Cloud-hosted model quickstart

This quickstart walks you through the process of:

  • Copying a set of images into Google Cloud Storage.
  • Creating a CSV listing the images and their labels.
  • Using AutoML Vision to create your dataset, and train and deploy your model.

Before you begin

Set up your project

  1. Sign in to your Google Account.

    If you don't already have one, sign up for a new account.

  2. In the Cloud Console, on the project selector page, select or create a Cloud project.

    Go to the project selector page

  3. Make sure that billing is enabled for your Google Cloud project. Learn how to confirm billing is enabled for your project.

  4. Enable the AutoML and Cloud Storage APIs.

    Enable the APIs

  5. Install the gcloud command line tool.
  6. Follow the instructions to create a service account and download a key file for that account.
  7. Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path to the service account key file that you downloaded when you created the service account.
  8. Set the PROJECT_ID environment variable to your Project ID.
    export PROJECT_ID=your-project-id
    The AutoML API calls and resource names include your Project ID in them. The PROJECT_ID environment variable provides a convenient way to specify the ID.
  9. If you are an owner for your project, add your service account to the AutoML Editor IAM role, replacing service-account-name with the name of your new service account. For example,
    gcloud auth login
    gcloud projects add-iam-policy-binding $PROJECT_ID \
       --member="serviceAccount:service-account-name" \
  10. Otherwise (if you are not a project owner), ask a project owner to add both your user ID and your service account to the AutoML Editor IAM role.

Create a Cloud Storage bucket

Use Cloud Shell, a browser-based Linux command line connected to your Cloud Console project, to create your Cloud Storage bucket:

  1. Open Cloud Shell.

  2. Create a Google Cloud Storage bucket. The bucket name must be in the format: project-id-vcm. The following command creates a storage bucket in the us-central1 region named project-id-vcm. For a complete list of available regions, see the Bucket Locations page.

    gsutil mb -p project-id -c regional -l us-central1 gs://project-id-vcm/

    Recommended file structure for your Cloud Storage files:


Copy the sample images into your bucket

Next, copy the flower dataset used in this Tensorflow blog post. The images are stored in a public Cloud Storage bucket, so you can copy them directly from there to your own bucket.

  1. In your Cloud Shell session, enter:

    gsutil -m cp -R gs://cloud-ml-data/img/flower_photos/ gs://${BUCKET}/img/

    The file copying takes about 20 minutes to complete.

Create the CSV file

The sample dataset contains a CSV file with all of the image locations and the labels for each image. You'll use that to create your own CSV file:

  1. Update the CSV file to point to the files in your own bucket:

    gsutil cat gs://${BUCKET}/img/flower_photos/all_data.csv | sed "s:cloud-ml-data:${BUCKET}:" > all_data.csv
  2. Copy the CSV file into your bucket:

    gsutil cp all_data.csv gs://${BUCKET}/csv/

Create your dataset

Visit the AutoML Vision UI to begin the process of creating your dataset and training your model.

When prompted, make sure to select the project that you used for your Cloud Storage bucket.

  1. From the AutoML Vision page, click New Dataset:

    New dataset button in console

  2. Specify a name for this dataset. Click the + sign to continue.

    New dataset name field

  3. Specify the Cloud Storage URI of your CSV file. For this quickstart, the CSV file is at gs://your-project-123-vcm/csv/all_data.csv. Make sure to replace your-project-123 with your specific project ID.

  4. Click Create Dataset. The import process takes a few minutes. When it completes, you are taken to the next page which has details on all of the images identified for your dataset, both labeled and unlabeled images. You can filter images by label by selecting a label under Filter labels. If you are using the flower dataset, you will see a warning alert which will notify you of repeated images or images with multiple labels (if multi-label is not enabled).

    Filtering by label example

    • You can add additional images and update labels for new and existing images after you have imported a CSV file.

Train your model

Once your dataset has been created and processed, click the Train tab to initiate model training.

Click Train New Model to continue.

Training is initiated for your model. Training your model should take about 10 minutes for this dataset. The service will email you once training has completed, or if any errors occur.

Once training is complete, your model is automatically deployed.

You can click the Evaluate tab to get more details on F1, Precision, and Recall scores. Click on each label under Filter labels to get details on true positives, false negatives and false positives.

Use the custom model

After your model has been successfully trained, you can use it to label images using your custom model. Select the Test and Use tab.

If you didn't opt-in for auto-deploy you will be prompted to deploy your model before you can make predictions.

Make a Prediction

Click the Predict tab for instructions on sending an image to your model for a prediction. You can also refer to Making an online prediction or Making batch predictions for examples.


If you no longer need your custom model or dataset, you can delete them.

To avoid unnecessary Google Cloud Platform charges, use the GCP Console to delete your project if you do not need it.

Undeploy your model

Your model incurs charges while it is deployed.

  1. Select the Test & Use tab just below the title bar.
  2. Select Remove deployment from the banner beneath your model name to open the undeploy option window.

    undeploy popup menu

  3. Select Remove deployment to undeploy the model.

    model deploying

  4. You will receive an email when model undeployment has completed.

    deploy finished email

Delete your project (optional)

To avoid unnecessary Google Cloud Platform charges, use the Cloud Console to delete your project if you do not need it.