Train an AutoML Edge model using the Vertex AI API

You create an AutoML model directly in the Google Cloud console, or by creating a training pipeline programmatically, using the API or one of the Vertex AI client libraries.

This model is created using a prepared dataset provided by you using the console or the API. The Vertex AI API uses the items from the dataset to train the model, test it, and evaluate model performance. Review the evaluations results, adjust the training dataset as needed, and create a new training pipeline using the improved dataset.

Model training can take several hours to complete. The Vertex AI API enables you to get the status of training.

Create an AutoML Edge training pipeline

When you have a dataset with a representative set of training items, you are ready to create an AutoML Edge training pipeline.

Select a data type.

Image

Select the tab below for your objective:

Classification

At training time you can choose the type of AutoML Edge model you want, depending on your specific use case:

low latency (MOBILE_TF_LOW_LATENCY_1)
general purpose usage (MOBILE_TF_VERSATILE_1)
higher prediction quality (MOBILE_TF_HIGH_ACCURACY_1)

Select the tab below for your language or environment:

REST & CMD LINE

Before using any of the request data, make the following replacements:

LOCATION: Region where dataset is located and Model is created. For example, us-central1.
PROJECT: Your project ID.
TRAININGPIPELINE_DISPLAYNAME: Required. A display name for the trainingPipeline.
DATASET_ID: The ID number for the dataset to use for training.
fractionSplit: Optional. One of several possible ML use split options for your data. For fractionSplit, values must sum to 1. For example:
- {"trainingFraction": "0.7","validationFraction": "0.15","testFraction": "0.15"}
MODEL_DISPLAYNAME^*: A display name for the model uploaded (created) by the TrainingPipeline.
MODEL_DESCRIPTION^*: A description for the model.
modelToUpload.labels^*: Any set of key-value pairs to organize your models. For example:
- "env": "prod"
- "tier": "backend"
EDGE_MODELTYPE^†: The type of Edge model to train. The options are:
- MOBILE_TF_LOW_LATENCY_1
- MOBILE_TF_VERSATILE_1
- MOBILE_TF_HIGH_ACCURACY_1
NODE_HOUR_BUDGET^†: The actual training cost will be equal or less than this value. For Edge models the budget must be: 1,000 - 100,000 milli node hours (inclusive).
PROJECT_NUMBER: Project number for your project (appears in the response)

HTTP method and URL:

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

Request JSON body:

{
  "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
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default 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

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default 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

The response contains information about specifications as well as the TRAININGPIPELINE_ID.

Response

{
  "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"
}

You can get the status of the trainingPipeline job using the TRAININGPIPELINE_ID.

Classification

At training time you can choose the type of AutoML Edge model you want, depending on your specific use case:

low latency (MOBILE_TF_LOW_LATENCY_1)
general purpose usage (MOBILE_TF_VERSATILE_1)
higher prediction quality (MOBILE_TF_HIGH_ACCURACY_1)

Select the tab below for your language or environment:

REST & CMD LINE

Before using any of the request data, make the following replacements:

LOCATION: Region where dataset is located and Model is created. For example, us-central1.
PROJECT: Your project ID.
TRAININGPIPELINE_DISPLAYNAME: Required. A display name for the trainingPipeline.
DATASET_ID: The ID number for the dataset to use for training.
fractionSplit: Optional. One of several possible ML use split options for your data. For fractionSplit, values must sum to 1. For example:
- {"trainingFraction": "0.7","validationFraction": "0.15","testFraction": "0.15"}
MODEL_DISPLAYNAME^*: A display name for the model uploaded (created) by the TrainingPipeline.
MODEL_DESCRIPTION^*: A description for the model.
modelToUpload.labels^*: Any set of key-value pairs to organize your models. For example:
- "env": "prod"
- "tier": "backend"
EDGE_MODELTYPE^†: The type of Edge model to train. The options are:
- MOBILE_TF_LOW_LATENCY_1
- MOBILE_TF_VERSATILE_1
- MOBILE_TF_HIGH_ACCURACY_1
NODE_HOUR_BUDGET^†: The actual training cost will be equal or less than this value. For Edge models the budget must be: 1,000 - 100,000 milli node hours (inclusive).
PROJECT_NUMBER: Project number for your project (appears in the response)

HTTP method and URL:

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

Request JSON body:

{
  "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
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default 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

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default 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

The response contains information about specifications as well as the TRAININGPIPELINE_ID.

Response

{
  "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"
}

You can get the status of the trainingPipeline job using the TRAININGPIPELINE_ID.

Object detection

At training time you can choose the type of AutoML Edge model you want, depending on your specific use case:

low latency (MOBILE_TF_LOW_LATENCY_1)
general purpose usage (MOBILE_TF_VERSATILE_1)
higher prediction quality (MOBILE_TF_HIGH_ACCURACY_1)

Select the tab below for your language or environment:

REST & CMD LINE

Before using any of the request data, make the following replacements:

LOCATION: Region where dataset is located and Model is created. For example, us-central1.
PROJECT: Your project ID.
TRAININGPIPELINE_DISPLAYNAME: Required. A display name for the trainingPipeline.
DATASET_ID: The ID number for the dataset to use for training.
fractionSplit: Optional. One of several possible ML use split options for your data. For fractionSplit, values must sum to 1. For example:
- {"trainingFraction": "0.7","validationFraction": "0.15","testFraction": "0.15"}
MODEL_DISPLAYNAME^*: A display name for the model uploaded (created) by the TrainingPipeline.
MODEL_DESCRIPTION^*: A description for the model.
modelToUpload.labels^*: Any set of key-value pairs to organize your models. For example:
- "env": "prod"
- "tier": "backend"
EDGE_MODELTYPE^†: The type of Edge model to train. The options are:
- MOBILE_TF_LOW_LATENCY_1
- MOBILE_TF_VERSATILE_1
- MOBILE_TF_HIGH_ACCURACY_1
NODE_HOUR_BUDGET^†: The actual training cost will be equal or less than this value. For Cloud models the budget must be: 20,000 - 900,000 milli node hours (inclusive). The default value is 216,000 which represents one day in wall time, assuming 9 nodes are used.
PROJECT_NUMBER: Project number for your project (appears in the response)

HTTP method and URL:

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

Request JSON body:

{
  "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
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default 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

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default 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

The response contains information about specifications as well as the TRAININGPIPELINE_ID.

Response

{
  "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"
}

You can get the status of the trainingPipeline job using the TRAININGPIPELINE_ID.

Video

Select the tab below for your objective:

Action recognition

At training time, choose the following AutoML edge type:

MOBILE_VERSATILE_1: general purpose usage

REST & CMD LINE

Before using any of the request data, make the following replacements:

PROJECT: Your project ID.
LOCATION: Region where dataset is located and Model is created. For example, us-central1.
TRAINING_PIPELINE_DISPLAY_NAME: Required. A display name for the TrainingPipeline.
DATASET_ID: ID for the training Dataset.
TRAINING_FRACTION, TEST_FRACTION: The fractionSplit object is optional; you use it to control your data split. For more information about controlling data split, see see About data splits for AutoML models. For example:
- {"trainingFraction": "0.8","validationFraction": "0","testFraction": "0.2"}
MODEL_DISPLAY_NAME: Display name of the trained Model.
MODEL_DESCRIPTION: A description for the Model.
MODEL_LABELS: Any set of key-value pairs to organize your models. For example:
- "env": "prod"
- "tier": "backend"
EDGE_MODEL_TYPE:
- MOBILE_VERSATILE_1: general purpose usage
PROJECT_NUMBER: Project number for your project (appears in the response)

HTTP method and URL:

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

Request JSON body:

{
  "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"],
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default 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

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default 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

The response contains information about specifications as well as the TRAININGPIPELINE_ID.

Response

{
  "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"
}

You can get the status of the trainingPipeline progress to see when it finishes.

Classification

At training time, choose the following AutoML edge type:

MOBILE_VERSATILE_1: general purpose usage

REST & CMD LINE

Before using any of the request data, make the following replacements:

PROJECT: Your project ID.
LOCATION: Region where dataset is located and Model is created. For example, us-central1.
TRAINING_PIPELINE_DISPLAY_NAME: Required. A display name for the TrainingPipeline.
DATASET_ID: ID for the training Dataset.
TRAINING_FRACTION, TEST_FRACTION: The fractionSplit object is optional; you use it to control your data split. For more information about controlling data split, see see About data splits for AutoML models. For example:
- {"trainingFraction": "0.8","validationFraction": "0","testFraction": "0.2"}
MODEL_DISPLAY_NAME: Display name of the trained Model.
MODEL_DESCRIPTION: A description for the Model.
MODEL_LABELS: Any set of key-value pairs to organize your models. For example:
- "env": "prod"
- "tier": "backend"
EDGE_MODEL_TYPE:
- MOBILE_VERSATILE_1: general purpose usage
PROJECT_NUMBER: Project number for your project (appears in the response)

HTTP method and URL:

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

Request JSON body:

{
  "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"],
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default 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

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default 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

The response contains information about specifications as well as the TRAININGPIPELINE_ID.

Response

{
  "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"
}

You can get the status of the trainingPipeline progress to see when it finishes.

Object tracking

At training time, choose the AutoML edge type:

MOBILE_VERSATILE_1: general purpose usage
MOBILE_CORAL_VERSATILE_1: higher prediction quality for Google Coral
MOBILE_CORAL_LOW_LATENCY_1: lower latency for Google Coral
MOBILE_JETSON_VERSATILE_1: higher prediction quality for NVIDIA Jetson
MOBILE_JETSON_LOW_LATENCY_1: lower latency for NVIDIA Jetson

REST & CMD LINE

Before using any of the request data, make the following replacements:

PROJECT: Your project ID.
LOCATION: Region where dataset is located and Model is created. For example, us-central1.
TRAINING_PIPELINE_DISPLAY_NAME: Required. A display name for the TrainingPipeline.
DATASET_ID: ID for the training Dataset.
TRAINING_FRACTION, TEST_FRACTION: The fractionSplit object is optional; you use it to control your data split. For more information about controlling data split, see see About data splits for AutoML models. For example:
- {"trainingFraction": "0.8","validationFraction": "0","testFraction": "0.2"}
MODEL_DISPLAY_NAME: Display name of the trained Model.
MODEL_DESCRIPTION: A description for the Model.
MODEL_LABELS: Any set of key-value pairs to organize your models. For example:
- "env": "prod"
- "tier": "backend"
EDGE_MODEL_TYPE: One of the following:
- MOBILE_VERSATILE_1: general purpose usage
- MOBILE_CORAL_VERSATILE_1: higher prediction quality for Google Coral
- MOBILE_CORAL_LOW_LATENCY_1: lower latency for Google Coral
- MOBILE_JETSON_VERSATILE_1: higher prediction quality for NVIDIA Jetson
- MOBILE_JETSON_LOW_LATENCY_1: lower latency for NVIDIA Jetson
PROJECT_NUMBER: Project number for your project (appears in the response)

HTTP method and URL:

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

Request JSON body:

{
  "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"],
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default 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

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default 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

The response contains information about specifications as well as the TRAININGPIPELINE_ID.

Response

{
  "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"
}

You can get the status of the trainingPipeline progress to see when it finishes.

Get trainingPipeline status

Use the following code to programmatically get the status of trainingPipeline creation.

REST & CMD LINE

Before using any of the request data, make the following replacements:

LOCATION: Region where TrainingPipeline is located.
PROJECT: Your project ID.
TRAININGPIPELINE_ID: The ID of the specific TrainingPipeline.
PROJECT_NUMBER: Project number for your project (appears in the response)

HTTP method and URL:

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

To send your request, choose one of these options:

curl

Execute the following command:

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

PowerShell

Execute the following command:

$cred = gcloud auth application-default 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

The "state" field shows the current status of the operation. A completed trainingPipeline shows

You should see output similar to the following for a completed create trainingPipeline operation:

Response

{
  "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

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Java API reference documentation.

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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

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Python API reference documentation.

Open in Editor View on GitHub Feedback

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)

Get model information

After the trainingPipeline creation completes you can use the model's display name to get more detailed model information.

REST & CMD LINE

Before using any of the request data, make the following replacements:

LOCATION: Region where Model is located. For example, us-central1
PROJECT: Your project ID.
MODEL_DISPLAYNAME: The display name of your model that you specified when creating a trainingPipeline job.
PROJECT_NUMBER: Project number for your project (appears in the response)

HTTP method and URL:

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

To send your request, choose one of these options:

curl

Execute the following command:

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

PowerShell

Execute the following command:

$cred = gcloud auth application-default 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

You should see output similar to the following for a trained AutoML Edge model. The following sample output is for an image AutoML Edge model:

Response

{
  "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

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Java API reference documentation.

Open in Editor View on GitHub Feedback


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

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Node.js API reference documentation.

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/**
 * 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

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Python API reference documentation.

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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

*	Schema file's description you specify in `trainingTaskDefinition` describes the use of this field.
†	Schema file you specify in `trainingTaskDefinition` declares and describes this field.

Train an AutoML Edge model using the Vertex AI API

Create an AutoML Edge training pipeline

Image

Classification

REST & CMD LINE

curl

PowerShell

Response

Classification

REST & CMD LINE

curl

PowerShell

Response

Object detection

REST & CMD LINE

curl

PowerShell

Response

Video

Action recognition

REST & CMD LINE

curl

PowerShell

Response

Classification

REST & CMD LINE

curl

PowerShell

Response

Object tracking

REST & CMD LINE

curl

PowerShell

Response

Get trainingPipeline status

REST & CMD LINE

curl

PowerShell

Response

Java

Python

Get model information

REST & CMD LINE

curl

PowerShell

Response

Java

Node.js

Python

What's next