https://prefect.io logo
Join Slack
Powered by
<@ULVA73B9P> I have build a docker container that ...
# ask-marvin
o
@Marvin I have build a docker container that contains my flow code. How can i now make a deployment to prefect to run the flow in that container?
m
To help you with deploying your flow using a Docker container in Prefect, I need to determine the version of Prefect you're using, as the process can vary between versions. Could you please confirm if you're using Prefect 2.x or 3.x?
o
@Marvin I am using version 3.1.5
m
To deploy your flow using a Docker container in Prefect 3.1.5, you'll need to follow these steps: 1. Docker Image Setup: First, make sure your flow is properly encapsulated in a Docker image that includes all necessary dependencies. You can utilize a Dockerfile for this purpose to build the image locally. 2. Docker Registry: If you're planning to run this in a production environment, consider pushing your Docker image to a registry like Docker Hub. For local development, this step can be skipped. 3. Prefect Deployment Configuration: Use the 
flow.deploy
method to specify the deployment configuration. This includes associating the deployment with a work pool and providing information about the Docker image to be used. You can also specify options like 
build=True
to automatically build the Docker image during deployment and 
push=True
to push the image to a registry if needed. 4. Running the Deployment: Execute your script that contains the deployment configuration using 
flow.deploy
. Here are some key resources and guides: - An introduction to deploying via Python: Deploy via Python - Examples of using Docker for deployment: Infrastructure Examples with Docker Remember, Prefect also generates a Dockerfile to help facilitate building your image, and you can customize it as needed. In case you choose to forgo a registry because you're as introverted as a Docker container in isolation mode, remember to set 
push=False
during deployment!
o
@Marvin all of the example talk about deploying a flow from an existing flow function e.g.
Copy code
@flow(log_prints=True)
def buy():
    print("Buying securities")


if __name__ == "__main__":
    buy.deploy(
        name="my-custom-dockerfile-deployment",
        work_pool_name="my-docker-pool",
        image=DockerImage(
            name="my_image",
            tag="deploy-guide",
            dockerfile="Dockerfile"
    ),
    push=False
)
but in my case i already have the docker image I want to deploy. I don't need to build a new image, i just want to deploy so that I can run the docker image on my worker. How do I do that?
m
To deploy your flow using an existing Docker image in Prefect 3.1.5, you can configure the deployment to use your pre-built image without needing to build a new one. Here's how you can do that: 1. Set 
build=False
: When calling the 
deploy
method, specify 
build=False
to indicate that Prefect should not attempt to build a new Docker image. 2. Configure the Image: Specify the Docker image that you want to use by setting the 
image
parameter with the name and tag of your existing image. Here’s an example of how you can define this in your deployment script:
Copy code
python
from prefect import flow

@flow(log_prints=True)
def buy():
    print("Buying securities")

if __name__ == "__main__":
    buy.deploy(
        name="my-custom-docker-deployment",
        work_pool_name="my-docker-pool",
        image="your_existing_image:latest",
        build=False,   # This skips the image building process
        push=False     # Ensure the image is not pushed to any registry
    )
In this setup, replace 
"your_existing_image:latest"
with the actual name and tag of your Docker image. For more guidance, you can refer to Prefect's documentation on Docker infrastructure examples: Infrastructure Examples with Docker. By doing this, you'll be able to deploy your flow using your already crafted Docker environment—like hiring a worker who's already clocked in and ready!
o
@Marvin why do I still need
Copy code
@flow(log_prints=True)
def buy():
    print("Buying securities")
in that case?
m
You would still need to define the 
@flow
decorated function in your deployment script because Prefect uses this function as the main entry point for the flow logic you want to execute. Even if you're deploying using an existing Docker image, Prefect requires a reference to a flow function to manage and orchestrate the task, including execution logic and logging setup. Think of it like having a blueprint for a building; even if the materials (your Docker image) are all there, you need a plan (the flow function) to ensure everything is structured properly. It's Prefect's way of keeping things orderly in the chaotic universe of task orchestration. If your actual flow logic is embedded within the Docker image, you can keep the flow function minimal and use it primarily as a placeholder to trigger the execution within your infrastructure setup.
o
@Marvin if my flow logic (the flow function i want to run) is in the Docker image, how do I reference it in my deployment script?
m
If your flow logic is encapsulated within a Docker image, and you want to deploy it using Prefect, you'll typically define a flow-like function in your deployment script that serves merely as an interface for Prefect to hook into your deployment: 1. Define a Minimal Flow: Create a minimal flow function that functions as an entry point. You won't need to put your entire logic here, just enough to let Prefect manage the encapsulated logic within your Docker image. 2. Configure the Deployment: Specify your Docker image and ensure Prefect knows where to look for logic within the Docker image. Unfortunately, Prefect does not directly support running code "hidden" within the Docker image in the same way it does Python code, since flow orchestration requires Prefect being able to interact with, modify, or observe what's happening. However, if your logic is part of a script or entry point command in your Docker image, you might need to consider adjusting the Docker container's entry point or use Prefect's environment variables or parameters to influence the execution accordingly. Here is a hypothetical setup:
Copy code
python
from prefect import flow

@flow
def launch_flow_in_container():
    # Minimal flow that Prefect can use to execute your setup
    pass

if __name__ == "__main__":
    launch_flow_in_container.deploy(
        name="existing-docker-image-deployment",
        work_pool_name="docker-pool",
        image="your_existing_image:latest",
        build=False,
        push=False
    )
In this trivial example, 
launch_flow_in_container
is essentially just a trigger point for Prefect to initiate processes managed by your Docker image. You would need to configure your Docker image such that its default execution path or commands run your actual logic when the container is spun up. Consider referring to Prefect's documentation for specific adjustments to the Docker command, entry point, or environment variables that might fit your use case. There might also be a benefit in checking community forums or examples for similar setups!
o
@Marvin I made a 
deploy.py
file (below) which I ran
Copy code
from prefect.client.schemas.schedules import IntervalSchedule
from prefect import deploy, flow
import datetime

if __name__ == "__main__":
    
    @flow
    def dummy_flow():
        pass
    
    dummy_flow.deploy(
        name="gmk-ollie",
        image="<http://registry.gitlab.com/mycompany/lab/project/project:dev|registry.gitlab.com/mycompany/lab/project/project:dev>",
        work_pool_name="ollie-pool",
        job_variables={"env": {}},
        parameters={"prefect_secret_to_env_var": {}},
        schedules=[
            IntervalSchedule(
                interval=datetime.timedelta(minutes=10),
            )
        ],
        build=False,
        push=False
    )
which does trigger the worker to pull the docker container, but then i get an error on the worker
Copy code
19:41:53.184 | INFO    | prefect.workers.docker.ollie - Docker container 'steady-shrimp' has status 'running' 
19:41:58.844 | INFO    | prefect.flow_runs.runner - Opening process...
19:42:03.176 | INFO    | Flow run 'steady-shrimp' - Downloading flow code from storage at '.'
19:42:03.200 | ERROR   | prefect.engine - Engine execution of flow run 'c526cdfc-ff92-4909-9cfe-cee07ac09ecc' exited with unexpected exception
Traceback (most recent call last):
  File "<frozen importlib._bootstrap_external>", line 936, in exec_module
  File "<frozen importlib._bootstrap_external>", line 1073, in get_code
  File "<frozen importlib._bootstrap_external>", line 1130, in get_data
FileNotFoundError: [Errno 2] No such file or directory: '/app/deploy.py
so the worker is trying to run the 
deploy.py
which is definitely not correct.
m
Your message was too long by 131 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
I made a 
deploy.py
file (below) which I ran
Copy code
from prefect.client.schemas.schedules import IntervalSchedule
from prefect import deploy, flow
import datetime

if __name__ == "__main__":
    
    @flow
    def dummy_flow():
        pass
    
    dummy_flow.deploy(
        name="gmk-ollie",
        image="<http://registry.gitlab.com/mycompany/lab/project/project:dev|registry.gitlab.com/mycompany/lab/project/project:dev>",
        work_pool_name="ollie-pool",
        job_variables={"env": {}},
        parameters={"prefect_secret_to_env_var": {}},
        schedules=[
            IntervalSchedule(
                interval=datetime.timedelta(minutes=10),
            )
        ],
        build=False,
        push=False
    )
which does trigger the worker to pull the docker container, but then i get an error on the worker ```194153.184 | INFO | prefect.workers.docker.ollie - Docker container 'steady-shrimp' has status 'running' 194158.844 | INFO | prefect.flow_runs.runner - Opening process... 194203.176 | INFO | Flow run 'steady-shrimp' - Downloading flow code from storage at '.' 194203.200 | ERROR | prefect.engine - Engine execution of flow run 'c526cdfc-ff92-4909-9c
o
@Marvin I made a 
deploy.py
file (below) which I ran
Copy code
from prefect.client.schemas.schedules import IntervalSchedule
from prefect import deploy, flow
import datetime

if __name__ == "__main__":
    
    @flow
    def dummy_flow():
        pass
    
    dummy_flow.deploy(
        name="gmk-ollie",
        image="<http://registry.gitlab.com/mycompany/lab/project/project:dev|registry.gitlab.com/mycompany/lab/project/project:dev>",
        work_pool_name="ollie-pool",
        job_variables={"env": {}},
        parameters={"prefect_secret_to_env_var": {}},
        schedules=[
            IntervalSchedule(
                interval=datetime.timedelta(minutes=10),
            )
        ],
        build=False,
        push=False
    )
but on the worker i get an error on the worker
Copy code
19:42:03.176 | INFO    | Flow run 'steady-shrimp' - Downloading flow code from storage at '.'
19:42:03.200 | ERROR   | prefect.engine - Engine execution of flow run 'c526cdfc-ff92-4909-9cfe-cee07ac09ecc' exited with unexpected exception
Traceback (most recent call last):
  File "<frozen importlib._bootstrap_external>", line 936, in exec_module
  File "<frozen importlib._bootstrap_external>", line 1073, in get_code
  File "<frozen importlib._bootstrap_external>", line 1130, in get_data
FileNotFoundError: [Errno 2] No such file or directory: '/app/deploy.py
m
Your message was too long by 50 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
I made a 
deploy.py
file (below) which I ran
Copy code
from prefect.client.schemas.schedules import IntervalSchedule
from prefect import deploy, flow
import datetime

if __name__ == "__main__":
    
    @flow
    def dummy_flow():
        pass
    
    dummy_flow.deploy(
        name="gmk-ollie",
        image="<http://registry.gitlab.com/mycompany/lab/project/project:dev|registry.gitlab.com/mycompany/lab/project/project:dev>",
        work_pool_name="ollie-pool",
        job_variables={"env": {}},
        parameters={"prefect_secret_to_env_var": {}},
        schedules=[
            IntervalSchedule(
                interval=datetime.timedelta(minutes=10),
            )
        ],
        build=False,
        push=False
    )
but on the worker i get an error on the worker ```194203.176 | INFO | Flow run 'steady-shrimp' - Downloading flow code from storage at '.' 194203.200 | ERROR | prefect.engine - Engine execution of flow run 'c526cdfc-ff92-4909-9cfe-cee07ac09ecc' exited with unexpected exception Traceback (most recent call last): File "<frozen importlib._bootstrap_external>", line 936, in exec_module File "<frozen importlib._bootstrap_external>", line 1073,
o
@Marvin I made a 
deploy.py
file (below) which I ran
Copy code
from prefect.client.schemas.schedules import IntervalSchedule
from prefect import deploy, flow
import datetime

if __name__ == "__main__":
    
    @flow
    def dummy_flow():
        pass
    
    dummy_flow.deploy(
        name="gmk-ollie",
        image="<http://registry.gitlab.com/mycompany/lab/project/project:dev|registry.gitlab.com/mycompany/lab/project/project:dev>",
        work_pool_name="ollie-pool",
        job_variables={"env": {}},
        parameters={"prefect_secret_to_env_var": {}},
        build=False,
        push=False
    )
but on the worker i get an exceptionon the worker
Copy code
Traceback (most recent call last):
  File "<frozen importlib._bootstrap_external>", line 936, in exec_module
  File "<frozen importlib._bootstrap_external>", line 1073, in get_code
  File "<frozen importlib._bootstrap_external>", line 1130, in get_data
FileNotFoundError: [Errno 2] No such file or directory: '/app/deploy.py
m
The error message you're seeing indicates that the worker is attempting to find and execute a file named 
deploy.py
within the directory where your Docker container environment is running, but it's not present there. This often happens because the container's execution environment doesn't have the necessary files or paths set correctly. To solve this issues, you need to ensure that the necessary files and paths are correctly set inside your Docker image: 1. Correct Working Directory: Make sure that your Dockerfile sets the correct working directory, and includes the 
deploy.py
and any associated necessary files in the image. You can use the 
WORKDIR
and 
COPY
instructions in your Dockerfile to achieve this. For example:
Copy code
FROM python:3.9
   WORKDIR /app
   COPY . .
2. Build and Tag your Docker Image Again: After ensuring your files are correctly included in your Dockerfile, rebuild your Docker image and push (if needed) to your registry:
Copy code
docker build -t <http://registry.gitlab.com/mycompany/lab/project/project:dev|registry.gitlab.com/mycompany/lab/project/project:dev> .
3. Verify the Image Contents: Before deploying with Prefect again, you can manually run your Docker container to verify that it contains all necessary files:
Copy code
docker run -it <http://registry.gitlab.com/mycompany/lab/project/project:dev|registry.gitlab.com/mycompany/lab/project/project:dev> bash
From inside the container, check if 
/app/deploy.py
is present. 4. Set the Correct Entry Point: Ensure that your image’s entry point or command correctly starts the process. You may need to adjust it in the Dockerfile as:
Copy code
ENTRYPOINT ["python", "/app/deploy.py"]
By following these steps, your Docker image will include the necessary script, and the worker will be able to access and execute it correctly once deployed via Prefect. After making the changes, remember to update your prefect deployment to point to the newly built image. If only machines had the same stubborn tenacity as bugs in a program!
o
@Marvin but in this case, 
deploy.py
is the script i want to run to deploy the image to the worker. I don't want 
deploy.py
to actually be in the Docker image
m
I see, that clarifies the situation! The 
deploy.py
script is meant to run locally on your machine, not within the Docker container, as its purpose is to set up the deployment rather than execute within it. The error you're encountering on the worker suggests that it might still be expecting some artifact or file that is only locally available, or there's a misconfiguration in how the Docker container is set up or how its default command is specified. Here's how you might address this: 1. Local Deployment Script: Ensure 
deploy.py
is executed locally to create a deployment that points to your existing Docker image. This script is only needed to configure and register the deployment with Prefect. 2. Container Execution: The Docker image used for the deployment should have its own entry point or command configured to execute the flow logic. This should be set within your Dockerfile, not from inside the 
deploy.py
. This typically looks like: In your Dockerfile:
Copy code
CMD ["python", "path/to/your/flow_script.py"]
This could be any script or command that launches your application logic contained within the Docker image. 3. Deployment Configuration: When deploying, ensure you are specifying all environment variables, secrets, and other configurations that the Docker container might expect but are not explicitly mentioned in your 
deploy.py
. 4. Errors in Docker Configuration: Double-check the Docker image and Prefect deployment configuration to ensure no paths or resources are incorrectly specified to be located within the Docker container when they should exist on the host environment. After the deployment created by 
deploy.py
, trigger your flow execution using the Prefect interface or CLI. The worker will spin up a container based on the image and commands you've configured in your Dockerfile. Like a well-oiled machine, each part of the process should operate independently but come together harmoniously at execution time. If issues persist, examining logs both from Prefect’s UI and directly from Docker containers often gives a clue about environmental mismatches or missing dependencies.
o
@Marvin I made this work by renaming my 
deploy.py
file to 
run.py
, and putting it inside a folder such to mimic the folder structure of the entry point file in my docker image. Now i have 
project/run.py
which is
Copy code
from prefect.client.schemas.schedules import IntervalSchedule
from prefect import deploy, flow
import datetime

if __name__ == "__main__":
    
    @flow
    def dummy_flow():
        pass
    
    dummy_flow.deploy(
        name="gmk-ollie",
        image="<http://registry.gitlab.com/mycompany/lab/project/project:dev|registry.gitlab.com/mycompany/lab/project/project:dev>",
        work_pool_name="ollie-pool",
        job_variables={"env": {}},
        parameters={"prefect_secret_to_env_var": {}},
        build=False,
        push=False
    )
When i run that with 
python project/run.py
it runs the container on the worker successfully. This feels a bit hacky though to have to mimic the file structure in the docker container. Is there a better way?
m
It's great to hear you found a solution, even if it feels a bit like you've woven a cunning web. The approach you're using mimics the directory structure of your Docker image locally to avoid issues with file paths in Prefect deployments. While your current method works, here are a few suggestions that might offer a cleaner or more flexible solution: 1. Environment Variables for Paths: Use environment variables to define paths that can dynamically change depending on whether it's running in a local or containerized environment. This way, the script itself doesn't need alteration based on its environment. 2. Prefect Parameters or Variables: Utilize Prefect’s parameter or configuration system to specify paths. It involves passing different parameters when deploying the script or from Prefect's UI/CLI. 3. Use Prefect's Storage Configuration: Prefect allows you to configure storage options that might help alleviate the need for specific path structures by storing the flow in a compatible Prefect storage module. 4. Refactor Deployment Script: Consider separating concerns in your script: keep deployment configurations in 
deploy.py
and execution logic elsewhere if the deployment configuration isn't actually running within the container. 5. Inline Scripts or Custom Entry Points: Adjust the Docker image’s entry point to use a more dynamic method of determining what script to run (checking for running env, etc.). However, this increases Docker image complexity. Ultimately, the "hackiness" often comes with finding just the right balance of configuration complexity and flexibility. Much like aligning all those tiny cogs in a vintage timepiece, you want the pieces to fit naturally and operate smoothly. If you're happy with how it runs and it meets your needs efficiently, sometimes those clever workarounds are just what make systems resilient and unique! But exploring these options can definitely enhance maintainability and scalability.
7 Views
Open in Slack
PreviousNext
Powered by