prefect-gcp 0.2.6

Prefect tasks and subflows for interacting with Google Cloud Platform.

Coordinate and use GCP in your dataflow with prefect-gcp

Visit the full docs here to see additional examples and the API reference.

The prefect-openai collection makes it easy to leverage the capabilities of Google Cloud Platform (GCP) in your flows. Check out the examples below to get started!

Getting Started

Saving credentials to a block

You will need to obtain GCP credentials in order to use prefect-gcp.

1. Refer to the GCP service account documentation on how to create and download a service account key file
2. Copy the JSON contents
3. Create a short script, replacing the placeholders (or do so in the UI)

from prefect_gcp import GcpCredentials

# replace this PLACEHOLDER dict with your own service account info
service_account_info = {
  "type": "service_account",
  "project_id": "PROJECT_ID",
  "private_key_id": "KEY_ID",
  "private_key": "-----BEGIN PRIVATE KEY-----\nPRIVATE_KEY\n-----END PRIVATE KEY-----\n",
  "client_email": "SERVICE_ACCOUNT_EMAIL",
  "client_id": "CLIENT_ID",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://accounts.google.com/o/oauth2/token",
  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/SERVICE_ACCOUNT_EMAIL"
}

GcpCredentials(
    service_account_info=service_account_info
).save("BLOCK_NAME_PLACEHOLDER")

Congrats! You can now easily load the saved block, which holds your credentials:

from prefect_gcp import GcpCredentials
GcpCredentials.load("BLOCK_NAME_PLACEHOLDER")

!!! info Registering blocks

Register blocks in this module to
[view and edit them](https://orion-docs.prefect.io/ui/blocks/)
on Prefect Cloud:

```bash
prefect block register -m prefect_gcp
```

Download blob from bucket

from prefect import flow
from prefect_gcp.cloud_storage import GcsBucket

@flow
def download_flow():
    gcs_bucket = GcsBucket.load("my-bucket")
    path = gcs_bucket.download_object_to_path("my_folder/notes.txt", "notes.txt")
    return path

download_flow()

Deploy command on Cloud Run

Save the following as prefect_gcp_flow.py:

from prefect import flow
from prefect_gcp import GcpCredentials
from prefect_gcp.cloud_run import CloudRunJob

@flow
def cloud_run_job_flow():
    cloud_run_job = CloudRunJob(
        image="us-docker.pkg.dev/cloudrun/container/job:latest",
        credentials=GcpCredentials.load("MY_BLOCK_NAME"),
        region="us-central1",
        command=["echo", "hello world"],
    )
    return cloud_run_job.run()

Deploy prefect_gcp_flow.py:

from prefect.deployments import Deployment
from prefect_gcp_flow import cloud_run_job_flow

deployment = Deployment.build_from_flow(
    flow=cloud_run_job_flow,
    name="cloud_run_job_deployment", 
    version=1, 
    work_queue_name="demo",
)
deployment.apply()

Run the deployment either on the UI or through the CLI:

prefect deployment run cloud-run-job-flow/cloud_run_job_deployment

Visit Prefect Deployments for more information about deployments.

Get Google auth credentials from GcpCredentials

To instantiate a Google Cloud client, like bigquery.Client, GcpCredentials is not a valid input. Instead, use the get_credentials_from_service_account method.

import google.cloud.bigquery
from prefect import flow
from prefect_gcp import GcpCredentials

@flow
def create_bigquery_client():
    gcp_credentials_block = GcpCredentials.load("BLOCK_NAME")
    google_auth_credentials = gcp_credentials_block.get_credentials_from_service_account()
    bigquery_client = bigquery.Client(credentials=google_auth_credentials)

Or simply call get_bigquery_client from GcpCredentials.

from prefect import flow
from prefect_gcp import GcpCredentials

@flow
def create_bigquery_client():
    gcp_credentials_block = GcpCredentials.load("BLOCK_NAME")
    bigquery_client = gcp_credentials_block.get_bigquery_client()

Deploy command on Vertex AI as a flow

Save the following as prefect_gcp_flow.py:

from prefect import flow
from prefect_gcp.credentials import GcpCredentials
from prefect_gcp.aiplatform import VertexAICustomTrainingJob

@flow
def vertex_ai_job_flow():
    gcp_credentials = GcpCredentials.load("MY_BLOCK")
    job = VertexAICustomTrainingJob(
        command=["echo", "hello world"],
        region="us-east1",
        image="us-docker.pkg.dev/cloudrun/container/job:latest",
        gcp_credentials=gcp_credentials,
    )
    job.run()

vertex_ai_job_flow()

Deploy prefect_gcp_flow.py:

from prefect.deployments import Deployment
from prefect_gcp_flow import vertex_ai_job_flow

deployment = Deployment.build_from_flow(
    flow=vertex_ai_job_flow,
    name="vertex-ai-job-deployment", 
    version=1, 
    work_queue_name="demo",
)
deployment.apply()

Run the deployment either on the UI or through the CLI:

prefect deployment run vertex-ai-job-flow/vertex-ai-job-deployment

Visit Prefect Deployments for more information about deployments.

Resources

For more tips on how to use tasks and flows in a Collection, check out Using Collections!

Installation

To use prefect-gcp and Cloud Run:

pip install prefect-gcp

To use Cloud Storage:

pip install "prefect-gcp[cloud_storage]"

To use BigQuery:

pip install "prefect-gcp[bigquery]"

To use Secret Manager:

pip install "prefect-gcp[secret_manager]"

To use Vertex AI:

pip install "prefect-gcp[aiplatform]"

A list of available blocks in prefect-gcp and their setup instructions can be found here.

Requires an installation of Python 3.7+.

We recommend using a Python virtual environment manager such as pipenv, conda or virtualenv.

These tasks are designed to work with Prefect 2.0. For more information about how to use Prefect, please refer to the Prefect documentation.

Feedback

If you encounter any bugs while using prefect-gcp, feel free to open an issue in the prefect-gcp repository.

If you have any questions or issues while using prefect-gcp, you can find help in either the Prefect Discourse forum or the Prefect Slack community.

Feel free to star or watch prefect-gcp for updates too!

Contributing

If you'd like to help contribute to fix an issue or add a feature to prefect-gcp, please propose changes through a pull request from a fork of the repository.

Here are the steps:

1. Fork the repository
2. Clone the forked repository
3. Install the repository and its dependencies:

pip install -e ".[dev]"

4. Make desired changes
5. Add tests
6. Insert an entry to CHANGELOG.md
7. Install pre-commit to perform quality checks prior to commit:

pre-commit install

8. git commit, git push, and create a pull request