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<@ULVA73B9P> I want to create a prefect workflow t...
# ask-marvin
d
@Marvin I want to create a prefect workflow that pulls nginx:latest image and then as the next step it runs the container nginx:latest on the agent. Can you create for me the deploy file?
m
Here is a basic example of how you can create a Prefect workflow that pulls the 
nginx:latest
image and then runs it as a container. Unfortunately, putting 
create_flow_to_pull_docker_image
and 
run_container_in_flow
keywords didn't directly lead us to the exact documents for container setup, but combining information from multiple documents, you can achieve your goal. Below is an example deployment script for Prefect, including pulling and running the 
nginx:latest
image:
Copy code
yaml
flows:
  - name: pull-and-run-nginx
    storage:
      type: docker
      build: false     # Indicating that we are not building a new Docker image.
      push: false      # Indicating that we are not pushing the image to a repository.
      image_registry:  
        base_image: nginx:latest
    entrypoint: flow.py
    infrastructure:
      type: docker-container
      image: nginx:latest
      command:
        - "nginx"
        - "-g"
        - "daemon off;"

work_queues:
  - name: default
You would also need a 
flow.py
file to define the Prefect flow:
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python
from prefect import flow

@flow
def pull_and_run_nginx():
    # In a real-world scenario you would perform more logic here
    print("Pulling and running nginx:latest container")

if __name__ == "__main__":
    pull_and_run_nginx()
To deploy the flow, you would use Prefect's deployment commands:
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sh
prefect deployment build -n pull-and-run-nginx -q default -f pull_and_run_nginx.py
prefect deployment apply pull-and-run-nginx-deployment.yaml
This is a basic definition. In a real workflow, you might consider pulling the image dynamically inside the flow using Docker Python SDK or Prefect task as shown more explicitly by the guides. ### Useful Links - Prefect Docker Worker Documentation - Example Prefect Docker Guide Feel free to ask if you have more specific requirements or need further assistance.
d
@Marvin prefect deployment build -n pull-and-run-nginx -q default -f pull_and_run_nginx.py Usage: prefect deployment build [OPTIONS] ENTRYPOINT Try 'prefect deployment build --help' for help. ╭─ Error ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ No such option: -f
m
Ah, of course. Prefect 2.x made a few changes, and it seems the 
-f
option is not correct here. Here's a refined approach for creating the deployment using the 
prefect.yaml
file. First, create a 
prefect.yaml
configuration file:
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yaml
version: 2.0

build: {}

pull: {}

deployments:
  - name: pull-and-run-nginx
    version: 1.0
    entrypoint: flow.py:pull_and_run_nginx
    work_queue_name: default
    description: A flow to pull and run nginx:latest Docker container
    storage: {}
    infrastructure:
      type: docker-container
      image: nginx:latest
      command:
        - "nginx"
        - "-g"
        - "daemon off;"
Then, let's update the 
flow.py
as follows:
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python
from prefect import flow

@flow
def pull_and_run_nginx():
    print("Pulling and running nginx:latest container")

if __name__ == "__main__":
    pull_and_run_nginx()
Finally, deploy your flow using the 
prefect deploy
command:
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sh
prefect deploy --prefect-file path/to/prefect.yaml
Make sure your project directory is set up correctly and that you're running these commands from within your project's root. ### Sources - Prefect Deploy Guide - Prefect Docker Deployment Guide This should get you started on deploying your Docker container with Prefect. If you run into any issues, let me know!
d
@Marvin 220311.122 | ERROR | Flow run 'mature-iguana' - Flow could not be retrieved from deployment. Traceback (most recent call last): File "/usr/local/lib/python3.10/site-packages/prefect/deployments/steps/core.py", line 154, in run_steps step_output = await run_step(step, upstream_outputs) File "/usr/local/lib/python3.10/site-packages/prefect/deployments/steps/core.py", line 125, in run_step result = await from_async.call_soon_in_new_thread( File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 327, in aresult return await asyncio.wrap_future(self.future) File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 352, in _run_sync result = self.fn(*self.args, **self.kwargs) File "/usr/local/lib/python3.10/site-packages/prefect/deployments/steps/pull.py", line 28, in set_working_directory os.chdir(directory) FileNotFoundError: [Errno 2] No such file or directory: '/home/proj/dijar-kickoff/prefect/marvin' how to fix this part?
m
Your message was too long by 367 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
220306.820 | INFO | prefect.flow_runs.worker - Worker 'DockerWorker c3703705-a21f-4a5f-b9ec-e6f4134f3b54' submitting flow run '2d37af56-532e-4a91-a1d1-527b083efbac'
220306.909 | INFO | prefect.worker.docker.dockerworker c3703705-a21f-4a5f-b9ec-e6f4134f3b54 - Creating Docker container 'mature-iguana'... 220306.966 | INFO | prefect.worker.docker.dockerworker c3703705-a21f-4a5f-b9ec-e6f4134f3b54 - Docker container 'mature-iguana' has status 'created' 220307.131 | INFO | prefect.worker.docker.dockerworker c3703705-a21f-4a5f-b9ec-e6f4134f3b54 - Docker container 'mature-iguana' has status 'running' 220307.147 | INFO | prefect.flow_runs.worker - Completed submission of flow run '2d37af56-532e-4a91-a1d1-527b083efbac' 220309.347 | INFO | prefect.flow_runs.runner
Your message was too long by 6 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
220311.122 | ERROR | Flow run 'mature-iguana' - Flow could not be retrieved from deployment.
Traceback (most recent call last): File "/usr/local/lib/python3.10/site-packages/prefect/deployments/steps/core.py", line 154, in run_steps step_output = await run_step(step, upstream_outputs) File "/usr/local/lib/python3.10/site-packages/prefect/deployments/steps/core.py", line 125, in run_step result = await from_async.call_soon_in_new_thread( File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 327, in aresult return await asyncio.wrap_future(self.future) File "/usr/local/lib/python3.10/site-packages/prefect/_internal/concurrency/calls.py", line 352, in _run_sync result = self.fn(*self.args, **self.kwargs) File "/usr/local/lib/python3.10/site-packages/prefect/deployments/steps/pull.py", line 28, in set_working_directory os.chdir(directory) FileNotFoundError: [Errno 2] No such file or directory: '/home/proj/dijar-kickoff/prefect/marvin' The above exception was the direct cause of the following exception:
It looks like the issue you're encountering is related to the working directory or path setup in your deployment, and it may involve an implicit assumption about the file structure that doesn't match your actual environment. Here are a few steps and adjustments you can make to resolve this: ### Step-by-Step Resolution 1. Check File Paths and Directories: Ensure that the paths and directories mentioned in your deployment configuration are correct and accessible. The 
FileNotFoundError
typically means that the specified directory does not exist, so double-check the paths used in your 
prefect.yaml
file and any other relevant configuration. 2. Directory Setup in `prefect.yaml`: Make sure the 
entrypoint
and other path configurations in your 
prefect.yaml
are correctly set. You may need to specify the relative or full path as required.
Copy code
yaml
    version: 2.0

    build: {}

    pull: {}

    deployments:
      - name: pull-and-run-nginx
        version: 1.0
        entrypoint: "./flow.py:pull_and_run_nginx"
        work_queue_name: default
        description: A flow to pull and run nginx:latest Docker container
        storage: {}
        infrastructure:
          type: docker-container
          image: nginx:latest
          command:
            - "nginx"
            - "-g"
            - "daemon off;"
3. Resolve Local Directory Issues: If you are using local file systems, ensure that the directory exists on both your local machine (where you create the deployment) and in the execution environment (e.g., Docker, Kubernetes). Sometimes you might want to create the directories manually before running the flow. 4. Inspect Storage Block Configuration: If you are using Prefect Storage Blocks, ensure they are correctly set up and referenced in your deployment file. ### Example Workflow Here is a step-by-step guide to set up and troubleshoot the deployment: 1. Ensure Directory Structure: Make sure the directory containing 
flow.py
exists and is correctly referenced in the project configuration.
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sh
    .
    ├── flow.py
    └── prefect.yaml
2. Correct Configuration in `flow.py`:
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python
    from prefect import flow

    @flow
    def pull_and_run_nginx():
        print("Pulling and running nginx:latest container")

    if __name__ == "__main__":
        pull_and_run_nginx()
3. Deploy Correctly: Ensure your deployment command uses the correct configuration:
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sh
    prefect deploy --prefect-file ./prefect.yaml
### Useful Links - Prefect Deploy Guide - Prefect Docker Deployment Guide - GitHub Issue: Flow could not be retrieved from deployment - FileNotFoundError This should help resolve the 
FileNotFoundError
and ensure your deployment runs successfully. If you continue to experience issues, please share more details about the structure and configuration so that we can further assist you.
d
@Marvin how can I pass the flow.py file into the docker agent container that has its local working directory in /opt/prefect? should I just mount it as a docker volume on that path?
m
Yes, mounting the local 
flow.py
file as a volume in the Docker container is an effective way to ensure that the file is accessible within the container's working directory. This approach allows the Docker container to use the local file system's flow code directly. ### Step-by-Step Guide 1. Update Docker Run Command to Mount Volume: Use the 
-v
(volume) option to mount your local directory (where 
flow.py
resides) to a directory inside the container (e.g., 
/opt/prefect
).
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sh
    docker run -d --name prefect -v /path/to/local/dir:/opt/prefect -e PREFECT_API_URL=<http://host.docker.internal:4200/api> --workdir /opt/prefect your-prefect-image
Replace 
/path/to/local/dir
with the actual path to your local directory containing 
flow.py
. 2. Ensure Consistency in Paths: Make sure that the paths in your 
prefect.yaml
and 
flow.py
configuration match the mounted directory inside the Docker container. Specifically, if the container expects the working directory to be 
/opt/prefect
, ensure your 
entrypoint
in the 
prefect.yaml
uses this path. 3. Example prefect.yaml:
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yaml
    version: 2.0

    build: {}

    pull: {}

    deployments:
      - name: pull-and-run-nginx
        version: 1.0
        entrypoint: "/opt/prefect/flow.py:pull_and_run_nginx"
        work_queue_name: default
        description: A flow to pull and run nginx:latest Docker container
        storage: {}
        infrastructure:
          type: docker-container
          image: nginx:latest
          command:
            - "nginx"
            - "-g"
            - "daemon off;"
4. Ensure 
flow.py
Path Consistency: Make sure your 
flow.py
script is structured correctly and is located in the mounted directory:
Copy code
python
    from prefect import flow

    @flow
    def pull_and_run_nginx():
        print("Pulling and running nginx:latest container")

    if __name__ == "__main__":
        pull_and_run_nginx()
5. Deploy the Prefect Flow: With everything set up, run the deployment command from within the project directory:
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sh
    prefect deploy --prefect-file ./prefect.yaml
### Example Docker Run Command Here's a complete example 
docker run
command that includes mounting the volume and setting environment variables:
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sh
docker run -d --name prefect -v /path/to/your/local/project:/opt/prefect -e PREFECT_API_URL=<http://host.docker.internal:4200/api> --workdir /opt/prefect prefecthq/prefect:latest
### Links - Docker Volumes Documentation - Prefect Deploy Guide - Prefect Docker Guide Mounting the volume ensures that any changes in your local 
flow.py
file are reflected inside the Docker container, making it easy to manage and test your Prefect flows. If you follow these steps and still encounter issues, please share the exact error message for further troubleshooting.
d
@Marvin this function you provided to pull image and run it: should I use this function 
pull_docker_image
and then use 
create_docker_container
to create the container?
m
Certainly! Here’s how you can use the 
pull_docker_image
and 
create_docker_container
functions from the Prefect Docker collections to create a workflow that pulls the nginx image and runs it as a container. ### Step-by-Step Workflow 1. Flow Definition: First, define a Prefect flow that includes steps to pull the Docker image and create the Docker container. 2. Add Required Imports: Use the appropriate Prefect and Docker integration functions. Here’s an example configuration: ### 
flow.py
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python
from prefect import flow
from prefect_docker.images import pull_docker_image
from prefect_docker.containers import create_docker_container

@flow
async def pull_and_run_nginx():
    # Pull the latest nginx image
    image = await pull_docker_image(repository="nginx", tag="latest")
    print(f"Pulled image: {image}")
    
    # Create and run a container from the pulled image
    container = await create_docker_container(
        image="nginx:latest",
        command=["nginx", "-g", "daemon off;"]
    )
    print(f"Created container: {container}")

if __name__ == "__main__":
    pull_and_run_nginx()
### Deployment Configuration (
prefect.yaml
) Create a 
prefect.yaml
file to define the deployment details:
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yaml
version: 2.0

build: {}

pull: {}

deployments:
  - name: pull-and-run-nginx
    version: 1.0
    entrypoint: "flow.py:pull_and_run_nginx"
    work_queue_name: default
    description: A flow to pull and run nginx:latest Docker container
    storage: {}
    infrastructure:
      type: docker-container
      image: prefecthq/prefect:latest
      command:
        - "python"
        - "/opt/prefect/flow.py"
### Running the Deployment 1. Deploy the Flow: If you haven’t already, ensure your local directory structure matches what is expected in the configuration. In this example, the Python file is 
flow.py
in the root directory. Then, run the following command to deploy:
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sh
    prefect deploy --prefect-file ./prefect.yaml
2. Run the Docker Container: Ensure you correctly mount the project directory containing 
flow.py
to the appropriate path inside the container:
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sh
    docker run -d --name prefect -v /path/to/your/local/project:/opt/prefect -e PREFECT_API_URL=<http://host.docker.internal:4200/api> --workdir /opt/prefect prefecthq/prefect:latest
### Summary - Define the flow in 
flow.py
leveraging Prefect’s Docker functionalities. - Configure the deployment in 
prefect.yaml
. - Deploy the flow using Prefect CLI. - Execute the container with proper volume mounting. By using Prefect's integrated tasks for Docker operations, you can streamline the workflow configuration and execution. If you encounter any specific issues, feel free to ask for more detailed troubleshooting!
d
@Marvin three questions: in your prefect.yaml file as image under infrastructure for docker-container you are using prefect-latest image? Does it have to be a prefect image or can I use whatever image I want? I am getting this error: /opt/prefect# python flow.py /opt/prefect/flow.py19 RuntimeWarning: coroutine 'from_async.wait_for_call_in_loop_thread' was never awaited pull_and_run_nginx() RuntimeWarning: Enable tracemalloc to get the object allocation traceback
m
### Question 1: Can You Use Any Image? You can use any image for the 
infrastructure
section as long as the image contains all the dependencies needed to run your Prefect flow. For instance, if your flow requires certain Python libraries or Prefect itself, those need to be included in the custom image. ### Question 2: Using Prefect Image or Custom Image? Using the 
prefecthq/prefect:latest
is often convenient since it comes pre-configured with Prefect and common dependencies. However, you can definitely use any custom Docker image, just ensure it has Prefect and any other required libraries installed. ### Custom Image Example Here’s a refined 
prefect.yaml
if you choose to use a custom image:
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yaml
version: 2.0

build: {}

pull: {}

deployments:
  - name: pull-and-run-nginx
    version: 1.0
    entrypoint: "flow.py:pull_and_run_nginx"
    work_queue_name: default
    description: A flow to pull and run nginx:latest Docker container
    storage: {}
    infrastructure:
      type: docker-container
      image: custom-image:latest  # Use your custom image
      command:
        - "python"
        - "/opt/prefect/flow.py"
Just make sure 
custom-image:latest
has Prefect installed:
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dockerfile
FROM python:3.10-slim
WORKDIR /opt/prefect

# Install Prefect
RUN pip install prefect

# Copy your flow code into the image
COPY flow.py .

CMD ["python", "/opt/prefect/flow.py"]
### Question 3: RuntimeWarning for Untouched Coroutine The RuntimeWarning you are seeing is due to attempting to run an async function incorrectly. When using async functions outside an async context, you need to use an event loop. Here’s how you can adjust 
flow.py
for proper async handling:
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python
import asyncio
from prefect import flow
from prefect_docker.images import pull_docker_image
from prefect_docker.containers import create_docker_container

@flow
async def pull_and_run_nginx():
    # Pull the latest nginx image
    image = await pull_docker_image(repository="nginx", tag="latest")
    print(f"Pulled image: {image}")
    
    # Create and run a container from the pulled image
    container = await create_docker_container(
        image="nginx:latest",
        command=["nginx", "-g", "daemon off;"]
    )
    print(f"Created container: {container}")

if __name__ == "__main__":
    asyncio.run(pull_and_run_nginx())
### Execution Summary 1. Ensure Docker Image: Either use a Prefect image or create your custom image with all dependencies. 2. Modify `flow.py`: Correctly handle async flow functions. 3. Run Docker Container: Make sure to properly mount the local directory. ### Useful Links - Prefect Docker Integration - Dockerfile Reference This ensures that the async routines are handled correctly, and the appropriate libraries are available in your Docker runtime environment. Let me know if you need further assistance!
d
@Marvin So the workflow completes now but I don't see the container running: 224128.046 | INFO | Flow run 'super-seagull' - Executing 'pull_docker_image-0' immediately... 224128.121 | INFO | Task run 'pull_docker_image-0' - Pulling image: nginx:latest. 224128.393 | INFO | Task run 'pull_docker_image-0' - Finished in state Completed() Pulled image: Image: 'nginx:latest' 224128.425 | INFO | Flow run 'super-seagull' - Created task run 'create_docker_container-0' for task 'create_docker_container' 224128.426 | INFO | Flow run 'super-seagull' - Executing 'create_docker_container-0' immediately... 224128.480 | INFO | Task run 'create_docker_container-0' - Creating container with 'nginx:latest' image. 224128.560 | INFO | Task run 'create_docker_container-0' - Finished in state Completed() Created container: Container: cbe5bff07cea 224128.593 | INFO | Flow run 'super-seagull' - Finished in state Completed('All states completed.')
m
Your message was too long by 147 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
So the workflow completes now but I don't see the container running:
/opt/prefect# python flow.py 224127.956 | INFO | prefect.engine - Created flow run 'super-seagull' for flow 'pull-and-run-nginx' 224127.957 | INFO | Flow run 'super-seagull' - View at http://localhost:4200/flow-runs/flow-run/686fdf95-19e4-41c6-8dea-12f5b3851854 224128.045 | INFO | Flow run 'super-seagull' - Created task run 'pull_docker_image-0' for task 'pull_docker_image' 224128.046 | INFO | Flow run 'super-seagull' - Executing 'pull_docker_image-0' immediately... 224128.121 | INFO | Task run 'pull_docker_image-0' - Pulling image: nginx:latest. 224128.393 | INFO | Task run 'pull_docker_image-0' - Finished in state Completed() Pulled image: <Image: 'nginx:latest'> 224128.425 | INFO | Flow run 'super-seagull' - Created task run 'create_docker_container-0' for task 'create_docker_container
Your message was too long by 36 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
So the workflow completes now but I don't see the container running:
224128.045 | INFO | Flow run 'super-seagull' - Created task run 'pull_docker_image-0' for task 'pull_docker_image' 224128.046 | INFO | Flow run 'super-seagull' - Executing 'pull_docker_image-0' immediately... 224128.121 | INFO | Task run 'pull_docker_image-0' - Pulling image: nginx:latest. 224128.393 | INFO | Task run 'pull_docker_image-0' - Finished in state Completed() Pulled image: <Image: 'nginx:latest'> 224128.425 | INFO | Flow run 'super-seagull' - Created task run 'create_docker_container-0' for task 'create_docker_container' 224128.426 | INFO | Flow run 'super-seagull' - Executing 'create_docker_container-0' immediately... 224128.480 | INFO | Task run 'create_docker_container-0' - Creating container with 'nginx:latest' image. 224128.560 | INFO | Task run 'create_docker_container-0' - Finished in state Completed() Created container: <Container: cbe5bff07cea> 22
Your message was too long by 28 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
So the workflow completes now but I don't see the container running:
224128.045 | INFO | Flow run 'super-seagull' - Created task run 'pull_docker_image-0' for task 'pull_docker_image' 224128.046 | INFO | Flow run 'super-seagull' - Executing 'pull_docker_image-0' immediately... 224128.121 | INFO | Task run 'pull_docker_image-0' - Pulling image: nginx:latest. 224128.393 | INFO | Task run 'pull_docker_image-0' - Finished in state Completed() Pulled image: <Image: 'nginx:latest'> 224128.425 | INFO | Flow run 'super-seagull' - Created task run 'create_docker_container-0' for task 'create_docker_container' 224128.426 | INFO | Flow run 'super-seagull' - Executing 'create_docker_container-0' immediately... 224128.480 | INFO | Task run 'create_docker_container-0' - Creating container with 'nginx:latest' image. 224128.560 | INFO | Task run 'create_docker_container-0' - Finished in state Completed() Created container: <Container: cbe5bff07cea> 22
From the logs, it appears that the container is created but not started. The 
create_docker_container
function only creates the container without starting it. To start the container, you need to add an additional step to run the container after it's created. ### Update with Start Container You can use the 
start_docker_container
task from Prefect to start the container after it's created. Here's an updated version of your `flow.py`:
Copy code
python
import asyncio
from prefect import flow
from prefect_docker.images import pull_docker_image
from prefect_docker.containers import create_docker_container, start_docker_container

@flow
async def pull_and_run_nginx():
    # Pull the latest nginx image
    image = await pull_docker_image(repository="nginx", tag="latest")
    print(f"Pulled image: {image}")
    
    # Create a container from the pulled image
    container = await create_docker_container(
        image="nginx:latest",
        command=["nginx", "-g", "daemon off;"]
    )
    print(f"Created container: {container}")

    # Start the container
    started_container = await start_docker_container(container_id=container.id)
    print(f"Started container: {started_container}")

if __name__ == "__main__":
    asyncio.run(pull_and_run_nginx())
### Ensure All Prefect Docker Task Installations Make sure you have all necessary Prefect Docker tasks installed:
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sh
pip install prefect-docker
### Final Steps 1. Update the Flow Code: Ensure the 
flow.py
script includes the 
start_docker_container
task as shown above. 2. Deploy Again: After updating the 
flow.py
, redeploy your flow with the correct configurations.
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sh
    prefect deploy --prefect-file ./prefect.yaml
3. Run the Docker Container: Make sure to run the Docker container with the updated flow script mounted, as explained earlier:
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sh
    docker run -d --name prefect -v /path/to/your/local/project:/opt/prefect -e PREFECT_API_URL=<http://host.docker.internal:4200/api> --workdir /opt/prefect prefecthq/prefect:latest
### Additional Step for Verification To ensure that the container is indeed running, you might want to list running containers or inspect logs:
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sh
docker ps -a   # This will list all containers, including stopped ones.
and for logs:
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sh
docker logs <container_id>
This approach ensures the workflow not only creates but also starts the container. If you have any more issues or questions, feel free to ask!
d
@Marvin this works perfectly, can you kindly share how can I pull also from private docker repositories? How can I provide the credentials safely?
m
To pull images from private Docker repositories safely, you can use Prefect’s Docker Registry Credentials block. This allows you to store and manage your credentials securely. Here's how you can set it up and use it in your workflow. ### Step-by-Step Guide 1. Create Docker Registry Credentials in Prefect First, you'll need to create a Docker Registry Credentials block in Prefect Cloud or Prefect Server.
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python
    from prefect_docker.credentials import DockerRegistryCredentials

    docker_registry_credentials = DockerRegistryCredentials(
        username="your_username",
        password="your_password",  # It's better to use a secure method to store passwords, e.g., Prefect Secret
        registry_url="your_registry_url"
    )

    docker_registry_credentials.save(name="my-docker-registry")
2. Use Docker Registry Credentials in Your Flow You can then use these saved credentials to pull images securely. ### 
flow.py
Update your 
flow.py
to use `DockerRegistryCredentials`:
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python
import asyncio
from prefect import flow
from prefect_docker.images import pull_docker_image
from prefect_docker.containers import create_docker_container, start_docker_container
from prefect_docker.credentials import DockerRegistryCredentials

@flow
async def pull_and_run_nginx():
    # Load Docker credentials
    docker_registry_credentials = await DockerRegistryCredentials.load("my-docker-registry")

    # Pull the latest nginx image from the private repository
    image = await pull_docker_image(
        repository="your_private_repo/nginx",
        tag="latest",
        docker_registry_credentials=docker_registry_credentials
    )
    print(f"Pulled image: {image}")

    # Create and run a container from the pulled image
    container = await create_docker_container(
        image="your_private_repo/nginx:latest",
        command=["nginx", "-g", "daemon off;"]
    )
    print(f"Created container: {container}")

    # Start the container
    started_container = await start_docker_container(container_id=container.id)
    print(f"Started container: {started_container}")

if __name__ == "__main__":
    asyncio.run(pull_and_run_nginx())
### Update Deployment Configuration (
prefect.yaml
) Ensure your deployment YAML remains correct:
Copy code
yaml
version: 2.0

build: {}

pull: {}

deployments:
  - name: pull-and-run-nginx
    version: 1.0
    entrypoint: "flow.py:pull_and_run_nginx"
    work_queue_name: default
    description: A flow to pull and run nginx:latest Docker container
    storage: {}
    infrastructure:
      type: docker-container
      image: custom-image:latest
      command:
        - "python"
        - "/opt/prefect/flow.py"
### Secure Credential Storage It's good practice to store sensitive information securely. Prefect's 
Secret
for secure storage can be used or environment variables:
Copy code
python
import os
from prefect_docker.credentials import DockerRegistryCredentials
from prefect.blocks.system import Secret

# Using Prefect Secret to store the password securely
docker_registry_password = Secret.load("docker_registry_password")

docker_registry_credentials = DockerRegistryCredentials(
    username="your_username",
    password=docker_registry_password.get(),  # Getting the password from Prefect Secret
    registry_url="your_registry_url"
)
docker_registry_credentials.save(name="my-docker-registry")
### Deployment and Execution Deploy the updated flow:
Copy code
sh
prefect deploy --prefect-file ./prefect.yaml
Run the Docker container as previously configured:
Copy code
sh
docker run -d --name prefect -v /path/to/your/local/project:/opt/prefect -e PREFECT_API_URL=<http://host.docker.internal:4200/api> --workdir /opt/prefect custom-image:latest
### Documentation Links - Prefect Docker Image Pull - Prefect Docker Container Management
- Prefect Docker Registry Credentials By following these steps, you can securely pull Docker images from private repositories and run them within your Prefect workflows. If you have further questions or need additional help, feel free to ask!
d
@Marvin I configured the docker registry credentials block but I get this error: docker.errors.ImageNotFound: 404 Client Error for http+docker://localhost/v1.43/images/create?tag=latest&amp;fromImage=your_private_repo%2Fnginx: Not Found ("pull access denied for your_private_repo/nginx, repository does not exist or may require 'docker login': denied: requested access to the resource is denied") 230647.244 | ERROR | Flow run 'swinging-pelican' - Finished in state Failed('Flow run encountered an exception. ImageNotFound: 404 Client Error for http+docker://localhost/v1.43/images/create?tag=latest&amp;fromImage=your_private_repo%2Fnginx: Not Found ("pull access denied for your_private_repo/nginx, repository does not exist or may require \'docker login\': denied: requested access to the resource is denied")') looks like requests is being made to the local docker registry.
m
The error indicates that Docker couldn't pull the image, either because the repository doesn't exist or because it requires authentication. Given that you're using a private Docker repository, it seems likely that authentication is the issue. ### Verify Docker Registry Credentials First, ensure your 
DockerRegistryCredentials
block is correctly configured with the necessary credentials:
Copy code
python
from prefect_docker.credentials import DockerRegistryCredentials
from prefect.blocks.system import Secret

# Using Prefect Secret to store the password securely
docker_registry_password = Secret.load("docker_registry_password")

docker_registry_credentials = DockerRegistryCredentials(
    username="your_username",
    password=docker_registry_password.get(),  # Getting the password from Prefect Secret
    registry_url="your.registry.url"
)
docker_registry_credentials.save(name="my-docker-registry")
### Verify Registry URL The 
registry_url
should match the one you use when running 
docker login
. For Docker Hub, it would be 
<http://index.docker.io|index.docker.io>
. Here’s an example for Docker Hub:
Copy code
python
docker_registry_credentials = DockerRegistryCredentials(
    username="your_username",
    password=docker_registry_password.get(),
    registry_url="<http://index.docker.io|index.docker.io>"  # For Docker Hub
)
docker_registry_credentials.save(name="my-docker-registry")
### Use Registry Credentials in Flow Ensure your 
flow.py
correctly uses the stored credentials:
Copy code
python
import asyncio
from prefect import flow
from prefect_docker.images import pull_docker_image
from prefect_docker.containers import create_docker_container, start_docker_container
from prefect_docker.credentials import DockerRegistryCredentials

@flow
async def pull_and_run_nginx():
    # Load Docker credentials
    docker_registry_credentials = await DockerRegistryCredentials.load("my-docker-registry")

    # Pull the latest nginx image from the private repository
    image = await pull_docker_image(
        repository="your_private_repo/nginx",
        tag="latest",
        docker_registry_credentials=docker_registry_credentials
    )
    print(f"Pulled image: {image}")

    # Create and run a container from the pulled image
    container = await create_docker_container(
        image="your_private_repo/nginx:latest",
        command=["nginx", "-g", "daemon off;"]
    )
    print(f"Created container: {container}")

    # Start the container
    started_container = await start_docker_container(container_id=container.id)
    print(f"Started container: {started_container}")

if __name__ == "__main__":
    asyncio.run(pull_and_run_nginx())
### Deployment Configuration Ensure 
prefect.yaml
is correctly pointing to your script:
Copy code
yaml
version: 2.0

build: {}

pull: {}

deployments:
  - name: pull-and-run-nginx
    version: 1.0
    entrypoint: "flow.py:pull_and_run_nginx"
    work_queue_name: default
    description: A flow to pull and run nginx:latest Docker container
    storage: {}
    infrastructure:
      type: docker-container
      image: custom-image:latest
      command:
        - "python"
        - "/opt/prefect/flow.py"
### Check Docker Login Finally, verify you can manually log in and pull the image using the same credentials. Run the following commands to ensure your credentials are correct:
Copy code
sh
docker login your.registry.url
docker pull your_private_repo/nginx:latest
### Summary - Ensure 
DockerRegistryCredentials
is correctly configured and saved. - Ensure the 
registry_url
in 
DockerRegistryCredentials
matches the login URL. - Use the credentials in your flow and make sure the flow can access them. - Verify you can manually log in and pull the image. ### Documentation Links - Prefect Docker Registry Credentials - Docker Registry Authentication
By following these steps, you should be able to correctly authenticate and pull images from a private repository within your Prefect workflow. If you continue to see the error, double-check the credentials and permissions for the private repository.
d
@Marvin how can I in this flow.py code make sure when container runs it uses these input flags: 
docker run --gpus all -v "$(pwd)/../test.py:/workspace/test.py" -v /ninjadata:/ninjadata --group-add ninja -it --cap-add SYSLOG  ninja/build:latest python test.py
m
To ensure that the container runs with the specified input flags, you need to pass these flags as parameters when creating and starting the Docker container in your Prefect flow. You can achieve this by adjusting the arguments you pass to the 
create_docker_container
and 
start_docker_container
tasks. Here's how you can do that: ### Updated 
flow.py
Below is an updated version of your 
flow.py
script to include the specified input flags:
Copy code
python
import asyncio
from prefect import flow
from prefect_docker.images import pull_docker_image
from prefect_docker.containers import create_docker_container, start_docker_container
from prefect_docker.credentials import DockerRegistryCredentials

@flow
async def pull_and_run_nginx():
    # Load Docker credentials
    docker_registry_credentials = await DockerRegistryCredentials.load("my-docker-registry")

    # Pull the latest image from the private repository
    image = await pull_docker_image(
        repository="ninja/build",
        tag="latest",
        docker_registry_credentials=docker_registry_credentials
    )
    print(f"Pulled image: {image}")

    # Create a container with the specified flags
    container = await create_docker_container(
        image="ninja/build:latest",
        command=["python", "test.py"],
        volumes=[
            f"{pwd}/../test.py:/workspace/test.py",
            "/ninjadata:/ninjadata"
        ],
        device_requests=[
            {
                'Driver': 'nvidia',
                'Count': -1,
                'Capabilities': [['gpu']],
            },
        ],
        group_add=["ninja"],
        detach=True,
        tty=True,
        stdin_open=True,  # -it is a combination of -i (interactive) and -t (tty)
        cap_add=["SYSLOG"]
    )
    print(f"Created container: {container}")

    # Start the container
    started_container = await start_docker_container(container_id=container.id)
    print(f"Started container: {started_container}")

if __name__ == "__main__":
    asyncio.run(pull_and_run_nginx())
### Key Changes 1. Volumes: Specify volumes to mount using the 
volumes
parameter. 2. Device Requests: Request GPU access with the 
device_requests
parameter. 3. Group: Add a group to the container with 
group_add
. 4. TTY: Enable TTY with 
tty=True
and interactive mode with 
stdin_open=True
. 5. Capabilities: Add SYSLOG capability with 
cap_add
. ### Test and Deploy Deploy and run the Prefect flow:
Copy code
sh
prefect deploy --prefect-file ./prefect.yaml
Run the Docker container as previously configured:
Copy code
sh
docker run -d --name prefect -v /path/to/your/local/project:/opt/prefect -e PREFECT_API_URL=<http://host.docker.internal:4200/api> --workdir /opt/prefect custom-image:latest
### Verifying Setup To ensure the container is running with the expected flags: 1. Check running containers:
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sh
    docker ps
2. Inspect the container:
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sh
    docker inspect <container_id>
3. Logs:
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sh
    docker logs <container_id>
### Documentation Links - Docker Volume - Docker Device Requests for GPU - Docker Capabilities By following these steps, you ensure that your container runs with the specified flags and configurations. If you encounter any issues, make sure to verify the paths and permissions for volumes and other parameters.
d
@Marvin on this flow.py should I use 
@flow
or should I use 
@task
? Also on the last 
prefect.yaml
do I only need to specify the entrypoint or is the command also mandatory?
m
Good questions! Let's address both: ### Flow and Task Decorators 1. @flow vs @task: - @flow: Marks the function as a Prefect flow, which is the top-level orchestration unit. - @task: Marks the function as a Prefect task, which is a single unit of work that can be executed within a flow. In your script, if the entire 
pull_and_run_nginx
process is your main orchestration, then using 
@flow
is appropriate. If you have smaller granular tasks (such as pulling the Docker image, creating the container, starting the container), those should be marked with 
@task
. ### Updated 
flow.py
using @task Here's an updated version using both 
@task
and `@flow`:
Copy code
python
import asyncio
from prefect import flow, task
from prefect_docker.images import pull_docker_image
from prefect_docker.containers import create_docker_container, start_docker_container
from prefect_docker.credentials import DockerRegistryCredentials

@task
async def pull_image(credentials):
    # Pull the latest image from the private repository
    return await pull_docker_image(
        repository="ninja/build",
        tag="latest",
        docker_registry_credentials=credentials
    )

@task
async def create_container():
    import os
    pwd = os.getcwd()
    # Create a container with the specified flags
    return await create_docker_container(
        image="ninja/build:latest",
        command=["python", "test.py"],
        volumes=[
            f"{pwd}/../test.py:/workspace/test.py",
            "/ninjadata:/ninjadata"
        ],
        device_requests=[
            {
                'Driver': 'nvidia',
                'Count': -1,
                'Capabilities': [['gpu']],
            },
        ],
        group_add=["ninja"],
        detach=True,
        tty=True,
        stdin_open=True,  # -it is a combination of -i (interactive) and -t (tty)
        cap_add=["SYSLOG"]
    )

@task
async def start_container(container_id):
    # Start the container
    return await start_docker_container(container_id=container_id)

@flow
async def pull_and_run_nginx():
    # Load Docker credentials
    docker_registry_credentials = await DockerRegistryCredentials.load("my-docker-registry")

    # Pull the image
    image = await pull_image(docker_registry_credentials)
    print(f"Pulled image: {image}")

    # Create the container
    container = await create_container()
    print(f"Created container: {container}")

    # Start the container
    started_container = await start_container(container.id)
    print(f"Started container: {started_container}")

if __name__ == "__main__":
    asyncio.run(pull_and_run_nginx())
### 
prefect.yaml
Configuration 2. Entrypoint and Command: - entrypoint: Specifies the script to run and the function to call. - command: Specifies the command to run within the Docker container. This is typically the command needed to run your script within the Prefect infrastructure. ### Example 
prefect.yaml
Here’s an example of the updated 
prefect.yaml
configuration:
Copy code
yaml
version: 2.0

build: {}

pull: {}

deployments:
  - name: pull-and-run-nginx
    version: 1.0
    entrypoint: "flow.py:pull_and_run_nginx"  # This is the entrypoint for Prefect
    work_queue_name: default
    description: A flow to pull and run nginx:latest Docker container
    storage: {}
    infrastructure:
      type: docker-container
      image: custom-image:latest  # Use your custom image
      command: ["python", "/opt/prefect/flow.py"]  # This is the command to run within the Docker container
### Execution Summary 1. Define Tasks and Flow: - Use 
@task
for smaller units of work. - Use 
@flow
for the main orchestration. 2. Update Deployment Configuration: - Specify the entrypoint for Prefect. - Use the command to run the script in the Docker container. 3. Deploy and Run: - Deploy using 
prefect deploy
.
- Run your Docker container with the necessary volume mounts and environment variables. By following these steps, you ensure that your Prefect flow is correctly structured and can run within your specified Docker environment. If you encounter any issues, feel free to ask for further assistance!
d
@Marvin can you explain why both entrypoint for prefect and command is required to be added in the prefect.yaml file? Wouldn't just entrypoint suffice?
m
Certainly! The need for both an entrypoint and a command in the 
prefect.yaml
file can be clarified by understanding their distinct roles in the context of Prefect deployments and Docker container executions. ### Roles of 
entrypoint
and 
command
1. Entrypoint (Prefect-Specific): - Purpose: The 
entrypoint
specifies the path to the flow function within the specified script file. It's used by Prefect to know where to find the Python function to execute when running the flow. - Example: 
flow.py:pull_and_run_nginx
indicates that Prefect should run the 
pull_and_run_nginx
function within the 
flow.py
module. 2. Command (Docker-Specific): - Purpose: The 
command
specifies what command(s) to run within the Docker container when it starts. This command is required to execute the script or application that will trigger the flow. - Example: 
["python", "/opt/prefect/flow.py"]
tells Docker to execute the 
flow.py
script using the Python interpreter when the container starts. ### Why Both Are Needed Entrypoint Alone Is Not Sufficient: - The 
entrypoint
is specific to Prefect's internal understanding of your flow and orchestrates which function to run, but it doesn't dictate how the script will be executed in the context of a running Docker container. - Prefect needs to know the specific function entrypoint, but Docker needs to know how to start the container and what command to run inside it. Command Is Essential for Docker: - Docker needs to know the 
command
to run inside the container to initiate the Python script where the Prefect flow resides. This ensures that the environment inside the container starts correctly, and the correct script is executed. Without the 
command
, Docker would not know to execute your Python script (
flow.py
), even if Prefect knows the entrypoint function within that script. ### Practical Example Consider the combination of 
entrypoint
and 
command
in the `prefect.yaml`:
Copy code
yaml
version: 2.0

build: {}

pull: {}

deployments:
  - name: pull-and-run-nginx
    version: 1.0
    entrypoint: "flow.py:pull_and_run_nginx"  # Prefect needs this to know which function to call
    work_queue_name: default
    description: A flow to pull and run nginx:latest Docker container
    storage: {}
    infrastructure:
      type: docker-container
      image: custom-image:latest
      command: ["python", "/opt/prefect/flow.py"]  # Docker needs this to know how to start the script
- 
entrypoint: "flow.py:pull_and_run_nginx"
 tells Prefect to run the 
pull_and_run_nginx
function inside the 
flow.py
file. - 
command: ["python", "/opt/prefect/flow.py"]
 tells Docker to run the 
flow.py
file using Python. ### Conclusion Both 
entrypoint
and 
command
are necessary because they serve different purposes: - 
entrypoint
for Prefect's understanding of flow orchestration and locating the specific function to execute. - 
command
for Docker's understanding of how to start and run the container environment, including initiating the Python script. If you have any additional questions or need further clarification, feel free to ask!
d
@Marvin this works but generated the following: Created task run 'pull_image-0' for task 'pull_image' 030745 PM prefect.flow_runs INFO Executing 'pull_image-0' immediately... 030745 PM prefect.flow_runs INFO Created task run 'pull_docker_image-0' for task 'pull_docker_image' 030745 PM pull_image-0 How to print out the stdout from the individual tasks as well? as it pulls the image etc?
m
Your message was too long by 400 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
this works but generated the following:
Created task run 'pull_image-0' for task 'pull_image' 030745 PM prefect.flow_runs INFO Executing 'pull_image-0' immediately... 030745 PM prefect.flow_runs INFO Created task run 'pull_docker_image-0' for task 'pull_docker_image' 030745 PM pull_image-0 prefect.task_runs INFO Executing 'pull_docker_image-0' immediately... 030745 PM pull_image-0 prefect.task_runs INFO Pulling image: seedooinsights/build:11ba042. 030745 PM pull_docker_image-0 prefect.task_runs INFO Finished in state Completed() 030745 PM pull_docker_image-0 prefect.task_runs INFO Finished in state Completed() 030745 PM pull_image-0 prefect.task_runs INFO Created task run 'create_container-0' for task 'create_container' 030745 PM prefect.flow_runs INFO Executing 'create_container-0' immediately... 030745 PM prefect.flow_runs INFO Created task run 'create_docker_container-0' for task 'create_docker_container' 030746 PM create
d
@Marvin this works but generated the following: Created task run 'pull_image-0' for task 'pull_image' 030745 PM prefect.flow_runs INFO Executing 'pull_image-0' immediately... 030745 PM prefect.flow_runs INFO Created task run 'pull_docker_image-0' for task 'pull_docker_image' 030745 PM pull_image-0 How to print out the stdout from the individual tasks as well? as it pulls the image etc?
m
To print the stdout (or logs) of individual tasks, you should use Prefect's logging utilities. Prefect's task and flow decorators allow you to integrate logging conveniently. Here's how you can enhance your 
flow.py
to include detailed logging for each task and ensure that stdout is captured and printed: ### Updated 
flow.py
with Logging 1. Import Logging: - Import logging from Prefect. - Initialize logging in each task and flow. 2. Use Logging in Tasks: - Use the 
get_run_logger
method to log messages during each task's execution. ### Example Code Here's an updated version of 
flow.py
that includes logging for each task:
Copy code
python
import asyncio
import os
from prefect import flow, task, get_run_logger
from prefect_docker.images import pull_docker_image
from prefect_docker.containers import create_docker_container, start_docker_container
from prefect_docker.credentials import DockerRegistryCredentials

@task
async def pull_image(credentials):
    logger = get_run_logger()
    # Pull the latest image from the private repository
    <http://logger.info|logger.info>("Pulling the latest image from the private repository...")
    image = await pull_docker_image(
        repository="ninja/build",
        tag="latest",
        docker_registry_credentials=credentials
    )
    <http://logger.info|logger.info>(f"Pulled image: {image}")
    return image

@task
async def create_container():
    logger = get_run_logger()
    pwd = os.getcwd()
    # Create a container with the specified flags
    <http://logger.info|logger.info>("Creating a container with the specified flags...")
    container = await create_docker_container(
        image="ninja/build:latest",
        command=["python", "test.py"],
        volumes=[
            f"{pwd}/../test.py:/workspace/test.py",
            "/ninjadata:/ninjadata"
        ],
        device_requests=[
            {
                'Driver': 'nvidia',
                'Count': -1,
                'Capabilities': [['gpu']],
            },
        ],
        group_add=["ninja"],
        detach=True,
        tty=True,
        stdin_open=True,  # -it is a combination of -i (interactive) and -t (tty)
        cap_add=["SYSLOG"]
    )
    <http://logger.info|logger.info>(f"Created container: {container}")
    return container

@task
async def start_container(container_id):
    logger = get_run_logger()
    # Start the container
    <http://logger.info|logger.info>(f"Starting container with ID: {container_id}...")
    started_container = await start_docker_container(container_id=container_id)
    <http://logger.info|logger.info>(f"Started container: {started_container}")
    return started_container

@flow
async def pull_and_run_nginx():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Loading Docker credentials...")
    # Load Docker credentials
    docker_registry_credentials = await DockerRegistryCredentials.load("my-docker-registry")

    # Pull the image
    image = await pull_image(docker_registry_credentials)
    <http://logger.info|logger.info>(f"Pulled image: {image}")

    # Create the container
    container = await create_container()
    <http://logger.info|logger.info>(f"Created container: {container}")

    # Start the container
    started_container = await start_container(container.id)
    <http://logger.info|logger.info>(f"Started container: {started_container}")

if __name__ == "__main__":
    asyncio.run(pull_and_run_nginx())
### Logging Configuration Make sure Prefect's logging level is set to capture the level of detail you want. You can configure this in the Prefect configuration file or environment variable:
Copy code
yaml
# ~/.prefect/config.toml
[logging]
level = "INFO"  # You can also set this to DEBUG for more detail
Alternatively, you can set the logging level via environment variables:
Copy code
sh
export PREFECT_LOGGING_LEVEL=INFO
### Summary - Add Logging: Use Prefect's logging utilities (
get_run_logger
) in each task and flow to log messages. - Ensure Logging Level: Make sure the logging level is set to capture INFO (or DEBUG) messages.
- Observe Logs: You will see the logs in stdout during the execution of tasks and flows. These changes will ensure that you get detailed stdout logging from each of the tasks in your flow. If you have any further questions or need additional assistance, feel free to ask! this works but generated the following: Created task run 'pull_image-0' for task 'pull_image' 030745 PM prefect.flow_runs INFO Executing 'pull_image-0' immediately... 030745 PM prefect.flow_runs INFO Created task run 'pull_docker_image-0' for task 'pull_docker_image' 030745 PM pull_image-0 How to print out the stdout from the individual tasks as well? as it pulls the image etc?
d
@Marvin the environment variable do you specify on the prefect server side or the side where you run the flow.py?
m
The specification of environment variables depends on their purpose and where they need to be accessed: 1. Environment Variables for Prefect Server: - These are variables that control the behavior of the Prefect Server, Agent, or other Prefect infrastructure settings. - You specify these on the server side (where Prefect is running), typically to configure logging, API URLs, etc. 2. Environment Variables for Flow Execution: - These are variables needed by your flow during execution, such as credentials, paths, or other configuration settings. - These should be specified on the side where the flow is executed, which could be local machine, Docker container, or any infrastructure managed by Prefect. ### Setting Environment Variables #### 1. Environment Variables on the Prefect Server Side If you need to configure settings related to Prefect itself, like the logging level or API URL, you set these on the Prefect server side: - Prefect Configuration File (config.toml):
Copy code
toml
    # ~/.prefect/config.toml
    [logging]
    level = "INFO"  # You can also set this to DEBUG for more detail

    [server]
    api_url = "<http://host.docker.internal:4200/api>"
- Environment Variables:
Copy code
sh
    export PREFECT_LOGGING_LEVEL=INFO
    export PREFECT_API_URL=<http://host.docker.internal:4200/api>
You would generally configure these in the environment where the Prefect server and agents run, often using 
.bashrc
, 
.zshrc
, or orchestration tools like Kubernetes Secrets or Docker Compose. #### 2. Environment Variables for Flow Execution To set environment variables needed during the execution of the flow, you have several options: - Directly in the Docker Run Command:
Copy code
sh
    docker run -d --name prefect -v /path/to/your/local/project:/opt/prefect -e PREFECT_API_URL=<http://host.docker.internal:4200/api> --workdir /opt/prefect custom-image:latest
- Using Prefect Context and Secrets: Prefect allows for using context variables and secrets to access environment-specific variables:
Copy code
python
    from prefect import task, Flow, context
    from prefect.tasks.secrets import PrefectSecret

    @task
    def use_secret():
        secret_value = context.get("my_secret")
        print(f"The secret value is: {secret_value}")

    with Flow("use-secret") as flow:
        use_secret()

    if __name__ == "__main__":
        flow.run()
Ensure you have set the secret in your Prefect Cloud or Server:
Copy code
sh
    prefect secrets set MY_SECRET="my_secret_value"
- Via Docker Container Configuration: When running your flow inside a Docker container, you can pass in environment variables directly during the container creation:
Copy code
yaml
    # prefect.yaml
    version: 2.0

    build: {}

    pull: {}

    deployments:
      - name: pull-and-run-nginx
        version: 1.0
        entrypoint: "flow.py:pull_and_run_nginx"  # This is the entrypoint for Prefect
        work_queue_name: default
        description: A flow to pull and run nginx:latest Docker container
        storage: {}
        infrastructure:
          type: docker-container
          image: custom-image:latest  # Use your custom image
          command: ["python", "/opt/prefect/flow.py"]
          env:
            PREFECT_API_URL: "<http://host.docker.internal:4200/api>"
            MY_SECRET_ENV: "some_secret_value"
### Summary - Prefect Server Side: Environment variables such as 
PREFECT_LOGGING_LEVEL
and 
PREFECT_API_URL
are set where Prefect server, agents, and related infrastructure is running. - Flow Execution Side: Environment variables used within flow tasks (like credentials or specific execution configs) are set where the flow is executed. This could involve the Docker container, local machine, or via Prefect Secrets.
By understanding these distinctions and configuring environment variables appropriately, you ensure that both Prefect infrastructure and flow execution environments are correctly set up. If you need more specific guidance, feel free to ask!
d
@Marvin in my flow.py: 
import asyncio
from prefect import flow, task
from prefect_docker.images import pull_docker_image
from prefect_docker.containers import create_docker_container, start_docker_container
from prefect_docker.credentials import DockerRegistryCredentials
@task
async def pull_image(credentials):
image = await pull_docker_image(
repository="ninja/build",
tag="latest",
docker_registry_credentials=credentials
)
return image
@flow
async def pull_and_run_image():
docker_registry_credentials = await DockerRegistryCredentials.load("my-docker-registry")
image = await pull_image(docker_registry_credentials)
if __name__ == "__main__":
asyncio.run(pull_and_run_image())
I am trying to pull an docker image from a private registry. the task pull_docker_image-0 was created for task pull_docker_image. Why doesn't the output display the 0be7a4a1cf8e: Waiting 0be7a4a1cf8e: Layer already exists 7394ab65c509: Layer already exists fb28f03e5fe8: Layer already exists 2d03e19fdd90: Layer already exists
m
Your message was too long by 436 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
in my flow.py:
import asyncio from prefect import flow, task, get_run_logger from prefect_docker.images import pull_docker_image from prefect_docker.containers import create_docker_container, start_docker_container from prefect_docker.credentials import DockerRegistryCredentials @task async def pull_image(credentials): logger = get_run_logger() # Pull the latest image from the private repository logger.info("Pulling the latest image from the private repository...") image = await pull_docker_image( repository="ninja/build", tag="latest", docker_registry_credentials=credentials ) logger.info(f"Pulled image: {image}") return image @task async def create_container(): logger = get_run_logger() # Create a container with the specified flags logger.info("Creating a container with the specified flags...") container = await create_docker_container( image="ninja/build:latest", command=["python", "test.py"], volumes=[ "/ninjadata:/ninjadata" ], device_requests=[ { 'Driver': 'nvidia', 'Count': -1, 'Capabilities': [['gpu']], }, ], group_add=["ninja"], detach=True, tty=True, stdin_open=True, # -it is a
Your message was too long by 140 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
in my flow.py:
import asyncio
from prefect import flow, task, get_run_logger
from prefect_docker.images import pull_docker_image
from prefect_docker.containers import create_docker_container, start_docker_container
from prefect_docker.credentials import DockerRegistryCredentials
@task
async def pull_image(credentials):
logger = get_run_logger()
# Pull the latest image from the private repository
<http://logger.info|logger.info>("Pulling the latest image from the private repository...")
image = await pull_docker_image(
repository="ninja/build",
tag="latest",
docker_registry_credentials=credentials
)
<http://logger.info|logger.info>(f"Pulled image: {image}")
return image
@flow
async def pull_and_run_image():
logger = get_run_logger()
<http://logger.info|logger.info>("Loading Docker credentials...")
# Load Docker credentials
docker_registry_credentials = await DockerRegistryCredentials.load("my-docker-registry")
# Pull the image
image = await pull_image(docker_registry_credentials)
<http://logger.info|logger.info>(f"Pulled image: {image}")
if __name__ == "__main__":
`asyncio.run(pull
Your message was too long by 15 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
in my flow.py:
import asyncio
from prefect import flow, task
from prefect_docker.images import pull_docker_image
from prefect_docker.containers import create_docker_container, start_docker_container
from prefect_docker.credentials import DockerRegistryCredentials
@task
async def pull_image(credentials):
image = await pull_docker_image(
repository="ninja/build",
tag="latest",
docker_registry_credentials=credentials
)
return image
@flow
async def pull_and_run_image():
docker_registry_credentials = await DockerRegistryCredentials.load("my-docker-registry")
image = await pull_image(docker_registry_credentials)
if __name__ == "__main__":
asyncio.run(pull_and_run_image())
I am trying to pull an docker image from a private registry. the task pull_docker_image-0 was created for task pull_docker_image. Why doesn't the output display the 0be7a4a1cf8e: Waiting 0be7a4a1cf8e: Layer already exists 7394ab65c509: Layer already exists ec65d0dc039c: Layer already exists a6bc029bd7ed: Layer already exists 125ace385f78: Layer already exists fb28f03e5fe8: Layer
To ensure the detailed output from 
pull_docker_image
(like the one shown by the native Docker CLI), you need to adjust your logging to capture and display the specific steps and details of the pulling process. The 
prefect_docker
integration might not directly echo the same layer-by-layer progress that you'd see with the Docker CLI, but you can enhance your logging to provide more detail. ### Enhanced Logging in Prefect Task We'll use the logging capabilities within Prefect tasks to print more detailed information about the Docker image pulling process. Here's how you can structure your task to capture and print more granular details. ### 
flow.py
with Detailed Logging
Copy code
python
import asyncio
import os
from prefect import flow, task, get_run_logger
from prefect_docker.images import pull_docker_image
from prefect_docker.containers import create_docker_container, start_docker_container
from prefect_docker.credentials import DockerRegistryCredentials

@task
async def pull_image(credentials):
    logger = get_run_logger()
    <http://logger.info|logger.info>("Pulling the latest image from the private repository...")
    
    # Start the pull and log detailed steps
    image = await pull_docker_image(
        repository="ninja/build",
        tag="latest",
        docker_registry_credentials=credentials
    )
    
    # Assuming pull_docker_image returns an object, you can log more details from it
    <http://logger.info|logger.info>(f"Pulled image details: {image.__dict__}")
    return image

@flow
async def pull_and_run_image():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Loading Docker credentials...")
    
    # Load Docker credentials
    docker_registry_credentials = await DockerRegistryCredentials.load("my-docker-registry")

    # Pull the image
    image = await pull_image(docker_registry_credentials)
    <http://logger.info|logger.info>(f"Image pulling complete. Image details: {image}")

if __name__ == "__main__":
    asyncio.run(pull_and_run_image())
### Explanation 1. Logging Setup: - Initialize a logger in each task using 
get_run_logger()
. - Log detailed messages before and after pulling the Docker image. 2. Detailed Logging: - The 
pull_docker_image
function's response is logged with 
image.__dict__
, which prints detailed metadata of the image object. Modify this based on what attributes are available in the response object. ### Capture Docker Pull Details If the 
pull_docker_image
function from 
prefect_docker
isn't providing a detailed log (like the Docker CLI output), you might want to use Python's Docker SDK directly within a Prefect task to achieve this: ### Using Docker SDK for Detailed Output ```python import asyncio import os import docker from prefect import flow, task, get_run_logger from prefect_docker.credentials import DockerRegistryCredentials @task async def pull_image_with_detailed_output(credentials): logger = get_run_logger() logger.info("Pulling the latest image from the private repository...") # Initialize Docker client client = docker.DockerClient(base_url='unix://var/run/docker.sock') # Login to the private repository using credentials registry_url = credentials.registry_url username = credentials.username password = credentials.password.get_secret_value() # Assuming this is a Prefect Secret client.login(username=username, password=password, registry=registry_url) # Pull image with detailed output response = client.images.pull(repository="ninja/build", tag="latest", stream=True, decode=True) # Log detailed response for chunk in response: if 'status' in chunk: logger.info(chunk['status']) if 'progress' in chunk: logger.info(chunk['progress']) @flow async def pull_and_run_image(): logger = get_run_logger() logger.info("Loading Docker credentials...") # Load Docker credentials docker_registry_credentials = await DockerRegistryCredentials.load("my-docker-registry")
# Pull the image with detailed output await pull_image_with_detailed_output(docker_registry_credentials) if name == "__main__": asyncio.run(pull_and_run_image()) ``` ### Explanation 1. Docker SDK Initialization: - Using Python's Docker SDK to interact directly with the Docker daemon. 2. Login to Private Registry: - Login using credentials to the private Docker registry. 3. Pull Image with Stream: - Using the Docker SDK's 
stream=True
and 
decode=True
options to get detailed output in streaming mode. 4. Log Detailed Output: - Printing log messages for each chunk of the response, capturing statuses and progress. By integrating the Docker SDK directly, you ensure detailed logs similar to the native Docker CLI's output during the image pull process. This enhances visibility into the pulling process within your Prefect task and flow. If you need further customization or assistance, feel free to ask!
The Prefect Docker integration does not automatically print detailed layer-by-layer progress information like the native Docker CLI. However, we can use the Python Docker SDK directly to achieve this level of detail in your logs. Here is the full example using the Docker SDK to pull the image and print the detailed progress: ### Using Docker SDK in Prefect Tasks Here is an updated version of 
flow.py
that uses the Python Docker SDK to pull the image and logs detailed output:
Copy code
python
import asyncio
import docker
from prefect import flow, task, get_run_logger
from prefect_docker.credentials import DockerRegistryCredentials

@task
async def pull_image_with_detailed_output(credentials):
    logger = get_run_logger()
    <http://logger.info|logger.info>("Pulling the latest image from the private repository...")

    client = docker.from_env()

    # Login to the Docker registry
    client.login(
        username=credentials.username,
        password=credentials.password.get_secret_value(),
        registry=credentials.registry_url
    )

    # Pull image with detailed output
    image_stream = client.api.pull(
        repository="ninja/build", 
        tag="latest", 
        stream=True, 
        decode=True
    )

    for chunk in image_stream:
        if 'status' in chunk:
            log_message = chunk['status']
            if 'id' in chunk:
                log_message += f" {chunk['id']}"
            if 'progress' in chunk:
                log_message += f" {chunk['progress']}"
            <http://logger.info|logger.info>(log_message)

    # Optionally, verify the image pulled successfully
    image = client.images.get("ninja/build:latest")
    <http://logger.info|logger.info>(f"Pulled image: {image.tags}")

@flow
async def pull_and_run_image():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Loading Docker credentials...")
    
    # Load Docker credentials
    docker_registry_credentials = await DockerRegistryCredentials.load("my-docker-registry")

    # Pull the image with detailed output
    await pull_image_with_detailed_output(docker_registry_credentials)

if __name__ == "__main__":
    asyncio.run(pull_and_run_image())
### Explanation 1. Docker SDK Initialization: - Using Docker SDK to interact directly with the Docker daemon. 2. Login to Private Docker Registry: - Authentication with the private Docker registry is handled using the credentials. 3. Streaming Pull with Detailed Logs: - The 
stream=True
and 
decode=True
options allow you to get a detailed log for each chunk. - Logs detailed status information for each layer during the pull. ### Running the Flow Make sure your environment has the Docker SDK installed:
Copy code
sh
pip install docker
And then run your flow:
Copy code
sh
python flow.py
### Summary This approach directly uses the Docker SDK to provide detailed, real-time feedback on the image-pulling process, closely mirroring what you'd see using the Docker CLI. By integrating this into your Prefect tasks, you get the desired level of detail in your logs. If you need further customization or assistance, feel free to ask!
d
@Marvin how to fix: Worker 'DockerWorker 620bdfa0-5310-47c2-8bed-e2855c22f806' started! 150429.303 | INFO | prefect.worker.docker.dockerworker 620bdfa0-5310-47c2-8bed-e2855c22f806 - Docker container 'statuesque-peccary' has status 'created' 150429.597 | INFO | prefect.worker.docker.dockerworker 620bdfa0-5310-47c2-8bed-e2855c22f806 - Docker container 'statuesque-peccary' has status 'running' 150429.615 | INFO | prefect.flow_runs.worker - Completed submission of flow run '4b4607d3-7775-4d33-8c30-15edb9482dcb' 150432.555 | INFO | prefect.flow_runs.runner - Opening process... frozen runpy128 RuntimeWarning: 'prefect.engine' found in sys.modules after import of package 'prefect', but prior to execution of 'prefect.engine'; this may result in unpredictable behaviour 150434.743 | ERROR | Flow run 'statuesque-peccary' - Flow could not be retrieved from deployment. Traceback (most recent call last):
m
Your message was too long by 187 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
how to fix:
Worker 'DockerWorker 620bdfa0-5310-47c2-8bed-e2855c22f806' started! 150409.059 | INFO | prefect.flow_runs.worker - Worker 'DockerWorker 620bdfa0-5310-47c2-8bed-e2855c22f806' submitting flow run '4b4607d3-7775-4d33-8c30-15edb9482dcb' 150409.143 | INFO | prefect.worker.docker.dockerworker 620bdfa0-5310-47c2-8bed-e2855c22f806 - Pulling image 'prefecthq/prefect:2.19.5-python3.12'... 150429.260 | INFO | prefect.worker.docker.dockerworker 620bdfa0-5310-47c2-8bed-e2855c22f806 - Creating Docker container 'statuesque-peccary'... 150429.303 | INFO | prefect.worker.docker.dockerworker 620bdfa0-5310-47c2-8bed-e2855c22f806 - Docker container 'statuesque-peccary' has status 'created' 150429.597 | INFO | prefect.worker.docker.dockerworker 620bdfa
The error message you encountered, 
Flow could not be retrieved from deployment
, indicates that the flow could not be found or loaded properly from the deployment. This issue could be caused by several factors, including incorrect paths, missing files, or issues with accessing the Docker storage. ### Steps to Resolve the Issue 1. Verify Deployment Configuration: Ensure that your deployment configuration (
prefect.yaml
) is correct and properly references your flow and entrypoint. 2. Ensure Flow Code Accessibility: Make sure the flow code and all required files are accessible from the Docker container when running the flow. 3. Check Dockerfile and Volumes: Verify that your Dockerfile is correctly set up and that volume mounts are configured to make the flow code accessible inside the Docker container. ### Example Deployment Configuration Here's an example of a correct deployment configuration:
Copy code
yaml
version: 2.0

build: {}

pull: {}

deployments:
  - name: pull-and-run-nginx
    version: 1.0
    entrypoint: "flow.py:pull_and_run_nginx"  # This is the entrypoint for Prefect
    work_queue_name: default
    description: A flow to pull and run nginx:latest Docker container
    storage: {}
    infrastructure:
      type: docker-container
      image: custom-image:latest  # Use your custom image
      command: ["python", "/opt/prefect/flow.py"]
      volumes:
        - "/path/to/your/local/project:/opt/prefect"
      env:
        PREFECT_API_URL: "<http://host.docker.internal:4200/api>"
        MY_SECRET_ENV: "some_secret_value"
Ensure that the path to your local project and the flow script is correctly mounted inside the Docker container. ### Docker Run Command Ensure you are using the correct 
docker run
command to make sure all required files are correctly mounted and the environment is properly set:
Copy code
sh
docker run -d --name prefect -v /path/to/your/local/project:/opt/prefect -e PREFECT_API_URL=<http://host.docker.internal:4200/api> -e MY_SECRET_ENV=some_secret_value --workdir /opt/prefect custom-image:latest
### Additional Debug Steps 1. Inspect Docker Logs: Check the logs of the Docker container to see detailed error messages:
Copy code
sh
   docker logs <container_id>
2. Run Docker Container Interactively: Run an interactive Docker container to manually inspect file paths and contents:
Copy code
sh
   docker run -it --name prefect -v /path/to/your/local/project:/opt/prefect -e PREFECT_API_URL=<http://host.docker.internal:4200/api> -e MY_SECRET_ENV=some_secret_value --workdir /opt/prefect custom-image:latest /bin/bash
3. Verify Prefect Deployment Path: Ensure the actual deployment path and entrypoint match what Prefect is trying to access. ### Additional References - Prefect Deployment Documentation - Docker Volume Mounting By following these steps, you should be able to diagnose and fix the issue. If the problem persists, please provide more details about your deployment setup, Dockerfile, and configuration to allow for further troubleshooting.
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