Based on the research, I'll help you set up a Cloud Run v2 work pool that's ready to start processing work. Here's how to do it: 1. Create the work pool using the CLI command:

bash
prefect work-pool create "my-cloud-run-pool" --type cloud-run-v2

2. To ensure it's not paused and ready to accept work, you can either: - Create it initially without pausing:

bash
     prefect work-pool create "my-cloud-run-pool" --type cloud-run-v2 --no-paused

- Or if it's already created and paused, resume it:

bash
     prefect work-pool resume "my-cloud-run-pool"

3. You can verify the work pool's status with:

bash
prefect work-pool inspect "my-cloud-run-pool"

Important Notes: 1. Make sure you have the necessary Google Cloud credentials configured in your environment 2. The work pool will need appropriate IAM permissions to create and manage Cloud Run jobs 3. You might want to configure additional settings like concurrency limits or job variables depending on your specific needs Let me know if you need help with any additional configuration or if you'd like to set up specific job templates for your Cloud Run v2 work pool!

Create your Cloud Run work pool:

bash
prefect work-pool create "cloud-run-pool" --type cloud-run:push

### Step 2: Prepare Your Flow Here's an example of how to structure your flow and deployment:

python
from prefect import flow
from datetime import timedelta

@flow(name="my-cloud-run-flow")
def my_flow(name: str = "world"):
    print(f"Hello {name}!")

if __name__ == "__main__":
    # Deploy the flow
    my_flow.from_source(
        source="<https://github.com/your-username/your-repo>",
        entrypoint="path/to/flow.py:my_flow",
    ).deploy(
        name="cloud-run-deployment",
        work_pool_name="cloud-run-pool",
        image="<http://gcr.io/your-project/your-image:tag|gcr.io/your-project/your-image:tag>",
        push=True,
        build=True
    )

### Step 3: Create a Dockerfile Create a Dockerfile in your project directory:

dockerfile
FROM prefecthq/prefect:2-python3.9

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

### Step 4: Deploy the Flow 1. First, ensure your code is in a Git repository (if you're using source control) 2. Run your deployment script:

bash
python path/to/your_flow_script.py

### Step 5: Run the Deployment Once deployed, you can run your flow using:

bash
prefect deployment run 'my-cloud-run-flow/cloud-run-deployment'

### Important Configuration Notes: 1. Environment Variables: You'll need to set up the following: - `PREFECT_API_URL`: Your Prefect API URL - Google Cloud credentials (typically through service account) 2. Job Template: You can customize the Cloud Run job template with: - Memory limits - CPU allocation - Environment variables - Service account configuration 3. Monitoring: You can monitor your deployments through: - Prefect UI - Google Cloud Console -

prefect deployment ls

command ### Common Issues and Solutions: 1. If the deployment fails, check: - Docker image build and push permissions - GCP service account permissions - Cloud Run API enablement - Network connectivity 2. For authentication issues: - Verify GCP credentials are properly configured - Ensure the service account has necessary permissions - Check Docker authentication with GCP Would you like me to elaborate on any of these steps or provide more specific details about any part of the process?

I can help you fix this error. The key is that you need to use

.from_source()

before

.deploy()

when working with Cloud Run work pools. Here's the corrected version of your deployment code:

python
my_flow.from_source(
    source=".",  # Use "." for current directory or specify your Git repo URL
    entrypoint="path/to/your_script.py:my_flow"  # Replace with your actual path
).deploy(
    name="my_flow",
    description="My flow",
    tags=[],
    schedule=None,
    parameters={
        "credentials_block_name": "gcp-credentials",
    },
    work_pool_name="workpool",
    image="my_image:latest",
    push=False,
)

There are two main ways to specify the source: 1. Local Directory:

python
my_flow.from_source(
    source=".",  # Uses current directory
    entrypoint="flow.py:my_flow"
).deploy(...)

2. Git Repository:

python
my_flow.from_source(
    source="<https://github.com/username/repo>",
    entrypoint="flows/flow.py:my_flow"
).deploy(...)

Important notes: 1. The

entrypoint

should be the path to your flow file relative to the source, followed by

:

and the flow function name 2. When using

push=False

, make sure your image is already built and available in the container registry 3. If you want Prefect to build and push the image for you, set

push=True

and

build=True

Would you like me to explain any part of this in more detail or help you with the specific source configuration for your deployment?

Ah, I see! Even though

my_flow

is already decorated with

@flow

, you still need to use

from_source()

with Cloud Run work pools because they need to know where to find your flow code when running in the cloud environment. Let me explain why and show you how to fix it. Let's say your project structure looks something like this:

your_project/
├── flows/
│   └── my_flow.py  # Your flow file
└── deployment.py   # Your deployment script

And your

my_flow.py

looks like this:

python
from prefect import flow

@flow
def my_flow(credentials_block_name: str):
    # your flow code here
    pass

Here's how to properly deploy it. In your deployment script:

python
from flows.my_flow import my_flow  # Import your flow

# Deploy the flow
my_flow.from_source(
    source=".",  # Uses the current directory as the source
    entrypoint="flows/my_flow.py:my_flow"  # Path to your flow file and function
).deploy(
    name="my_flow",
    description="My flow",
    tags=[],
    schedule=None,
    parameters={
        "credentials_block_name": "gcp-credentials",
    },
    work_pool_name="workpool",
    image="my_image:latest",
    push=True,  # This will push the image to your container registry
)

The key points to understand: 1.

from_source()

is required for Cloud Run work pools even if you've imported the flow directly 2. The

source="."

tells Prefect to use your local directory as the source 3. The

entrypoint

should point to your flow file relative to the source directory 4. The format is

"path/to/file.py:function_name"

If you're getting any specific errors with this setup, please share them and I can help you troubleshoot further! Also, make sure you have a Dockerfile in your project root that properly sets up your Python environment. Here's a basic example:

dockerfile
FROM prefecthq/prefect:2-python3.9

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

Would you like me to help you with any specific part of this setup or explain anything in more detail?

I see the issue now. You need to create your work pool specifically as a

cloud-run:push

type work pool. Here's how to fix it: 1. First, delete the existing work pool if it exists:

bash
prefect work-pool delete workpool

2. Create a new work pool with the correct type:

bash
prefect work-pool create "workpool" --type cloud-run:push

3. Then your deployment code should work as follows:

python
my_flow.from_source(
    source=".",  # Uses the current directory as the source
    entrypoint="path/to/your_flow.py:my_flow"
).deploy(
    name="my_flow",
    description="My flow",
    tags=[],
    schedule=None,
    parameters={
        "credentials_block_name": "gcp-credentials",
    },
    work_pool_name="workpool",
    image="my_image:latest",
    push=True,
)

The key points: 1. The work pool type MUST be

cloud-run:push

2. This type supports custom Docker images and push-based deployments 3. Make sure your GCP credentials are properly configured 4. The image should be a full path to your container registry (e.g., `gcr.io/your-project/my_image:latest`) You can verify your work pool type with:

bash
prefect work-pool inspect workpool

Would you like me to help you verify your work pool configuration or explain any part of this in more detail?

Ah, I see the issue! I apologize for the confusion. According to your Prefect version (3.4.10), the correct type is simply

cloud-run

or

cloud-run-v2

. Let's create the work pool with the correct type:

bash
prefect work-pool create "workpool1" --type cloud-run-v2

Now your deployment code should look like this:

python
my_flow.from_source(
    source=".",  # Uses the current directory as the source
    entrypoint="path/to/your_flow.py:my_flow"
).deploy(
    name="my_flow",
    description="My flow",
    tags=[],
    schedule=None,
    parameters={
        "credentials_block_name": "gcp-credentials",
    },
    work_pool_name="workpool1",
    image="my_image:latest",
    push=True,
)

The

:push

suffix was incorrect in my previous responses. The work pool types have been simplified in Prefect 3.x, and the push functionality is built into the work pool types themselves. Some important notes: 1.

cloud-run-v2

is the newer version and recommended for new deployments 2. Make sure your image path includes the full registry path (e.g., `gcr.io/your-project/my_image:latest`) 3. Ensure you have the necessary GCP credentials configured Would you like me to help you with anything else regarding the work pool setup or deployment?

Yes! You're absolutely correct. For Cloud Run v2 work pools, you do need to run a worker, and your approach is valid. Here's the proper way to set it up: 1. Create a Dockerfile for your worker:

dockerfile
FROM prefecthq/prefect:3-latest

# Install required packages
RUN pip install prefect-gcp

# The command to start the worker
CMD ["prefect", "worker", "start", "--pool", "workpool1"]

2. Build and push the worker image to Google Container Registry (GCR) or Artifact Registry:

bash
docker build -t <http://gcr.io/your-project/prefect-worker:latest|gcr.io/your-project/prefect-worker:latest> .
docker push <http://gcr.io/your-project/prefect-worker:latest|gcr.io/your-project/prefect-worker:latest>

3. Deploy the worker to Cloud Run. You can do this through the Google Cloud Console or using the

gcloud

CLI:

bash
gcloud run deploy prefect-worker \
  --image <http://gcr.io/your-project/prefect-worker:latest|gcr.io/your-project/prefect-worker:latest> \
  --platform managed \
  --region your-region \
  --service-account <mailto:your-service-account@your-project.iam.gserviceaccount.com|your-service-account@your-project.iam.gserviceaccount.com>

Important Configuration Notes: 1. Make sure to set these environment variables in your Cloud Run service: -

PREFECT_API_KEY

(if using Prefect Cloud) -

PREFECT_API_URL

(if using Prefect Cloud) - Any other necessary credentials or configuration 2. The service account needs these permissions: -

roles/run.developer

-

roles/iam.serviceAccountUser

3. Configure the Cloud Run service with: - Appropriate memory limits - CPU allocation - Concurrency settings - Authentication (usually "Require authentication") This worker will: - Run continuously as a Cloud Run service - Poll your work pool for new work - Execute flows when they're scheduled - Scale automatically based on load (if configured) Your flow deployments will then be executed by this worker when you run them through the Prefect UI or CLI. Would you like me to provide more details about any of these steps or help with specific configuration options for the Cloud Run service?

You need to grant the following IAM roles to your service account. Here's how to fix it: 1. First, set some environment variables to make the commands easier:

bash
export PROJECT_ID=mlops-461404  # Your project ID
export SERVICE_ACCOUNT=prefect-worker-pool@mlops-461404.iam.gserviceaccount.com  # Your service account

2. Grant the necessary roles using gcloud:

bash
# Grant Cloud Run Developer role
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SERVICE_ACCOUNT" \
    --role="roles/run.developer"

# Grant Service Account User role
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SERVICE_ACCOUNT" \
    --role="roles/iam.serviceAccountUser"

# Grant Cloud Run Invoker role
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SERVICE_ACCOUNT" \
    --role="roles/run.invoker"

# Grant Storage Object Viewer (for pulling container images)
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SERVICE_ACCOUNT" \
    --role="roles/storage.objectViewer"

3. If you're using Artifact Registry for your container images, also add:

bash
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SERVICE_ACCOUNT" \
    --role="roles/artifactregistry.reader"

4. Your Cloud Run deployment command looks good, but let's modify it slightly:

bash
gcloud run deploy prefect-worker-pool \
    --image=prefecthq/prefect:3-latest \
    --set-env-vars PREFECT_API_URL=xxx \
    --service-account=$SERVICE_ACCOUNT \
    --no-cpu-throttling \
    --min-instances 1 \
    --max-instances 1 \
    --startup-probe httpGet.port=8080,httpGet.path=/health,initialDelaySeconds=100,periodSeconds=20,timeoutSeconds=20 \
    --args "prefect","worker","start","--install-policy","always","--with-healthcheck","--pool","cloudrun-worker-pool" \
    --region us-central1

5. Additional checks: - Verify the service account exists:

bash
gcloud iam service-accounts describe $SERVICE_ACCOUNT

- Check current IAM bindings:

bash
gcloud projects get-iam-policy $PROJECT_ID \
    --flatten="bindings[].members" \
    --format='table(bindings.role)' \
    --filter="bindings.members:$SERVICE_ACCOUNT"

If you're still getting permission errors after applying these roles, please: 1. Wait a few minutes for the IAM changes to propagate 2. Check if there are any organization-level policies that might be restricting service account permissions 3. Verify that the service account has permission to access the specific region you're deploying to Would you like me to help you verify any of these settings or explain anything in more detail?

Let's fix this step by step: 1. First, let's check the logs for the failed job:

bash
gcloud run jobs executions describe vegan-trogon-d330cc0fce394f43a907aaaea8537951 --region us-central1

2. For more detailed logs:

bash
gcloud logging read "resource.type=cloud_run_job AND resource.labels.job_name=vegan-trogon-d330cc0fce394f43a907aaaea8537951" --limit 50

3. Let's modify your deployment configuration to ensure proper resources and settings. Here's a more robust configuration:

bash
gcloud run jobs create prefect-worker-pool \
    --image=prefecthq/prefect:3-latest \
    --set-env-vars="PREFECT_API_URL=xxx" \
    --set-env-vars="PREFECT_API_KEY=xxx" \
    --service-account=$SERVICE_ACCOUNT \
    --memory=2Gi \
    --cpu=1 \
    --max-retries=0 \
    --task-timeout=3600s \
    --region=us-central1 \
    --command="prefect" \
    --args="worker,start,--pool,cloudrun-worker-pool,--type,cloud-run-v2"

4. For your flow deployment, ensure your Python script includes:

python
from prefect import flow

@flow
def your_flow():
    # your flow code here
    pass

if __name__ == "__main__":
    your_flow.from_source(
        source=".",  # or your git repo URL
        entrypoint="path/to/flow.py:your_flow"
    ).deploy(
        name="your-flow-deployment",
        work_pool_name="cloudrun-worker-pool",
        image="<http://gcr.io/your-project/your-image:latest|gcr.io/your-project/your-image:latest>",
        push=True,
        build=True,
        job_variables={
            "memory": "2Gi",
            "cpu": 1,
            "timeout": "3600s"
        }
    )

5. Make sure your Dockerfile for the flow is properly configured:

dockerfile
FROM prefecthq/prefect:3-latest

# Install your requirements
COPY requirements.txt .
RUN pip install -r requirements.txt

# Copy your flow code
COPY . .

# No need to specify CMD - it will be provided by Prefect

Common issues to check: 1. Environment Variables: Ensure all required environment variables are set -

PREFECT_API_URL

-

PREFECT_API_KEY

(if using Prefect Cloud) - Any other environment variables your flow needs 2. Resource Allocation: Make sure you have sufficient resources - Memory: At least 512MB, recommend 2GB for most flows - CPU: At least 1 CPU - Timeout: Sufficient for your flow to complete 3. Network Access: Ensure your service has proper network access - Check if your flow needs access to external services - Configure VPC connector if needed 4. Image Access: Verify the service account can access your container images - Check if you need

roles/artifactregistry.reader

or

roles/storage.objectViewer

To debug this specific error: 1. Try running the flow locally first to verify it works 2. Check if all dependencies are properly included in your requirements.txt 3. Verify the image builds and runs locally:

bash
docker build -t your-image .
docker run -e PREFECT_API_URL=xxx your-image prefect flow run your-flow-name

Would you like me to help you check any of these specific areas or would you like to share more details about your flow and deployment configuration?