bash
# Create a Docker work pool
prefect work-pool create "docker-pool" --type docker

# Start a worker
prefect worker start -p "docker-pool"

# Run your deployment
prefect deployment run my-flow/docker-deployment

Some important notes: 1. Make sure Docker is installed and running on your system. 2. The Docker infrastructure will automatically handle: - Building the Docker image - Running your task in a container - Managing container lifecycle - Passing results back to your flow 3. If you need to customize the Docker environment, you can: - Provide a custom Dockerfile - Specify additional build arguments - Set environment variables - Mount volumes - Configure network settings 4. For production use, consider: - Using specific image tags instead of 'latest' - Properly managing secrets and environment variables - Setting resource limits for your containers - Implementing proper error handling The Docker infrastructure in Prefect 3.x provides a robust way to isolate and scale your tasks while maintaining consistency across different environments. You can find more detailed information in the Prefect documentation about Docker infrastructure. Would you like me to elaborate on any particular aspect of Docker-based tasks in Prefect, such as custom Dockerfile configuration or advanced Docker settings?

Let me show you how to create a task that runs Docker containers. Here's a comprehensive example with different approaches: 1. Basic Docker Container Task:

python
from prefect import task
import subprocess
from typing import List, Optional, Dict

@task
def run_docker_container(
    image: str,
    command: Optional[List[str]] = None,
    environment: Optional[Dict[str, str]] = None,
    volumes: Optional[List[str]] = None,
    network: Optional[str] = None,
    remove: bool = True
) -> subprocess.CompletedProcess:
    """
    Run a Docker container as a Prefect task.
    
    Args:
        image: Docker image name
        command: Command to run in the container
        environment: Environment variables to pass to the container
        volumes: Volume mappings
        network: Docker network to use
        remove: Whether to remove the container after execution
    
    Returns:
        CompletedProcess instance with return code and output
    """
    # Build the docker command
    docker_cmd = ["docker", "run"]
    
    # Add optional parameters
    if remove:
        docker_cmd.append("--rm")
    
    if environment:
        for key, value in environment.items():
            docker_cmd.extend(["-e", f"{key}={value}"])
    
    if volumes:
        for volume in volumes:
            docker_cmd.extend(["-v", volume])
            
    if network:
        docker_cmd.extend(["--network", network])
    
    # Add the image name
    docker_cmd.append(image)
    
    # Add the command if specified
    if command:
        docker_cmd.extend(command)
    
    try:
        # Run the container
        result = subprocess.run(
            docker_cmd,
            check=True,
            text=True,
            capture_output=True
        )
        return result
    except subprocess.CalledProcessError as e:
        raise RuntimeError(f"Docker container failed: {e.stderr}")

2. Async Version (if you need non-blocking container execution):

python
from prefect import task
import asyncio
from typing import List, Optional, Dict

@task
async def run_docker_container_async(
    image: str,
    command: Optional[List[str]] = None,
    environment: Optional[Dict[str, str]] = None,
    volumes: Optional[List[str]] = None,
    stream_logs: bool = True
) -> str:
    """
    Run a Docker container asynchronously as a Prefect task.
    
    Args:
        image: Docker image name
        command: Command to run in the container
        environment: Environment variables to pass to the container
        volumes: Volume mappings
        stream_logs: Whether to stream container logs
    
    Returns:
        Container output
    """
    docker_cmd = ["docker", "run", "--rm"]
    
    if environment:
        for key, value in environment.items():
            docker_cmd.extend(["-e", f"{key}={value}"])
    
    if volumes:
        for volume in volumes:
            docker_cmd.extend(["-v", volume])
    
    docker_cmd.append(image)
    if command:
        docker_cmd.extend(command)
    
    process = await asyncio.create_subprocess_exec(
        *docker_cmd,
        stdout=asyncio.subprocess.PIPE,
        stderr=asyncio.subprocess.PIPE
    )
    
    stdout, stderr = await process.communicate()
    
    if process.returncode != 0:
        raise RuntimeError(f"Docker container failed: {stderr.decode()}")
    
    return stdout.decode()

3. Example usage in a flow: ```python from prefect import flow @flow def docker_processing_flow(): # Run a simple container result1 = run_docker_container( image="alpine:latest", command=["echo", "Hello from Docker!"] ) print(f"Simple container output: {result1.stdout}") # Run a container with more options result2 = run_docker_container( image="postgres:13", environment={ "POSTGRES_PASSWORD": "secretpassword", "POSTGRES_DB": "testdb" }, volumes=["/local/path:/container/path"], network="my-network" )

print(f"Database container output: {result2.stdout}") # For async version @flow async def async_docker_flow(): result = await run_docker_container_async( image="python:3.9-slim", command=["python", "-c", "print('Hello from Python container!')"] ) print(f"Async container output: {result}")

Key features of these implementations:

1. *Error Handling*: Both versions include proper error handling and will raise meaningful exceptions if the container fails.

2. *Flexibility*: The tasks accept various Docker options like:
   - Environment variables
   - Volume mappings
   - Network configuration
   - Custom commands

3. *Output Capture*: Both implementations capture and return the container's output.

4. *Container Cleanup*: The `--rm` flag ensures containers are removed after execution.

Some important considerations:

1. *Security*:
   ```python
   # Be careful with user input - validate and sanitize any parameters
   @task
   def secure_docker_run(image: str):
       # Validate image name format
       if not re.match(r'^[a-zA-Z0-9_.-]+(?::[a-zA-Z0-9_.-]+)?$', image):
           raise ValueError("Invalid image name")
       # ... rest of the implementation

2. Resource Management:

python
   @task
   def resource_controlled_docker_run(
       image: str,
       memory_limit: str = "512m",
       cpu_limit: str = "0.5"
   ):
       docker_cmd = [
           "docker", "run",
           "--memory", memory_limit,
           "--cpus", cpu_limit,
           image
       ]
       # ... rest of the implementation

3. Logging:

python
   from prefect import get_run_logger

   @task
   def logged_docker_run(image: str):
       logger = get_run_logger()
       <http://logger.info|logger.info>(f"Starting container with image: {image}")
       # ... implementation
       <http://logger.info|logger.info>("Container completed successfully")

To use these tasks, make sure: 1. Docker is installed and running on the machine where the task will execute 2. The user running the Prefect flow has permissions to execute Docker commands 3. Any required images are either available locally or can be pulled from a registry 4. Network, volume, and environment configurations are properly set up Would you like me to elaborate on any particular aspect, such as handling specific Docker options or implementing additional features?