Processing Landsat satellite images with GPUs

Note: The following considerations apply to this GA offering:

• Jobs using GPUs incur charges as specified in the Dataflow pricing page.
• You must use Dataflow Runner v2.

This tutorial shows you how to use GPUs on Dataflow to process Landsat 8 satellite images and render them as JPEG files. The tutorial is based on the example Processing Landsat satellite images with GPUs. If you'd like to run the pipeline using Dataflow Prime, use the Prime-enabled version of the tutorial.

Objectives

• Build a Docker image for Dataflow that has TensorFlow with GPU support.
• Run a Dataflow job with GPUs.

Costs

This tutorial uses billable components of Google Cloud, including:

• Cloud Storage
• Dataflow
• Container Registry

Use the pricing calculator to generate a cost estimate based on your projected usage.

Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

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

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

4. Enable the Dataflow and Cloud Build APIs.

   Enable the APIs

5. Create a service account:

   1. In the Google Cloud console, go to the Create service account page.

      Go to Create service account
   2. Select your project.

   3. In the Service account name field, enter a name. The Google Cloud console fills in the Service account ID field based on this name.

      In the Service account description field, enter a description. For example, Service account for quickstart.

   4. Click Create and continue.

   5. To provide access to your project, grant the following role(s) to your service account: Project > Owner.

      In the Select a role list, select a role.

      For additional roles, click Add another role and add each additional role.

      Note: The Role field affects which resources your service account can access in your project. You can revoke these roles or grant additional roles later. In production environments, do not grant the Owner, Editor, or Viewer roles. Instead, grant a predefined role or custom role that meets your needs.
   6. Click Continue.

   7. Click Done to finish creating the service account.

      Do not close your browser window. You will use it in the next step.

6. Create a service account key:

   1. In the Google Cloud console, click the email address for the service account that you created.
   2. Click Keys.
   3. Click Add key, and then click Create new key.
   4. Click Create. A JSON key file is downloaded to your computer.
   5. Click Close.

7. Set the environment variable GOOGLE_APPLICATION_CREDENTIALS to the path of the JSON file that contains your service account key. This variable only applies to your current shell session, so if you open a new session, set the variable again.

   Example: Linux or macOS

   export GOOGLE_APPLICATION_CREDENTIALS="KEY_PATH"

   Replace KEY_PATH with the path of the JSON file that contains your service account key.

   For example:

   export GOOGLE_APPLICATION_CREDENTIALS="/home/user/Downloads/service-account-file.json"

   Example: Windows

   For PowerShell:

   $env:GOOGLE_APPLICATION_CREDENTIALS="KEY_PATH"

   Replace KEY_PATH with the path of the JSON file that contains your service account key.

   For example:

   $env:GOOGLE_APPLICATION_CREDENTIALS="C:\Users\username\Downloads\service-account-file.json"

   For command prompt:

   set GOOGLE_APPLICATION_CREDENTIALS=KEY_PATH

   Replace KEY_PATH with the path of the JSON file that contains your service account key.

8. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

9. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

10. Enable the Dataflow and Cloud Build APIs.

    Enable the APIs

11. Create a service account:

    1. In the Google Cloud console, go to the Create service account page.

       Go to Create service account
    2. Select your project.

    3. In the Service account name field, enter a name. The Google Cloud console fills in the Service account ID field based on this name.

       In the Service account description field, enter a description. For example, Service account for quickstart.

    4. Click Create and continue.

    5. To provide access to your project, grant the following role(s) to your service account: Project > Owner.

       In the Select a role list, select a role.

       For additional roles, click Add another role and add each additional role.

       Note: The Role field affects which resources your service account can access in your project. You can revoke these roles or grant additional roles later. In production environments, do not grant the Owner, Editor, or Viewer roles. Instead, grant a predefined role or custom role that meets your needs.
    6. Click Continue.

    7. Click Done to finish creating the service account.

       Do not close your browser window. You will use it in the next step.

12. Create a service account key:

    1. In the Google Cloud console, click the email address for the service account that you created.
    2. Click Keys.
    3. Click Add key, and then click Create new key.
    4. Click Create. A JSON key file is downloaded to your computer.
    5. Click Close.

13. Set the environment variable GOOGLE_APPLICATION_CREDENTIALS to the path of the JSON file that contains your service account key. This variable only applies to your current shell session, so if you open a new session, set the variable again.

    Example: Linux or macOS

    export GOOGLE_APPLICATION_CREDENTIALS="KEY_PATH"

    Replace KEY_PATH with the path of the JSON file that contains your service account key.

    For example:

    export GOOGLE_APPLICATION_CREDENTIALS="/home/user/Downloads/service-account-file.json"

    Example: Windows

    For PowerShell:

    $env:GOOGLE_APPLICATION_CREDENTIALS="KEY_PATH"

    Replace KEY_PATH with the path of the JSON file that contains your service account key.

    For example:

    $env:GOOGLE_APPLICATION_CREDENTIALS="C:\Users\username\Downloads\service-account-file.json"

    For command prompt:

    set GOOGLE_APPLICATION_CREDENTIALS=KEY_PATH

    Replace KEY_PATH with the path of the JSON file that contains your service account key.

To store the output JPEG image files from this tutorial, create a Cloud Storage bucket:
1. In the Google Cloud console, go to the Cloud Storage Buckets page.

   Go to Buckets page

2. Click Create bucket.
3. On the Create a bucket page, enter your bucket information. To go to the next step, click Continue.
   • For Name your bucket, enter a unique bucket name. Don't include sensitive information in the bucket name, because the bucket namespace is global and publicly visible.
   • For Choose where to store your data, do the following:
     • Select a Location type option.
     • Select a Location option.
   • For Choose a default storage class for your data, select the following: Standard.
   • For Choose how to control access to objects, select an Access control option.
   • For Advanced settings (optional), specify an encryption method, a retention policy, or bucket labels.
4. Click Create.

Preparing your working environment

Before you can work through this tutorial, you must set up your development environment and download the starter files.

1. Clone the python-docs-samples repository.

   git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
   

2. Navigate to the sample code directory.

   cd python-docs-samples/dataflow/gpu-examples/tensorflow-landsat
   

Building the Docker image

Cloud Build allows you to build a Docker image using a Dockerfile and save it into Container Registry, where the image is accessible to other Google Cloud products.

We build the container image using the build.yaml config file.

gcloud builds submit --config build.yaml

Running the Dataflow job with GPUs

The following code block demonstrates how to launch this Dataflow pipeline with GPUs.

We run the Dataflow pipeline using the run.yaml config file.

Note: We launch the job using the container image to make sure the job launches with the same Python version as the workers and with all the dependencies installed.

export PROJECT=PROJECT_NAME
export BUCKET=BUCKET_NAME

export JOB_NAME="satellite-images-$(date +%Y%m%d-%H%M%S)"
export OUTPUT_PATH="gs://$BUCKET/samples/dataflow/landsat/output-images/"
export REGION="us-central1"
export GPU_TYPE="nvidia-tesla-t4"

gcloud builds submit \
    --config run.yaml \
    --substitutions _JOB_NAME=$JOB_NAME,_OUTPUT_PATH=$OUTPUT_PATH,_REGION=$REGION,_GPU_TYPE=$GPU_TYPE \
    --no-source

Replace the following:

• PROJECT_NAME: the Google Cloud project name
• BUCKET_NAME: the Cloud Storage bucket name (without the gs:// prefix)

After you run this pipeline, wait for the command to finish. If you exit your shell, you might lose the environment variables that you've set.

To avoid sharing the GPU between multiple worker processes, this sample uses a machine type with 1 vCPU. The memory requirements of the pipeline are addressed by using 13 GB of extended memory. For more information, read GPUs and worker parallelism.

Viewing your results

The pipeline in tensorflow-landsat/main.py processes Landsat 8 satellite images and renders them as JPEG files. Use the following steps to view these files.

1. List the output JPEG files with details by using gsutil.

   gsutil ls -lh "gs://$BUCKET/samples/dataflow/landsat/"
   

2. Copy the files into your local directory.

   mkdir outputs
   gsutil -m cp "gs://$BUCKET/samples/dataflow/landsat/*" outputs/
   

3. Open these image files with the image viewer of your choice.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Deleting the project

The easiest way to eliminate billing is to delete the project that you created for the tutorial.

To delete the project:

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for this tutorial, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple tutorials and quickstarts, reusing projects can help you avoid exceeding project quota limits.

1. In the Google Cloud console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.

What's next

• Look at a minimal GPU-enabled TensorFlow example
• Look at a minimal GPU-enabled PyTorch example
• Learn more about GPU support on Dataflow.
• Look through tasks for Using GPUs.
• Explore reference architectures, diagrams, tutorials, and best practices about Google Cloud. Take a look at our Cloud Architecture Center.