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<@ULVA73B9P> I am using Prefect 3 locally using do...
# ask-marvin
c
@Marvin I am using Prefect 3 locally using docker to run the server, and then for each flow, we deplpoy to docker (local). It was working before but now it's not after rebooting my machine. I think it's due to the DOCKER_HOST env variable (whcih is not set currently). I am on MacOS and Docker Desktop is running. Flow deploy co
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ctos_monitoring_list_flow.deploy(
            name=config["name"],
            work_pool_name="local-docker-work-pool",
            work_queue_name="ctos-monitoring-list-queue",
            image=DockerImage(
                name="prefecthq/prefect", tag="3-latest", dockerfile="Dockerfile.worker"
            ),
            parameters=config["params"],
            cron=config["schedule"],
            job_variables={"networks": ["dmp_default", "dmp-etl-prefect_default"]},
            push=False,
            build=True,
        )
Error when trying to deploy:
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The above exception was the direct cause of the following exception:

...
  File "/Users/cdrum/Development/alternatives/dmp-etl-prefect/dmp-etl-venv/lib/python3.13/site-packages/prefect/utilities/dockerutils.py", line 166, in build_image
    with docker_client() as client:
         ~~~~~~~~~~~~~^^
  File "/opt/homebrew/Cellar/python@3.13/3.13.0_1/Frameworks/Python.framework/Versions/3.13/lib/python3.13/contextlib.py", line 141, in __enter__
    return next(self.gen)
  File "/Users/cdrum/Development/alternatives/dmp-etl-prefect/dmp-etl-venv/lib/python3.13/site-packages/prefect/utilities/dockerutils.py", line 115, in docker_client
    raise RuntimeError(
        "This error is often thrown because Docker is not running. Please ensure Docker is running."
    ) from exc
RuntimeError: This error is often thrown because Docker is not running. Please ensure Docker is running.
m
It seems like your issue might be related to Docker not being properly configured after your reboot. The error message suggests that Docker might not be running, but since you mentioned Docker Desktop is indeed running, the problem might be with the 
DOCKER_HOST
environment variable or Docker's connection settings. Here are a few steps and considerations: 1. Check Docker Connection: Ensure that Docker is accessible from the terminal. You can try running a simple Docker command like 
docker ps
to see if it lists running containers. If you encounter any issues, Docker might not be properly configured in your shell environment. 2. Set 
DOCKER_HOST
Environment Variable: On MacOS, if Docker is running but not connecting, you might need to set the 
DOCKER_HOST
environment variable. Typically, for Docker Desktop, this should not be necessary, but you can try setting it to 
unix:///var/run/docker.sock
temporarily to see if it resolves the issue. 3. Prefect Docker Deployment: Ensure your deployment setup is correct. The Prefect 3.x documentation mentions using the 
DockerImage
class for custom Dockerfiles. Make sure your Dockerfile is correctly configured and accessible. More details can be found in the Prefect Docker Deployment Guide. 4. Review Prefect 3.x Changes: Prefect 3.x has replaced agents with workers and work pools. Ensure your deployment configuration aligns with these changes. You can review the Prefect 3.x Gotchas for more details. If you're still having issues after these checks, there might be a deeper configuration problem, possibly related to Docker Desktop's settings or Prefect's internal Docker client.
c
docker ps
from the command line returns the containers, so docker is indeed running.
@Marvin setting DOCKER_HOST to unix:///var/run/docker.sock, while 
docker ps
works, the deploy code still cannot find docker.
m
The issue you're experiencing with Prefect not recognizing Docker, despite Docker being operational, seems to be a known problem. Here's a summary of relevant findings from GitHub issues: 1. RuntimeError with Docker Client: This error often occurs when Prefect tries to build a Docker image but fails to connect to Docker. The error message you're seeing is typically thrown when Docker is not running, but in your case, it might be an issue with how Prefect is trying to connect to Docker. 2. URLSchemeUnknown Error: One of the issues reported on GitHub discusses an error related to an unsupported URL scheme (
http+docker
). This might indicate a problem with the Docker client configuration or how the Docker environment variables are set. 3. Potential Solutions: - Verify Docker Client Configuration: Ensure that the Docker client used by Prefect is correctly configured. You might need to check the Docker SDK for Python and ensure it's correctly set up. - Environment Variables: Double-check the 
DOCKER_HOST
and other related environment variables. Sometimes setting 
DOCKER_HOST
to 
unix:///var/run/docker.sock
might not be sufficient if other configurations are off. - Docker SDK for Python: Make sure you have the correct version of the Docker SDK for Python installed. Sometimes, updating or reinstalling this package can resolve connection issues. 4. Community Discussions: There are ongoing discussions and reports about similar issues in the Prefect community. You might want to follow this GitHub issue for updates or potential fixes. If these steps don't resolve the issue, consider reaching out to the Prefect community or checking for any open issues on Prefect's GitHub repository that might be related to your problem.
c
@Marvin ok i resolved that by setting DOCKER_HOST environment to point to my sock file. Now. the issue. when the flow runs via Docker, it still can't find the docker. Error:
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Reported flow run 'a780e342-3551-4fc4-bb81-aba4d534cc4f' as crashed: Flow run could not be submitted to infrastructure:
RuntimeError('Could not connect to Docker.')
08:53:01 AM
prefect.flow_runs.worker
Failed to submit flow run 'a780e342-3551-4fc4-bb81-aba4d534cc4f' to infrastructure.
Traceback (most recent call last):
  File "/Users/cdrum/Development/alternatives/dmp-etl-prefect/dmp-etl-venv/lib/python3.13/site-packages/urllib3/connectionpool.py", line 787, in urlopen
    response = self._make_request(
        conn,
    ...<10 lines>...
        **response_kw,
    )
  File "/Users/cdrum/Development/alternatives/dmp-etl-prefect/dmp-etl-venv/lib/python3.13/site-packages/urllib3/connectionpool.py", line 493, in _make_request
    conn.request(
    ~~~~~~~~~~~~^
        method,
        ^^^^^^^
    ...<6 lines>...
        enforce_content_length=enforce_content_length,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
m
The error you're encountering, "Could not connect to Docker," suggests that the Prefect flow running inside the Docker container is unable to access the Docker daemon. Here are some potential solutions and considerations: 1. Docker Socket Access: Ensure that the Docker socket is correctly mounted in the container. When running Prefect flows inside a Docker container, the Docker socket (
/var/run/docker.sock
) must be accessible to allow the container to communicate with the Docker daemon. You can achieve this by adding a volume mount to your Docker run command:
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bash
   -v /var/run/docker.sock:/var/run/docker.sock
2. Docker Group Permissions: Verify that the user running the Docker container has the necessary permissions to access the Docker socket. On Unix-like systems, this typically means the user should be part of the 
docker
group. 3. Environment Variables: Double-check the environment variables within the container. Ensure that 
DOCKER_HOST
and any other relevant Docker environment variables are correctly set inside the container. 4. Network Configuration: If you are running Docker in a more complex network setup, make sure that the container has the necessary network permissions and configurations to access the Docker daemon. If these steps do not resolve the issue, consider reviewing the discussions and potential fixes in the Prefect community or GitHub issues, such as this one, which discusses similar connection problems and their resolutions.
c
@Marvin all working now. Now i have the issue of the containers that are started to run a flow, don't seem to stop. sop i have a ton of ... i guess orphaned containers?
m
It seems like you're encountering an issue where Docker containers started by Prefect for running flows are not stopping as expected. This can lead to a buildup of orphaned containers. Here are some steps and considerations to help manage this situation: 1. Auto Remove Setting: When deploying flows with Docker, ensure that the containers are set to automatically remove themselves after the flow run is complete. You can achieve this by setting the 
auto_remove
parameter to 
True
in your Docker deployment configuration. This will ensure that containers are cleaned up automatically once they finish executing. 2. Manual Cleanup: If containers are already orphaned, you can manually remove them using Docker commands. Use 
docker ps -a
to list all containers, and then 
docker rm <container_id>
to remove any that are no longer needed. 3. Docker Deployment Configuration: Review your deployment setup to ensure it aligns with best practices for container lifecycle management. You can refer to the Prefect Docker Deployment Guide for more details on configuring Docker deployments with Prefect. 4. Prefect Docker Library: Consider using the 
prefect-docker
library, which provides integration capabilities and might offer additional options for managing Docker containers effectively. You can find more information in the Prefect Docker Integration Docs. Implementing these strategies should help you manage and clean up Docker containers more effectively. If the problem persists, you might want to look into logging and monitoring solutions to better understand why containers aren't stopping as expected.
c
TypeError: Flow.deploy() got an unexpected keyword argument 'auto_remove'
@Marvin TypeError: Flow.deploy() got an unexpected keyword argument 'auto_remove'
m
It seems that the 
auto_remove
parameter is not directly available in the 
Flow.deploy()
method for Prefect 3.x. Instead, container cleanup should be managed through Docker's configuration or by using Prefect's infrastructure settings. Here are some options to manage the lifecycle of Docker containers: 1. Docker Infrastructure Configuration: Ensure that your Docker infrastructure is configured to automatically remove containers after they stop. This can be done by setting the 
auto_remove
option within the Docker container configuration itself, not directly in the Prefect flow deployment. Unfortunately, the specific documentation for this setup wasn't found, but you can typically configure this through Docker's API or CLI commands. 2. Prefect Docker Library: Consider using the 
prefect-docker
library, which might provide additional capabilities for managing Docker containers. More information can be found in the Prefect Docker Integration Docs. 3. Manual Cleanup: If automatic cleanup isn't working as expected, you might need to periodically run a cleanup script that removes stopped containers. This can be done using Docker commands like 
docker container prune
to remove all stopped containers. For more detailed configuration or if you encounter further issues, you might want to explore Prefect's community resources or GitHub issues for insights into best practices for managing Docker containers with Prefect.
c
@Marvin how does the toml config work? Can I put this stuff in a toml so when it's deployed to ECS they will be applied? prefect gcl create ctos-api --limit 60 --slot-decay-per-second 0.016 prefect work-pool create --overwrite --type docker local-docker-work-pool prefect work-queue create ctos-monitoring-list-queue --pool local-docker-work-pool
m
In Prefect 3.x, you can use a 
prefect.toml
file to configure settings and profiles for your workflows. This allows you to customize behavior across different environments, such as development, testing, and production, without changing your workflow code. Here's how you can use TOML configuration: 1. Creating a 
prefect.toml
File: Place a 
prefect.toml
file in your project directory to declare settings. This file can be used to adjust environment-specific configurations, such as logging levels, retry attempts, and more. 2. Example Configuration:
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toml
   [settings]
   PREFECT_LOGGING_LEVEL = "INFO"
   PREFECT_API_URL = "<http://localhost:4200/api>"
3. Profiles: You can define different profiles within 
prefect.toml
to switch between configurations easily. This is useful for managing different environments. 4. Deployment and Infrastructure: While 
prefect.toml
is great for settings, deployment configurations are typically handled using a 
prefect.yaml
file. This file can contain deployment-specific settings, such as work pools and queues. 5. Using Prefect CLI: Prefect CLI commands like 
prefect work-pool create
and 
prefect work-queue create
are typically executed as part of your deployment scripts or setup processes, not directly through TOML. For more detailed configuration options, you can refer to the Prefect Settings and Profiles Documentation and the Prefect YAML Deployment Guide. These resources provide comprehensive guidance on how to structure and use configuration files in Prefect 3.x.
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