Here's how to effectively use Prefect with Docker containers: 1. Basic Approach The recommended way to run Docker containers with Prefect is to use the

DockerContainer

infrastructure block. This allows you to: - Run pre-built Docker images - Pass environment variables and commands - Configure resource requirements - Handle container lifecycle 2. Configuration Setup Create a

prefect.yaml

file to define your deployment:

yaml
deployments:
- name: docker-flow
  version: 1
  tags: []
  description: "Runs a pre-built Docker container"
  schedule: {}
  flow_name: my_docker_flow
  entrypoint: flow.py:my_flow
  infrastructure:
    type: docker-container
    env: {}
    image: your-image:tag
    image_pull_policy: ALWAYS
    command: []

3. Deployment Creation You can create the deployment using either: - The CLI:

prefect deploy

- Python code:

python
from prefect import flow
from prefect.infrastructure import DockerContainer

docker_container = DockerContainer(
    image="your-image:tag",
    image_pull_policy="ALWAYS"
)

@flow
def my_flow():
    # Your flow logic here
    pass

flow.from_source(
    source="local/",
    entrypoint="flow.py:my_flow"
).deploy(
    name="docker-flow",
    infrastructure=docker_container
)

4. Best Practices - Use specific image tags rather than

latest

to ensure reproducibility - Configure appropriate resource limits in your Docker infrastructure - Consider using Prefect's built-in retry mechanisms for resilient container execution - Use environment variables for sensitive configuration - Implement proper logging within your containers to leverage Prefect's logging infrastructure 5. Advanced Features - You can use private container registries by configuring appropriate credentials - Scale horizontally by running multiple containers across different workers - Implement health checks and container lifecycle management - Chain multiple container executions in a single flow For more detailed information and examples, check out: - Docker Infrastructure Guide - Creating Deployments - Scalable Microservices with Prefect and Docker Would you like me to elaborate on any particular aspect of Docker orchestration with Prefect?

You're right to question this - I need to be more precise. Let me clarify the two distinct approaches: 1. Running a Flow Inside a Docker Container (what my previous example showed):

python
from prefect import flow
from prefect.infrastructure import DockerContainer

docker_container = DockerContainer(
    image="your-image:tag"
)

@flow
def my_flow():
    # This flow code runs INSIDE the container
    pass

This approach packages and runs your Prefect flow code inside the specified container. The container needs Python and Prefect installed. 2. Running a Pre-built Docker Container AS PART of a Flow (probably what you want):

python
from prefect import flow, task
import subprocess

@task
def run_docker_container(image_name: str, command: str = None):
    cmd = ["docker", "run", image_name]
    if command:
        cmd.extend(command.split())
    
    result = subprocess.run(cmd, capture_output=True, text=True)
    if result.returncode != 0:
        raise Exception(f"Container failed: {result.stderr}")
    return result.stdout

@flow
def orchestrate_container():
    # This flow runs your pre-built container
    result = run_docker_container("your-image:tag", "your command here")
    return result

Or using the Docker Python SDK for more control:

python
from prefect import flow, task
import docker

@task
def run_container(image_name: str, command: str = None):
    client = docker.from_client()
    container = client.containers.run(
        image_name,
        command=command,
        detach=True
    )
    
    # Wait for container to complete
    result = container.wait()
    logs = container.logs().decode()
    
    if result['StatusCode'] != 0:
        raise Exception(f"Container failed: {logs}")
    return logs

@flow
def orchestrate_container():
    result = run_container("your-image:tag", "your command here")
    return result

The key differences are: - In approach #1, your Prefect flow code runs INSIDE the container - In approach #2, your Prefect flow ORCHESTRATES the container from outside For orchestrating pre-built Docker images, approach #2 is what you want. This allows you to: - Run any pre-built Docker image without modifying it - Chain multiple container executions - Handle container outputs and errors within your flow - Add Prefect's observability and retry logic around container execution - Pass parameters and environment variables to your containers - Manage container lifecycle (start, stop, cleanup) Would you like me to provide more details about any specific aspect of container orchestration with Prefect?

Here's how to use `prefect-docker`: 1. First, install the collection:

bash
pip install prefect-docker

2. Here's a basic example of running a container:

python
from prefect import flow
from prefect_docker.containers import DockerContainer

@flow
def run_container_flow():
    container = DockerContainer(
        image="your-image:tag",
        command="your-command",  # optional
        environment={"ENV_VAR": "value"},  # optional
        auto_remove=True  # automatically remove container when done
    )
    
    # This runs the container and returns the logs
    result = container.run()
    return result

3. You can also use more advanced features:

python
from prefect import flow
from prefect_docker.containers import DockerContainer

@flow
def advanced_container_flow():
    container = DockerContainer(
        image="your-image:tag",
        command="your-command",
        environment={"ENV_VAR": "value"},
        auto_remove=True,
        volumes=["/host/path:/container/path"],
        network_mode="host",
        stream_output=True  # stream logs in real-time
    )
    
    # Run with additional options
    result = container.run(
        pull_image=True,  # force pull latest image
        raise_on_error=True  # raise exception if container exits with non-zero
    )
    return result

Key benefits of using `prefect-docker`: - Proper integration with Prefect's task runners and logging - Built-in retry logic and error handling - Stream container logs directly to Prefect's logging system - Easy configuration of volumes, networks, and environment variables - Automatic container cleanup - Support for private registries through Docker credentials The

prefect-docker

collection also provides additional utilities like: -

docker_pull

- Pull images explicitly -

docker_build

- Build images from Dockerfiles -

docker_push

- Push images to registries For example, if you need to pull an image first:

python
from prefect import flow
from prefect_docker.containers import DockerContainer
from prefect_docker.images import docker_pull

@flow
def pull_and_run_flow():
    # Pull the image first
    docker_pull("your-image:tag")
    
    # Then run the container
    container = DockerContainer(
        image="your-image:tag",
        auto_remove=True
    )
    return container.run()

This approach using

prefect-docker

is much more robust than using raw Docker commands or the Docker SDK directly, as it's specifically designed to work with Prefect's execution model and provides better observability and control. Would you like to see examples of any specific Docker container scenarios with

prefect-docker

?

Here are several practical examples showcasing different use cases: 1. Basic Data Processing Container

python
from prefect import flow
from prefect_docker.containers import DockerContainer

@flow
def process_data():
    # Run a pandas data processing container
    container = DockerContainer(
        image="pandas-processor:latest",
        command="python process.py --input /data/input.csv --output /data/output.csv",
        volumes=["/local/data:/data"],  # Mount local directory
        environment={
            "POSTGRES_HOST": "localhost",
            "POSTGRES_PASSWORD": "secret"
        }
    )
    
    return container.run()

2. ML Training with GPU

python
from prefect import flow
from prefect_docker.containers import DockerContainer

@flow
def train_model(dataset_path: str, epochs: int):
    container = DockerContainer(
        image="tensorflow/tensorflow:latest-gpu",
        command=f"python train.py --epochs {epochs}",
        volumes=[f"{dataset_path}:/data"],
        device_requests=[{"Driver": "nvidia", "Count": 1}],  # Request GPU
        environment={"NVIDIA_VISIBLE_DEVICES": "all"},
        stream_output=True  # See training progress in real-time
    )
    
    return container.run()

3. Chaining Multiple Containers

python
from prefect import flow
from prefect_docker.containers import DockerContainer
import json

@flow
def etl_pipeline():
    # Extract data
    extract_container = DockerContainer(
        image="data-extractor:latest",
        volumes=["/tmp/data:/data"],
        auto_remove=True
    )
    extract_result = extract_container.run()
    
    # Transform data
    transform_container = DockerContainer(
        image="data-transformer:latest",
        volumes=["/tmp/data:/data"],  # Same volume as extractor
        environment={"PROCESSING_MODE": "batch"},
        auto_remove=True
    )
    transform_result = transform_container.run()
    
    # Load data
    load_container = DockerContainer(
        image="data-loader:latest",
        volumes=["/tmp/data:/data"],
        environment={
            "DB_HOST": "<http://warehouse.example.com|warehouse.example.com>",
            "DB_PASSWORD": "secret"
        },
        auto_remove=True
    )
    load_result = load_container.run()
    
    return {
        "extract": extract_result,
        "transform": transform_result,
        "load": load_result
    }

4. Using Private Registry with Authentication

python
from prefect import flow
from prefect_docker.containers import DockerContainer
from prefect_docker.images import docker_pull

@flow
def run_private_container():
    # Pull from private registry
    docker_pull(
        "<http://private.registry.com/myapp:latest|private.registry.com/myapp:latest>",
        auth_config={
            "username": "user",
            "password": "secret"
        }
    )
    
    container = DockerContainer(
        image="<http://private.registry.com/myapp:latest|private.registry.com/myapp:latest>",
        auto_remove=True
    )
    
    return container.run()

5. Container with Error Handling and Retries ```python from prefect import flow, task from prefect_docker.containers import DockerContainer from prefect.tasks import task_input_hash from datetime import timedelta @task( retries=3, retry_delay_seconds=30, cache_key_fn=task_input_hash, cache_expiration=timedelta(hours=1) ) def run_container_with_retries(image: str, command: str): container = DockerContainer( image=image, command=command, auto_remove=True, stream_output=True ) try: result = container.run(raise_on_error=True) return result except Exception as e: print(f"Container failed: {e}") raise @flow def resilient_container_flow(): try: