This legacy version of AutoML Vision is deprecated and will no longer be available on Google Cloud after January 23, 2024. All the functionality of legacy AutoML Vision and new features are available on the Vertex AI platform. See Migrate to Vertex AI to learn how to migrate your resources.

Training Edge exportable models

Stay organized with collections Save and categorize content based on your preferences.

You create a custom model by training it using a prepared dataset. AutoML API uses the items from the dataset to train the model, test it, and evaluate its performance. You review the results, adjust the training dataset as needed, and train a new model using the improved dataset.

Training a model can take several hours to complete. The AutoML API enables you to check the status of training.

Since AutoML Vision creates a new model each time you start training, your project may include numerous models. You can get a list of the models in your project and you can delete models you no longer need. Alternatively, you can use the Cloud AutoML Vision UI to list and delete models created via the AutoML API that you do not need anymore.

Models are based on state-of-the-art research at Google. Your model will be available as a TF Lite package. For more information about how to integrate a TensorFlow Lite model using the TensorFlow Lite SDK reference the following links for iOS and Android.

Training Edge models

When you have a dataset with a solid set of labeled training items, you are ready to create and train your custom Edge model.

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the AutoML Vision reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the AutoML Vision reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the AutoML Vision reference documentation for Ruby.

TensorFlow serving and TF Lite models

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

  • low latency (mobile-low-latency-1)
  • general purpose usage (mobile-versatile-1)
  • higher prediction quality (mobile-high-accuracy-1)

Web UI

  1. Open the Vision Dashboard.

    The Datasets page shows the available datasets for the current project.

    Listing a dataset page
  2. Select the dataset you want to use to train the custom model.

  3. When the dataset is ready, select the Train tab and Train new model button.

    This will open a "Train new model" side window with training options.

  4. From the training Define your model section, change the model name (or use the default value) and select Edge as the model type. After selecting to train an Edge model select Continue. Train edge model radio button image

  5. In the following Optimize model for section, select your desired optimization criterion: Higher accuracy, Best tradeoff, or Faster prediction. After selecting the optimization specification select Continue.

    Best trade off radio button image

  6. In the following Set a node hour budget section use the recommended node hour budget, or specify a different value.

    By default, 24 node hours should be sufficient for most datasets to train your model. This recommended value is an estimation to make the model fully converged. However, you can choose another amount. The minimum amount of node hours for Image Classification is 8. For Object Detection this minimum amount is 20.

    setting the node budget section

  7. Select Start training to begin model training.

Training a model can take several hours to complete. After the model is successfully trained, you will receive a message at the e-mail address that you used for your Google Cloud Platform project.

REST

When training Edge models you can specify three distinct values in the modelType field depending on your model needs:

  • mobile-low-latency-1 for low latency
  • mobile-versatile-1 for general purpose usage
  • mobile-high-accuracy-1 for higher prediction quality

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

  • project-id: your GCP project ID.
  • display-name: a string display name of your choosing.
  • dataset-id: the ID of your dataset. The ID is the last element of the name of your dataset. For example:
    • dataset name: projects/project-id/locations/location-id/datasets/3104518874390609379
    • dataset id: 3104518874390609379

HTTP method and URL: 

POST https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models

Request JSON body: 

{
  "displayName": "display-name",
  "datasetId": "dataset-id",
  "imageClassificationModelMetadata": {
    "trainBudget": "1",
    "modelType": "mobile-low-latency-1"
  }
}

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 print-access-token)" \
    -H "x-goog-user-project: project-id" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d @request.json \
    "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models"

PowerShell

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

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models" | Select-Object -Expand Content

You should see output similar to the following. You can use the operation ID (ICN2106290444865378475, in this case) to get the status of the task. For an example, see Working with long-running operations.

{
  "name": "projects/project-id/locations/us-central1/operations/ICN2106290444865378475",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-10-30T20:06:08.253243Z",
    "updateTime": "2019-10-30T20:06:08.253243Z",
    "createModelDetails": {}
  }
}

Go

Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
)

// visionClassificationCreateModel creates a model for image classification.
func visionClassificationCreateModel(w io.Writer, projectID string, location string, datasetID string, modelName string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// datasetID := "ICN123456789..."
	// modelName := "model_display_name"

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %v", err)
	}
	defer client.Close()

	req := &automlpb.CreateModelRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		Model: &automlpb.Model{
			DisplayName: modelName,
			DatasetId:   datasetID,
			ModelMetadata: &automlpb.Model_ImageClassificationModelMetadata{
				ImageClassificationModelMetadata: &automlpb.ImageClassificationModelMetadata{
					TrainBudgetMilliNodeHours: 1000, // 1000 milli-node hours are 1 hour
				},
			},
		},
	}

	op, err := client.CreateModel(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateModel: %v", err)
	}
	fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())
	fmt.Fprintf(w, "Training started...\n")

	return nil
}

Java

Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.ImageClassificationModelMetadata;
import com.google.cloud.automl.v1.LocationName;
import com.google.cloud.automl.v1.Model;
import com.google.cloud.automl.v1.OperationMetadata;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class VisionClassificationCreateModel {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String datasetId = "YOUR_DATASET_ID";
    String displayName = "YOUR_DATASET_NAME";
    createModel(projectId, datasetId, displayName);
  }

  // Create a model
  static void createModel(String projectId, String datasetId, String displayName)
      throws IOException, ExecutionException, InterruptedException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // A resource that represents Google Cloud Platform location.
      LocationName projectLocation = LocationName.of(projectId, "us-central1");
      // Set model metadata.
      ImageClassificationModelMetadata metadata =
          ImageClassificationModelMetadata.newBuilder().setTrainBudgetMilliNodeHours(24000).build();
      Model model =
          Model.newBuilder()
              .setDisplayName(displayName)
              .setDatasetId(datasetId)
              .setImageClassificationModelMetadata(metadata)
              .build();

      // Create a model with the model metadata in the region.
      OperationFuture<Model, OperationMetadata> future =
          client.createModelAsync(projectLocation, model);
      // OperationFuture.get() will block until the model is created, which may take several hours.
      // You can use OperationFuture.getInitialFuture to get a future representing the initial
      // response to the request, which contains information while the operation is in progress.
      System.out.format("Training operation name: %s\n", future.getInitialFuture().get().getName());
      System.out.println("Training started...");
    }
  }
}

Node.js

Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const dataset_id = 'YOUR_DATASET_ID';
// const displayName = 'YOUR_DISPLAY_NAME';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function createModel() {
  // Construct request
  const request = {
    parent: client.locationPath(projectId, location),
    model: {
      displayName: displayName,
      datasetId: datasetId,
      imageClassificationModelMetadata: {
        trainBudgetMilliNodeHours: 24000,
      },
    },
  };

  // Don't wait for the LRO
  const [operation] = await client.createModel(request);
  console.log(`Training started... ${operation}`);
  console.log(`Training operation name: ${operation.name}`);
}

createModel();

Python

Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# dataset_id = "YOUR_DATASET_ID"
# display_name = "your_models_display_name"

client = automl.AutoMlClient()

# A resource that represents Google Cloud Platform location.
project_location = f"projects/{project_id}/locations/us-central1"
# Leave model unset to use the default base model provided by Google
# train_budget_milli_node_hours: The actual train_cost will be equal or
# less than this value.
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#imageclassificationmodelmetadata
metadata = automl.ImageClassificationModelMetadata(
    train_budget_milli_node_hours=24000
)
model = automl.Model(
    display_name=display_name,
    dataset_id=dataset_id,
    image_classification_model_metadata=metadata,
)

# Create a model with the model metadata in the region.
response = client.create_model(parent=project_location, model=model)

print("Training operation name: {}".format(response.operation.name))
print("Training started...")

List operations status

Use the following code samples to list your project's operations, and filter results.

REST

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

  • project-id: your GCP project ID.

HTTP method and URL: 

GET https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/operations

To send your request, choose one of these options:

curl

Execute the following command: 

curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "x-goog-user-project: project-id" \
    "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/operations"

PowerShell

Execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/operations" | Select-Object -Expand Content

The output you see will vary depending on the operations you have requested.

You can also filter the operations returned by using select query parameters (operationId, done, and worksOn). For example, to return a list of operations that have finished running modify the URL:

GET https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/operations?filter="done=true"

Go

Before trying this sample, follow the setup instructions for this language on the APIs & Reference > Client Libraries page.

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	longrunning "cloud.google.com/go/longrunning/autogen/longrunningpb"
	"google.golang.org/api/iterator"
)

// listOperationStatus lists existing operations' status.
func listOperationStatus(w io.Writer, projectID string, location string) error {
	// projectID := "my-project-id"
	// location := "us-central1"

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %v", err)
	}
	defer client.Close()

	req := &longrunning.ListOperationsRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
	}

	it := client.LROClient.ListOperations(ctx, req)

	// Iterate over all results
	for {
		op, err := it.Next()
		if err == iterator.Done {
			break
		}
		if err != nil {
			return fmt.Errorf("ListOperations.Next: %v", err)
		}

		fmt.Fprintf(w, "Name: %v\n", op.GetName())
		fmt.Fprintf(w, "Operation details:\n")
		fmt.Fprintf(w, "%v", op)
	}

	return nil
}

Java

Before trying this sample, follow the setup instructions for this language on the APIs & Reference > Client Libraries page.

import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.LocationName;
import com.google.longrunning.ListOperationsRequest;
import com.google.longrunning.Operation;
import java.io.IOException;

class ListOperationStatus {

  static void listOperationStatus() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    listOperationStatus(projectId);
  }

  // Get the status of an operation
  static void listOperationStatus(String projectId) throws IOException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // A resource that represents Google Cloud Platform location.
      LocationName projectLocation = LocationName.of(projectId, "us-central1");

      // Create list operations request.
      ListOperationsRequest listrequest =
          ListOperationsRequest.newBuilder().setName(projectLocation.toString()).build();

      // List all the operations names available in the region by applying filter.
      for (Operation operation :
          client.getOperationsClient().listOperations(listrequest).iterateAll()) {
        System.out.println("Operation details:");
        System.out.format("\tName: %s\n", operation.getName());
        System.out.format("\tMetadata Type Url: %s\n", operation.getMetadata().getTypeUrl());
        System.out.format("\tDone: %s\n", operation.getDone());
        if (operation.hasResponse()) {
          System.out.format("\tResponse Type Url: %s\n", operation.getResponse().getTypeUrl());
        }
        if (operation.hasError()) {
          System.out.println("\tResponse:");
          System.out.format("\t\tError code: %s\n", operation.getError().getCode());
          System.out.format("\t\tError message: %s\n\n", operation.getError().getMessage());
        }
      }
    }
  }
}

Node.js

Before trying this sample, follow the setup instructions for this language on the APIs & Reference > Client Libraries page.

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function listOperationStatus() {
  // Construct request
  const request = {
    name: client.locationPath(projectId, location),
    filter: `worksOn=projects/${projectId}/locations/${location}/models/*`,
  };

  const [response] = await client.operationsClient.listOperations(request);

  console.log('List of operation status:');
  for (const operation of response) {
    console.log(`Name: ${operation.name}`);
    console.log('Operation details:');
    console.log(`${operation}`);
  }
}

listOperationStatus();

Python

Before trying this sample, follow the setup instructions for this language on the APIs & Reference > Client Libraries page.

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"

client = automl.AutoMlClient()
# A resource that represents Google Cloud Platform location.
project_location = f"projects/{project_id}/locations/us-central1"
# List all the operations names available in the region.
response = client._transport.operations_client.list_operations(
    name=project_location, filter_="", timeout=5
)

print("List of operations:")
for operation in response:
    print("Name: {}".format(operation.name))
    print("Operation details:")
    print(operation)

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the AutoML Vision Object Detection reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the AutoML Vision Object Detection reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the AutoML Vision Object Detection reference documentation for Ruby.

Working with long-running operations

REST

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

  • project-id: your GCP project ID.
  • operation-id: the ID of your operation. The ID is the last element of the name of your operation. For example:
    • operation name: projects/project-id/locations/location-id/operations/IOD5281059901324392598
    • operation id: IOD5281059901324392598

HTTP method and URL: 

GET https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/operations/operation-id

To send your request, choose one of these options:

curl

Execute the following command: 

curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "x-goog-user-project: project-id" \
    "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/operations/operation-id"

PowerShell

Execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/operations/operation-id" | Select-Object -Expand Content
You should see output similar to the following for a completed import operation: 
{
  "name": "projects/project-id/locations/us-central1/operations/operation-id",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2018-10-29T15:56:29.176485Z",
    "updateTime": "2018-10-29T16:10:41.326614Z",
    "importDataDetails": {}
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"
  }
}

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

{
  "name": "projects/project-id/locations/us-central1/operations/operation-id",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-07-22T18:35:06.881193Z",
    "updateTime": "2019-07-22T19:58:44.972235Z",
    "createModelDetails": {}
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.Model",
    "name": "projects/project-id/locations/us-central1/models/model-id"
  }
}

Go

Before trying this sample, follow the setup instructions for this language on the APIs & Reference > Client Libraries page.

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
)

// getOperationStatus gets an operation's status.
func getOperationStatus(w io.Writer, projectID string, location string, datasetID string, modelName string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// datasetID := "ICN123456789..."
	// modelName := "model_display_name"

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %v", err)
	}
	defer client.Close()

	req := &automlpb.CreateModelRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		Model: &automlpb.Model{
			DisplayName: modelName,
			DatasetId:   datasetID,
			ModelMetadata: &automlpb.Model_ImageClassificationModelMetadata{
				ImageClassificationModelMetadata: &automlpb.ImageClassificationModelMetadata{
					TrainBudgetMilliNodeHours: 1000, // 1000 milli-node hours are 1 hour
				},
			},
		},
	}

	op, err := client.CreateModel(ctx, req)
	if err != nil {
		return err
	}
	fmt.Fprintf(w, "Name: %v\n", op.Name())

	// Wait for the longrunning operation complete.
	resp, err := op.Wait(ctx)
	if err != nil && !op.Done() {
		fmt.Println("failed to fetch operation status", err)
		return err
	}
	if err != nil && op.Done() {
		fmt.Println("operation completed with error", err)
		return err
	}
	fmt.Fprintf(w, "Response: %v\n", resp)

	return nil
}

Java

Before trying this sample, follow the setup instructions for this language on the APIs & Reference > Client Libraries page.

import com.google.cloud.automl.v1.AutoMlClient;
import com.google.longrunning.Operation;
import java.io.IOException;

class GetOperationStatus {

  static void getOperationStatus() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String operationFullId = "projects/[projectId]/locations/us-central1/operations/[operationId]";
    getOperationStatus(operationFullId);
  }

  // Get the status of an operation
  static void getOperationStatus(String operationFullId) throws IOException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // Get the latest state of a long-running operation.
      Operation operation = client.getOperationsClient().getOperation(operationFullId);

      // Display operation details.
      System.out.println("Operation details:");
      System.out.format("\tName: %s\n", operation.getName());
      System.out.format("\tMetadata Type Url: %s\n", operation.getMetadata().getTypeUrl());
      System.out.format("\tDone: %s\n", operation.getDone());
      if (operation.hasResponse()) {
        System.out.format("\tResponse Type Url: %s\n", operation.getResponse().getTypeUrl());
      }
      if (operation.hasError()) {
        System.out.println("\tResponse:");
        System.out.format("\t\tError code: %s\n", operation.getError().getCode());
        System.out.format("\t\tError message: %s\n", operation.getError().getMessage());
      }
    }
  }
}

Node.js

Before trying this sample, follow the setup instructions for this language on the APIs & Reference > Client Libraries page.

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const operationId = 'YOUR_OPERATION_ID';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function getOperationStatus() {
  // Construct request
  const request = {
    name: `projects/${projectId}/locations/${location}/operations/${operationId}`,
  };

  const [response] = await client.operationsClient.getOperation(request);

  console.log(`Name: ${response.name}`);
  console.log('Operation details:');
  console.log(`${response}`);
}

getOperationStatus();

Python

Before trying this sample, follow the setup instructions for this language on the APIs & Reference > Client Libraries page.

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# operation_full_id = \
#     "projects/[projectId]/locations/us-central1/operations/[operationId]"

client = automl.AutoMlClient()
# Get the latest state of a long-running operation.
response = client._transport.operations_client.get_operation(operation_full_id)

print("Name: {}".format(response.name))
print("Operation details:")
print(response)

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the AutoML Vision reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the AutoML Vision reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the AutoML Vision reference documentation for Ruby.

Cancelling an 0peration

You can cancel an import or training task using the operation ID.

REST

Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

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

  • project-id: your GCP project ID.
  • operation-id: the ID of your operation. The ID is the last element of the name of your operation. For example:
    • operation name: projects/project-id/locations/location-id/operations/IOD5281059901324392598
    • operation id: IOD5281059901324392598

HTTP method and URL: 

POST https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/operations/operation-id:cancel

To send your request, choose one of these options:

curl

Execute the following command: 

curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "x-goog-user-project: project-id" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d "" \
    "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/operations/operation-id:cancel"

PowerShell

Execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -Uri "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/operations/operation-id:cancel" | Select-Object -Expand Content
You will see an empty JSON object returned from a successful request: 
{}

Getting information about a model

Use the following code samples to get information about a specific trained model. You can use the information returned from this request to modify the mode or send a prediction request.

REST

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

  • project-id: your GCP project ID.
  • model-id: the ID of your model, from the response when you created the model. The ID is the last element of the name of your model. For example:
    • model name: projects/project-id/locations/location-id/models/IOD4412217016962778756
    • model id: IOD4412217016962778756

HTTP method and URL: 

GET https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id

To send your request, choose one of these options:

curl

Execute the following command: 

curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "x-goog-user-project: project-id" \
    "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id"

PowerShell

Execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/project-id/locations/us-central1/models/model-id",
  "displayName": "display-name",
  "datasetId": "dataset-id",
  "createTime": "2019-10-30T20:06:08.253243Z",
  "deploymentState": "UNDEPLOYED",
  "updateTime": "2019-10-30T20:54:50.472328Z",
  "imageClassificationModelMetadata": {
    "trainBudget": "1",
    "modelType": "mobile-low-latency-1",
    "nodeQps": 3.2
  }
}

Go

Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
)

// getModel gets a model.
func getModel(w io.Writer, projectID string, location string, modelID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// modelID := "TRL123456789..."

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %v", err)
	}
	defer client.Close()

	req := &automlpb.GetModelRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/models/%s", projectID, location, modelID),
	}

	model, err := client.GetModel(ctx, req)
	if err != nil {
		return fmt.Errorf("GetModel: %v", err)
	}

	// Retrieve deployment state.
	deploymentState := "undeployed"
	if model.GetDeploymentState() == automlpb.Model_DEPLOYED {
		deploymentState = "deployed"
	}

	// Display the model information.
	fmt.Fprintf(w, "Model name: %v\n", model.GetName())
	fmt.Fprintf(w, "Model display name: %v\n", model.GetDisplayName())
	fmt.Fprintf(w, "Model create time:\n")
	fmt.Fprintf(w, "\tseconds: %v\n", model.GetCreateTime().GetSeconds())
	fmt.Fprintf(w, "\tnanos: %v\n", model.GetCreateTime().GetNanos())
	fmt.Fprintf(w, "Model deployment state: %v\n", deploymentState)

	return nil
}

Java

Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.Model;
import com.google.cloud.automl.v1.ModelName;
import java.io.IOException;

class GetModel {

  static void getModel() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    getModel(projectId, modelId);
  }

  // Get a model
  static void getModel(String projectId, String modelId) throws IOException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // Get the full path of the model.
      ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
      Model model = client.getModel(modelFullId);

      // Display the model information.
      System.out.format("Model name: %s\n", model.getName());
      // To get the model id, you have to parse it out of the `name` field. As models Ids are
      // required for other methods.
      // Name Format: `projects/{project_id}/locations/{location_id}/models/{model_id}`
      String[] names = model.getName().split("/");
      String retrievedModelId = names[names.length - 1];
      System.out.format("Model id: %s\n", retrievedModelId);
      System.out.format("Model display name: %s\n", model.getDisplayName());
      System.out.println("Model create time:");
      System.out.format("\tseconds: %s\n", model.getCreateTime().getSeconds());
      System.out.format("\tnanos: %s\n", model.getCreateTime().getNanos());
      System.out.format("Model deployment state: %s\n", model.getDeploymentState());
    }
  }
}

Node.js

Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const modelId = 'YOUR_MODEL_ID';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function getModel() {
  // Construct request
  const request = {
    name: client.modelPath(projectId, location, modelId),
  };

  const [response] = await client.getModel(request);

  console.log(`Model name: ${response.name}`);
  console.log(
    `Model id: ${
      response.name.split('/')[response.name.split('/').length - 1]
    }`
  );
  console.log(`Model display name: ${response.displayName}`);
  console.log('Model create time');
  console.log(`\tseconds ${response.createTime.seconds}`);
  console.log(`\tnanos ${response.createTime.nanos / 1e9}`);
  console.log(`Model deployment state: ${response.deploymentState}`);
}

getModel();

Python

Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"

client = automl.AutoMlClient()
# Get the full path of the model.
model_full_id = client.model_path(project_id, "us-central1", model_id)
model = client.get_model(name=model_full_id)

# Retrieve deployment state.
if model.deployment_state == automl.Model.DeploymentState.DEPLOYED:
    deployment_state = "deployed"
else:
    deployment_state = "undeployed"

# Display the model information.
print("Model name: {}".format(model.name))
print("Model id: {}".format(model.name.split("/")[-1]))
print("Model display name: {}".format(model.display_name))
print("Model create time: {}".format(model.create_time))
print("Model deployment state: {}".format(deployment_state))

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the AutoML Vision Object Detection reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the AutoML Vision Object Detection reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the AutoML Vision Object Detection reference documentation for Ruby.

Resumable training

You can now pause and resume your custom model training for large datasets (with more than one thousand images). Pausing model training isn't a manual action involving a pause button. Instead, the user sets the node hour budget to a specified time, for example, two hours. Once the time is reached, the training stops automatically regardless of the model status.

Resumable training is available with the following restrictions:

  • Base model time limit - You can resume training only on models that have been trained within the last 14 days; base models created more than 14 days before your request are not eligible for resumable training.
  • No label modification - Resumable training fails if you change the labels in the base model's dataset.
  • No guarantee of better performance - Using resumable training on a model does not guarantee better model performance.

Web UI

Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

  1. Open the AutoML Vision UI.

    The Datasets page shows the available datasets for the current project.

    Dataset list page

  2. Select the dataset you want to use to train the custom model.

    The display name of the selected dataset appears in the title bar, and the page lists the individual items in the dataset along with their labels.

    Image items page

  3. When you are done reviewing the dataset, select the Train tab just below the title bar.

    If you previously trained models they will appear in a list in this tab.

    List of trained models

  4. If resumable training is available for a model it will appear as an option near the See full evaluation option.

    Select Resume training to resume training on an applicable model.

    Resume training option

  5. Similar to training the original model, selecting Resume training will open a "Train new model" side window. In the Define your model section you can change the name of the new model. You will also see the name of the base model used to train this model.

    Define your model side window

    After defining your model select Continue to move to the next section.

  6. In the following Set a node hour budget section specify a node hour budget. This budget is subject to a pricing structure and quota restrictions similar to initial model training.

    After specifying a training budget select Start training. You will receive an email when resumable training is finished.

REST

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

  • base-model-id: the ID of the base (existent) model. The new model is created based on this base model. The following new model specifications must match the base model:

    * Base and new modelType will be checked even if this field is omitted in the new model request.

  • project-id: your GCP project ID.
  • display-name: a string display name of your choosing.
  • dataset-id: the ID of your dataset. The ID is the last element of the name of your dataset. For example:
    • dataset name: projects/project-id/locations/location-id/datasets/3104518874390609379
    • dataset id: 3104518874390609379

HTTP method and URL: 

POST https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models

Request JSON body: 

{
  "baseModelId": base-model-id,
  "displayName": "display-name",
  "datasetId": "dataset-id",
  "imageClassificationModelMetadata": {
    "trainBudget": "1",
    "modelType": "mobile-low-latency-1"
  }
}

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 print-access-token)" \
    -H "x-goog-user-project: project-id" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d @request.json \
    "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models"

PowerShell

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

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models" | Select-Object -Expand Content

You should see output similar to the following. You can use the operation ID (ICN2106290444865378475, in this case) to get the status of the task. For an example, see Working with long-running operations.

{
  "name": "projects/project-id/locations/us-central1/operations/ICN2106290444865378475",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-10-30T20:06:08.253243Z",
    "updateTime": "2019-10-30T20:06:08.253243Z",
    "createModelDetails": {}
  }
}