
  "endTime": "2020-07-14T18:46:08.169170Z",
  "updateTime": "2020-07-14T18:46:08.169170Z"
}

Java

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。


import com.google.cloud.aiplatform.v1.DeployedModelRef;
import com.google.cloud.aiplatform.v1.EnvVar;
import com.google.cloud.aiplatform.v1.FilterSplit;
import com.google.cloud.aiplatform.v1.FractionSplit;
import com.google.cloud.aiplatform.v1.InputDataConfig;
import com.google.cloud.aiplatform.v1.Model;
import com.google.cloud.aiplatform.v1.ModelContainerSpec;
import com.google.cloud.aiplatform.v1.PipelineServiceClient;
import com.google.cloud.aiplatform.v1.PipelineServiceSettings;
import com.google.cloud.aiplatform.v1.Port;
import com.google.cloud.aiplatform.v1.PredefinedSplit;
import com.google.cloud.aiplatform.v1.PredictSchemata;
import com.google.cloud.aiplatform.v1.TimestampSplit;
import com.google.cloud.aiplatform.v1.TrainingPipeline;
import com.google.cloud.aiplatform.v1.TrainingPipelineName;
import com.google.rpc.Status;
import java.io.IOException;

public class GetTrainingPipelineSample {
  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String trainingPipelineId = "YOUR_TRAINING_PIPELINE_ID";
    getTrainingPipeline(project, trainingPipelineId);
  }

  static void getTrainingPipeline(String project, String trainingPipelineId) throws IOException {
    PipelineServiceSettings pipelineServiceSettings =
        PipelineServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (PipelineServiceClient pipelineServiceClient =
        PipelineServiceClient.create(pipelineServiceSettings)) {
      String location = "us-central1";
      TrainingPipelineName trainingPipelineName =
          TrainingPipelineName.of(project, location, trainingPipelineId);

      TrainingPipeline trainingPipelineResponse =
          pipelineServiceClient.getTrainingPipeline(trainingPipelineName);

      System.out.println("Get Training Pipeline Response");
      System.out.format("\tName: %s\n", trainingPipelineResponse.getName());
      System.out.format("\tDisplay Name: %s\n", trainingPipelineResponse.getDisplayName());
      System.out.format(
          "\tTraining Task Definition: %s\n", trainingPipelineResponse.getTrainingTaskDefinition());
      System.out.format(
          "\tTraining Task Inputs: %s\n", trainingPipelineResponse.getTrainingTaskInputs());
      System.out.format(
          "\tTraining Task Metadata: %s\n", trainingPipelineResponse.getTrainingTaskMetadata());
      System.out.format("\tState: %s\n", trainingPipelineResponse.getState());
      System.out.format("\tCreate Time: %s\n", trainingPipelineResponse.getCreateTime());
      System.out.format("\tStart Time: %s\n", trainingPipelineResponse.getStartTime());
      System.out.format("\tEnd Time: %s\n", trainingPipelineResponse.getEndTime());
      System.out.format("\tUpdate Time: %s\n", trainingPipelineResponse.getUpdateTime());
      System.out.format("\tLabels: %s\n", trainingPipelineResponse.getLabelsMap());
      InputDataConfig inputDataConfig = trainingPipelineResponse.getInputDataConfig();

      System.out.println("\tInput Data Config");
      System.out.format("\t\tDataset Id: %s\n", inputDataConfig.getDatasetId());
      System.out.format("\t\tAnnotations Filter: %s\n", inputDataConfig.getAnnotationsFilter());
      FractionSplit fractionSplit = inputDataConfig.getFractionSplit();

      System.out.println("\t\tFraction Split");
      System.out.format("\t\t\tTraining Fraction: %s\n", fractionSplit.getTrainingFraction());
      System.out.format("\t\t\tValidation Fraction: %s\n", fractionSplit.getValidationFraction());
      System.out.format("\t\t\tTest Fraction: %s\n", fractionSplit.getTestFraction());
      FilterSplit filterSplit = inputDataConfig.getFilterSplit();

      System.out.println("\t\tFilter Split");
      System.out.format("\t\t\tTraining Filter: %s\n", filterSplit.getTrainingFilter());
      System.out.format("\t\t\tValidation Filter: %s\n", filterSplit.getValidationFilter());
      System.out.format("\t\t\tTest Filter: %s\n", filterSplit.getTestFilter());
      PredefinedSplit predefinedSplit = inputDataConfig.getPredefinedSplit();

      System.out.println("\t\tPredefined Split");
      System.out.format("\t\t\tKey: %s\n", predefinedSplit.getKey());
      TimestampSplit timestampSplit = inputDataConfig.getTimestampSplit();

      System.out.println("\t\tTimestamp Split");
      System.out.format("\t\t\tTraining Fraction: %s\n", timestampSplit.getTrainingFraction());
      System.out.format("\t\t\tTest Fraction: %s\n", timestampSplit.getTestFraction());
      System.out.format("\t\t\tValidation Fraction: %s\n", timestampSplit.getValidationFraction());
      System.out.format("\t\t\tKey: %s\n", timestampSplit.getKey());
      Model modelResponse = trainingPipelineResponse.getModelToUpload();

      System.out.println("\t\tModel to upload");
      System.out.format("\t\tName: %s\n", modelResponse.getName());
      System.out.format("\t\tDisplay Name: %s\n", modelResponse.getDisplayName());
      System.out.format("\t\tDescription: %s\n", modelResponse.getDescription());
      System.out.format("\t\tMetadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
      System.out.format("\t\tMeta Data: %s\n", modelResponse.getMetadata());
      System.out.format("\t\tTraining Pipeline: %s\n", modelResponse.getTrainingPipeline());
      System.out.format("\t\tArtifact Uri: %s\n", modelResponse.getArtifactUri());
      System.out.format(
          "\t\tSupported Deployment Resources Types: %s\n",
          modelResponse.getSupportedDeploymentResourcesTypesList().toString());
      System.out.format(
          "\t\tSupported Input Storage Formats: %s\n",
          modelResponse.getSupportedInputStorageFormatsList().toString());
      System.out.format(
          "\t\tSupported Output Storage Formats: %s\n",
          modelResponse.getSupportedOutputStorageFormatsList().toString());
      System.out.format("\t\tCreate Time: %s\n", modelResponse.getCreateTime());
      System.out.format("\t\tUpdate Time: %s\n", modelResponse.getUpdateTime());
      System.out.format("\t\tLabels: %s\n", modelResponse.getLabelsMap());
      PredictSchemata predictSchemata = modelResponse.getPredictSchemata();

      System.out.println("\tPredict Schemata");
      System.out.format("\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
      System.out.format(
          "\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
      System.out.format(
          "\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());

      for (Model.ExportFormat supportedExportFormat :
          modelResponse.getSupportedExportFormatsList()) {
        System.out.println("\tSupported Export Format");
        System.out.format("\t\tId: %s\n", supportedExportFormat.getId());
      }
      ModelContainerSpec containerSpec = modelResponse.getContainerSpec();

      System.out.println("\tContainer Spec");
      System.out.format("\t\tImage Uri: %s\n", containerSpec.getImageUri());
      System.out.format("\t\tCommand: %s\n", containerSpec.getCommandList());
      System.out.format("\t\tArgs: %s\n", containerSpec.getArgsList());
      System.out.format("\t\tPredict Route: %s\n", containerSpec.getPredictRoute());
      System.out.format("\t\tHealth Route: %s\n", containerSpec.getHealthRoute());

      for (EnvVar envVar : containerSpec.getEnvList()) {
        System.out.println("\t\tEnv");
        System.out.format("\t\t\tName: %s\n", envVar.getName());
        System.out.format("\t\t\tValue: %s\n", envVar.getValue());
      }

      for (Port port : containerSpec.getPortsList()) {
        System.out.println("\t\tPort");
        System.out.format("\t\t\tContainer Port: %s\n", port.getContainerPort());
      }

      for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
        System.out.println("\tDeployed Model");
        System.out.format("\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());
        System.out.format("\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
      }

      Status status = trainingPipelineResponse.getError();
      System.out.println("\tError");
      System.out.format("\t\tCode: %s\n", status.getCode());
      System.out.format("\t\tMessage: %s\n", status.getMessage());
    }
  }
}

Python

如需了解如何安装或更新 Python，请参阅安装 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python API 参考文档。

from google.cloud import aiplatform

def get_training_pipeline_sample(
    project: str,
    training_pipeline_id: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
    name = client.training_pipeline_path(
        project=project, location=location, training_pipeline=training_pipeline_id
    )
    response = client.get_training_pipeline(name=name)
    print("response:", response)

获取模型信息

训练流水线创建完成后，您可以使用模型的显示名来获取更详细的模型信息。

REST

在使用任何请求数据之前，请先进行以下替换：

LOCATION：模型所在的区域。例如 us-central1
PROJECT：您的项目 ID。
MODEL_DISPLAYNAME：您在创建 trainingPipeline 作业时指定的模型的显示名。
PROJECT_NUMBER：自动生成的项目编号

HTTP 方法和网址：

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models?filter=display_name=MODEL_DISPLAYNAME

如需发送请求，请选择以下方式之一：

curl

执行以下命令：

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models?filter=display_name=MODEL_DISPLAYNAME
"

PowerShell

执行以下命令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/models?filter=display_name=MODEL_DISPLAYNAME
" | Select-Object -Expand Content

AutoML Edge 模型训练完成后，您应该会看到类似如下所示的输出：以下是图片 AutoML Edge 模型的示例输出：

响应

{
  "models": [
    {
      "name": "projects/PROJECT/locations/LOCATION/models/MODEL_ID",
      "displayName": "MODEL_DISPLAYNAME",
      "predictSchemata": {
        "instanceSchemaUri": "https://storage.googleapis.com/google-cloud-aiplatform/schema/predict/instance/image_classification_1.0.0.yaml",
        "parametersSchemaUri": "https://storage.googleapis.com/google-cloud-aiplatform/schema/predict/params/image_classification_1.0.0.yaml",
        "predictionSchemaUri": "https://storage.googleapis.com/google-cloud-aiplatform/schema/predict/prediction/classification_1.0.0.yaml"
      },
      "metadataSchemaUri": "https://storage.googleapis.com/google-cloud-aiplatform/schema/model/metadata/automl_image_classification_1.0.0.yaml",
      "metadata": {
        "modelType": "MOBILE_TF_VERSATILE_1",
        "trainingDataItemsCount": "2174",
        "validationDataItemsCount": "467",
        "trainingAnnotationsCount": "2174",
        "validationAnnotationsCount": "467",
        "eligibleAsBaseModel": true
      },
      "trainingPipeline": "projects/PROJECT_NUMBER/locations/LOCATION/trainingPipelines/TRAININGPIPELINE_ID",
      "supportedDeploymentResourcesTypes": [
        "AUTOMATIC_RESOURCES"
      ],
      "supportedInputStorageFormats": [
        "jsonl"
      ],
      "supportedOutputStorageFormats": [
        "jsonl"
      ],
      "createTime": "2020-07-14T17:16:55.098953Z",
      "updateTime": "2020-07-14T18:46:08.169225Z",
      "etag": "AMEw9yNjhttIyiGUgec8vGNeidfX8mGcFT1owDOq1iDT6mmQ7HLHwoomie30pLCTtJ4=",
      "labels": {
        "env": "prod",
        "tier": "backend"
      },
      "supportedExportFormats": [
        {
          "id": "tflite"
        },
        {
          "id": "edgetpu-tflite"
        },
        {
          "id": "tf-saved-model"
        },
        {
          "id": "core-ml"
        },
        {
          "id": "tf-js"
        }
      ]
    }
  ]
}

Java

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。


import com.google.cloud.aiplatform.v1.DeployedModelRef;
import com.google.cloud.aiplatform.v1.EnvVar;
import com.google.cloud.aiplatform.v1.Model;
import com.google.cloud.aiplatform.v1.Model.ExportFormat;
import com.google.cloud.aiplatform.v1.ModelContainerSpec;
import com.google.cloud.aiplatform.v1.ModelName;
import com.google.cloud.aiplatform.v1.ModelServiceClient;
import com.google.cloud.aiplatform.v1.ModelServiceSettings;
import com.google.cloud.aiplatform.v1.Port;
import com.google.cloud.aiplatform.v1.PredictSchemata;
import java.io.IOException;

public class GetModelSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    getModelSample(project, modelId);
  }

  static void getModelSample(String project, String modelId) throws IOException {
    ModelServiceSettings modelServiceSettings =
        ModelServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {
      String location = "us-central1";
      ModelName modelName = ModelName.of(project, location, modelId);

      Model modelResponse = modelServiceClient.getModel(modelName);
      System.out.println("Get Model response");
      System.out.format("\tName: %s\n", modelResponse.getName());
      System.out.format("\tDisplay Name: %s\n", modelResponse.getDisplayName());
      System.out.format("\tDescription: %s\n", modelResponse.getDescription());

      System.out.format("\tMetadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
      System.out.format("\tMetadata: %s\n", modelResponse.getMetadata());
      System.out.format("\tTraining Pipeline: %s\n", modelResponse.getTrainingPipeline());
      System.out.format("\tArtifact Uri: %s\n", modelResponse.getArtifactUri());

      System.out.format(
          "\tSupported Deployment Resources Types: %s\n",
          modelResponse.getSupportedDeploymentResourcesTypesList());
      System.out.format(
          "\tSupported Input Storage Formats: %s\n",
          modelResponse.getSupportedInputStorageFormatsList());
      System.out.format(
          "\tSupported Output Storage Formats: %s\n",
          modelResponse.getSupportedOutputStorageFormatsList());

      System.out.format("\tCreate Time: %s\n", modelResponse.getCreateTime());
      System.out.format("\tUpdate Time: %s\n", modelResponse.getUpdateTime());
      System.out.format("\tLabels: %s\n", modelResponse.getLabelsMap());

      PredictSchemata predictSchemata = modelResponse.getPredictSchemata();
      System.out.println("\tPredict Schemata");
      System.out.format("\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
      System.out.format(
          "\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
      System.out.format(
          "\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());

      for (ExportFormat exportFormat : modelResponse.getSupportedExportFormatsList()) {
        System.out.println("\tSupported Export Format");
        System.out.format("\t\tId: %s\n", exportFormat.getId());
      }

      ModelContainerSpec containerSpec = modelResponse.getContainerSpec();
      System.out.println("\tContainer Spec");
      System.out.format("\t\tImage Uri: %s\n", containerSpec.getImageUri());
      System.out.format("\t\tCommand: %s\n", containerSpec.getCommandList());
      System.out.format("\t\tArgs: %s\n", containerSpec.getArgsList());
      System.out.format("\t\tPredict Route: %s\n", containerSpec.getPredictRoute());
      System.out.format("\t\tHealth Route: %s\n", containerSpec.getHealthRoute());

      for (EnvVar envVar : containerSpec.getEnvList()) {
        System.out.println("\t\tEnv");
        System.out.format("\t\t\tName: %s\n", envVar.getName());
        System.out.format("\t\t\tValue: %s\n", envVar.getValue());
      }

      for (Port port : containerSpec.getPortsList()) {
        System.out.println("\t\tPort");
        System.out.format("\t\t\tContainer Port: %s\n", port.getContainerPort());
      }

      for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
        System.out.println("\tDeployed Model");
        System.out.format("\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());
        System.out.format("\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
      }
    }
  }
}

Node.js

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const modelId = 'YOUR_MODEL_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

// Imports the Google Cloud Model Service Client library
const {ModelServiceClient} = require('@google-cloud/aiplatform');

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const modelServiceClient = new ModelServiceClient(clientOptions);

async function getModel() {
  // Configure the parent resource
  const name = `projects/${project}/locations/${location}/models/${modelId}`;
  const request = {
    name,
  };
  // Get and print out a list of all the endpoints for this resource
  const [response] = await modelServiceClient.getModel(request);

  console.log('Get model response');
  console.log(`\tName : ${response.name}`);
  console.log(`\tDisplayName : ${response.displayName}`);
  console.log(`\tDescription : ${response.description}`);
  console.log(`\tMetadata schema uri : ${response.metadataSchemaUri}`);
  console.log(`\tMetadata : ${JSON.stringify(response.metadata)}`);
  console.log(`\tTraining pipeline : ${response.trainingPipeline}`);
  console.log(`\tArtifact uri : ${response.artifactUri}`);
  console.log(
    `\tSupported deployment resource types : \
      ${response.supportedDeploymentResourceTypes}`
  );
  console.log(
    `\tSupported input storage formats : \
      ${response.supportedInputStorageFormats}`
  );
  console.log(
    `\tSupported output storage formats : \
      ${response.supportedOutputStoragFormats}`
  );
  console.log(`\tCreate time : ${JSON.stringify(response.createTime)}`);
  console.log(`\tUpdate time : ${JSON.stringify(response.updateTime)}`);
  console.log(`\tLabels : ${JSON.stringify(response.labels)}`);

  const predictSchemata = response.predictSchemata;
  console.log('\tPredict schemata');
  console.log(`\tInstance schema uri : ${predictSchemata.instanceSchemaUri}`);
  console.log(
    `\tParameters schema uri : ${predictSchemata.prametersSchemaUri}`
  );
  console.log(
    `\tPrediction schema uri : ${predictSchemata.predictionSchemaUri}`
  );

  const [supportedExportFormats] = response.supportedExportFormats;
  console.log('\tSupported export formats');
  console.log(`\t${supportedExportFormats}`);

  const containerSpec = response.containerSpec;
  console.log('\tContainer Spec');
  if (!containerSpec) {
    console.log(`\t\t${JSON.stringify(containerSpec)}`);
    console.log('\t\tImage uri : {}');
    console.log('\t\tCommand : {}');
    console.log('\t\tArgs : {}');
    console.log('\t\tPredict route : {}');
    console.log('\t\tHealth route : {}');
    console.log('\t\tEnv');
    console.log('\t\t\t{}');
    console.log('\t\tPort');
    console.log('\t\t{}');
  } else {
    console.log(`\t\t${JSON.stringify(containerSpec)}`);
    console.log(`\t\tImage uri : ${containerSpec.imageUri}`);
    console.log(`\t\tCommand : ${containerSpec.command}`);
    console.log(`\t\tArgs : ${containerSpec.args}`);
    console.log(`\t\tPredict route : ${containerSpec.predictRoute}`);
    console.log(`\t\tHealth route : ${containerSpec.healthRoute}`);
    const env = containerSpec.env;
    console.log('\t\tEnv');
    console.log(`\t\t\t${JSON.stringify(env)}`);
    const ports = containerSpec.ports;
    console.log('\t\tPort');
    console.log(`\t\t\t${JSON.stringify(ports)}`);
  }

  const [deployedModels] = response.deployedModels;
  console.log('\tDeployed models');
  console.log('\t\t', deployedModels);
}
getModel();

Python

如需了解如何安装或更新 Python，请参阅安装 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python API 参考文档。

def get_model_sample(project: str, location: str, model_name: str):

    aiplatform.init(project=project, location=location)

    model = aiplatform.Model(model_name=model_name)

    print(model.display_name)
    print(model.resource_name)
    return model

后续步骤

评估 AutoML 模型。
导出 AutoML Edge 模型。

*	您在 `trainingTaskDefinition` 中指定的架构文件的说明描述了此字段的用途。
†	您在 `trainingTaskDefinition` 中指定的架构文件声明并描述了此字段。

使用 Vertex AI API 训练 AutoML Edge 模型

创建 AutoML Edge 训练流水线

图片

分类

REST

curl

PowerShell

响应

分类

REST

curl

PowerShell

响应

对象检测

REST

curl

PowerShell

响应

视频

动作识别

REST

curl

PowerShell

响应

分类

REST

curl

PowerShell

响应

对象跟踪

REST

curl

PowerShell

响应

获取 trainingPipeline 状态

REST

curl

PowerShell

响应

Java

Python

获取模型信息

REST

curl

PowerShell

响应

Java

Node.js

Python

后续步骤