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Hi <@ULVA73B9P>. I'm trying to deploy a simple flo...
# ask-marvin
n
Hi @Marvin. I'm trying to deploy a simple flow that has access to the local file system that the worker pool is running on. using flow.deploy() it builds an image and runs nicely but I can't figure out how to mount local volumes to the container. how can I do this?
m
To mount local volumes to a container when deploying a Prefect flow, you should configure your deployment to specify the volume mounts. Here's a general approach on how to achieve this: 1. Configure Docker Deployment: When you deploy a flow using Prefect, you can specify the Docker infrastructure settings. This includes setting volume mounts. 2. Volume Mounts in Docker: You can specify volume mounts in your Docker configuration. For example, in a 
docker-compose.yaml
file, you might have:
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yaml
    services:
      prefect:
        image: prefecthq/prefect:3.0.0rc2-python3.12
        volumes:
          - /local/path:/container/path
3. Using Prefect's Deployment Configuration: If you are using Prefect 3.x, you might need to define these settings in your deployment YAML or through the Prefect UI if it supports such configurations. You can refer to Prefect's Docker Infrastructure Example for more details. 4. Running Local Workers: Ensure that your workers are running on the local machine where the volume is accessible. This is crucial since the worker needs to access the local file system. For more detailed guidance, you can refer to the Prefect 3.x documentation on Docker deployments. If you have specific configurations or setups, feel free to share them, and I can help tailor the instructions further.
n
@Marvin can you give me a very simple example of a flow and deployment that would use this docker compose file
m
Here's a simple example of a Prefect flow and deployment using your Docker Compose setup. This example assumes that you have a Docker Compose file configured to mount local volumes, similar to the one I mentioned earlier. ### Flow Definition
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python
from prefect import flow, task

@task
def read_file(file_path):
    with open(file_path, 'r') as file:
        data = file.read()
    return data

@flow
def file_reader_flow(file_path):
    content = read_file(file_path)
    print(content)

if __name__ == "__main__":
    file_reader_flow("/app/data.txt")
### Deployment Configuration You can create a deployment YAML file to specify the Docker infrastructure and volume mounts. Here's an example:
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yaml
name: file-reader-deployment
flow: file_reader_flow.py
infrastructure:
  type: docker-container
  image: prefecthq/prefect:3.0.0rc2-python3.12
  volumes:
    - /local/path:/app
### Docker Compose File Ensure your 
docker-compose.yaml
includes the volume mount:
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yaml
services:
  prefect:
    image: prefecthq/prefect:3.0.0rc2-python3.12
    volumes:
      - /local/path:/app
### Running the Deployment 1. Build and Run Docker Compose: Ensure your Docker Compose is up and running.
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bash
   docker-compose up -d
2. Deploy the Flow: Use Prefect's CLI or Python API to deploy the flow.
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bash
   prefect deploy file_reader_flow.py
This setup will mount the local 
/local/path
directory to 
/app
in the container, allowing the flow to read files located in that directory. Adjust paths and configurations as needed for your environment.
n
@Marvin don't I need to define a worker pool name or something like that?
m
Yes, you do need to define a worker pool for your deployment. Worker pools in Prefect are used to manage and execute flow runs. Here's a simple guide on how to set up a worker pool: ### Creating a Worker Pool You can create a worker pool using the Prefect CLI or UI. Here's how to do it using the CLI:
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bash
prefect work-pool create <pool-name> --type docker-container
Replace 
<pool-name>
with a unique name for your worker pool. The 
--type
option specifies the type of infrastructure; in this case, we're using 
docker-container
. ### Updating Your Deployment Configuration Once you have your worker pool, update your deployment YAML to include the worker pool name:
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yaml
name: file-reader-deployment
flow: file_reader_flow.py
infrastructure:
  type: docker-container
  image: prefecthq/prefect:3.0.0rc2-python3.12
  volumes:
    - /local/path:/app
work_pool:
  name: <pool-name>
### Starting a Worker Ensure that a worker is running and polling your worker pool:
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bash
prefect worker start <pool-name>
This will start a worker that listens for flow runs assigned to the specified worker pool. For more information, you can refer to the Prefect documentation on work pools.
n
@Marvin so I don't need a docker compose file then?
m
You don't necessarily need a Docker Compose file if you're using Prefect's built-in Docker infrastructure capabilities. Prefect can manage Docker containers directly through its deployment and worker pool configurations. Here's how it works: 1. Prefect's Docker Infrastructure: Prefect allows you to specify Docker infrastructure settings directly in your deployment configuration. This includes specifying the Docker image, volume mounts, and other Docker-related settings. 2. Worker Pools: By configuring a worker pool with the 
docker-container
type, Prefect will handle the creation and management of Docker containers for executing your flow runs. 3. Deployment YAML: You can specify all necessary Docker configurations within the deployment YAML file, including the image and volumes. Here's a quick recap of the deployment configuration without needing a Docker Compose file:
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yaml
name: file-reader-deployment
flow: file_reader_flow.py
infrastructure:
  type: docker-container
  image: prefecthq/prefect:3.0.0rc2-python3.12
  volumes:
    - /local/path:/app
work_pool:
  name: <pool-name>
By using this setup, Prefect will manage the Docker containers for you, making it unnecessary to use a separate Docker Compose file unless you have other services or dependencies that need to be orchestrated outside of Prefect's capabilities.
n
@Marvin okay got it, how do my flows dependencies get loaded into the image?
m
To include your flow's dependencies in the Docker image used by Prefect, you can follow these steps: 1. Use a 
requirements.txt
File: Prefect's deployment process can automatically include dependencies specified in a 
requirements.txt
file. Place this file in the same directory as your flow script. 2. Custom Dockerfile: Prefect generates a Dockerfile that copies the current directory into the Docker image and installs any dependencies from your 
requirements.txt
. If you need more control, you can create a custom Dockerfile. 3. Custom Docker Image: Use the 
DockerImage
class to specify a custom Dockerfile if needed. This allows you to customize the image further, such as installing private packages or additional system dependencies. 4. Deploy with Prefect: When you deploy your flow, Prefect will build the Docker image using the specified Dockerfile and include the dependencies. Here's a simple example of a 
requirements.txt
and a custom Dockerfile: requirements.txt
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pandas
numpy
Dockerfile
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dockerfile
FROM prefecthq/prefect:3.0.0rc2-python3.12

# Copy the current directory contents into the container
COPY . /app

# Set the working directory
WORKDIR /app

# Install any needed packages specified in requirements.txt
RUN pip install --no-cache-dir -r requirements.txt
Deployment YAML
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yaml
name: file-reader-deployment
flow: file_reader_flow.py
infrastructure:
  type: docker-container
  image: prefecthq/prefect:3.0.0rc2-python3.12
  volumes:
    - /local/path:/app
work_pool:
  name: <pool-name>
For more details, you can refer to Prefect's Docker Infrastructure Examples.
n
@Marvin show me how to do step 3
m
To use the 
DockerImage
class for specifying a custom Dockerfile in your Prefect deployment, you can follow this example: ### Example Usage of 
DockerImage
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python
from prefect.deployments import Deployment
from prefect.docker.docker_image import DockerImage
from prefect import flow, task

@task
def read_file(file_path):
    with open(file_path, 'r') as file:
        data = file.read()
    return data

@flow
def file_reader_flow(file_path):
    content = read_file(file_path)
    print(content)

# Specify the custom Docker image
docker_image = DockerImage(
    name="my-custom-image",
    dockerfile="Dockerfile",  # Path to your Dockerfile
    build_kwargs={"tag": "my-custom-image:latest"}
)

# Create a deployment with the custom Docker image
deployment = Deployment.build_from_flow(
    flow=file_reader_flow,
    name="file-reader-deployment",
    infrastructure=docker_image,
    work_pool_name="<pool-name>"
)

if __name__ == "__main__":
    deployment.apply()
### Explanation - DockerImage Parameters: - `name`: The name of your Docker image. - `dockerfile`: Path to your custom Dockerfile. - `build_kwargs`: Additional keyword arguments for the build process, such as tagging the image. - Deployment: Use 
Deployment.build_from_flow()
to create a deployment and specify the 
docker_image
as the infrastructure. This setup allows you to customize your Docker image by specifying a Dockerfile that includes all necessary dependencies and configurations. Adjust the paths and names according to your project structure.
In Prefect 3.x, the 
flow.deploy()
method is used instead of 
Deployment.build_from_flow
. Here's how you can create a deployment using `flow.deploy()`: ### Example Deployment with 
flow.deploy()
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python
from prefect import flow, task
from prefect.docker.docker_image import DockerImage

@task
def read_file(file_path):
    with open(file_path, 'r') as file:
        data = file.read()
    return data

@flow
def file_reader_flow(file_path):
    content = read_file(file_path)
    print(content)

if __name__ == "__main__":
    # Define the DockerImage with your custom Dockerfile
    docker_image = DockerImage(
        name="my-custom-image",
        dockerfile="Dockerfile",  # Path to your Dockerfile
        build_kwargs={"tag": "my-custom-image:latest"}
    )

    # Deploy the flow with the specified Docker infrastructure
    file_reader_flow.deploy(
        name="file-reader-deployment",
        infrastructure=docker_image,
        work_pool_name="<pool-name>"
    )
### Key Points - 
DockerImage
: Specify your custom Dockerfile and any build arguments. - 
flow.deploy()
: Use this method to deploy your flow, specifying the infrastructure and work pool. This setup allows you to deploy your Prefect flow using a custom Docker image, ensuring all dependencies and configurations are included. Adjust the Dockerfile path and work pool name as needed for your setup.
Apologies for the oversight. In Prefect 3.x, you should use a 
prefect.yaml
file to define your deployment configuration, and then use the Prefect CLI to deploy your flow. Here's how you can do it: ### Step-by-Step Deployment with Prefect 3.x 1. Create a 
prefect.yaml
File: This file will define your deployment configuration, including the Docker image and other settings. prefect.yaml
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yaml
name: file-reader-deployment
description: A deployment for the file reader flow
flow: file_reader_flow.py
entrypoint: file_reader_flow.py:file_reader_flow
infrastructure:
  type: docker-container
  image: my-custom-image:latest
  volumes:
    - /local/path:/app
work_pool:
  name: <pool-name>
2. Build Your Docker Image: Use your Dockerfile to build the Docker image.
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bash
docker build -t my-custom-image:latest .
3. Deploy Using the CLI: Use the Prefect CLI to deploy your flow.
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bash
prefect deploy --name file-reader-deployment
### Explanation - 
prefect.yaml
: This file specifies the deployment configuration, including the Docker image and work pool. - Docker Image: Build your Docker image using your custom Dockerfile. - Prefect CLI: Deploy the flow using the CLI, which will use the configuration specified in 
prefect.yaml
. This approach allows you to manage and deploy your Prefect flows using a predefined configuration file, making it easier to version control and automate deployments.
In Prefect 3.x, the 
build_from_flow
method is not part of the top-level API. Instead, Prefect 3.x encourages the use of a 
prefect.yaml
configuration file for managing deployments, alongside the Prefect CLI for deploying flows. The recommended approach is to define your deployment configuration in a 
prefect.yaml
file and use the CLI to deploy, as previously described. This method provides more flexibility and aligns with Prefect's infrastructure-as-code approach. If you have specific deployment requirements or configurations, the 
prefect.yaml
file and CLI commands are the way to go.
When the deployment process is stuck on "Scanning for flows...", it could be due to a few reasons. Here are some steps you can take to troubleshoot and resolve the issue: 1. Check the 
entrypoint
Path: Ensure that the 
entrypoint
specified in your 
prefect.yaml
file is correct and points to the Python file and function where your flow is defined. It should be in the format 
path/to/your_file.py:flow_function_name
. 2. Directory Structure: Verify that your flow script is located in the expected directory. If the script is not found, Prefect might be unable to locate the flow definition. 3. Python Environment: Ensure that your Python environment is correctly set up and that all necessary dependencies are installed. Missing dependencies can sometimes cause scanning issues. 4. Permissions: Check if there are any permission issues preventing Prefect from reading the flow files. Ensure that the user running the Prefect CLI has the necessary permissions. 5. Verbose Logging: Use verbose logging to get more information about what might be going wrong. You can enable verbose logging by setting the environment variable 
PREFECT_LOGGING_LEVEL=DEBUG
. 6. Prefect CLI Version: Make sure you are using the latest version of Prefect CLI, as updates often include bug fixes and improvements. If these steps do not resolve the issue, you might want to check for any open issues or discussions in the Prefect GitHub repository or reach out to the Prefect community for additional support.
n
@Marvin I've deployed it! however, now I'm seeing this 
FileNotFoundError: [Errno 2] No such file or directory: '/opt/prefect/mini_interp'
my dockerfile definitely copies files there and sets that as the workdir. what's going?
m
The 
FileNotFoundError
you're encountering suggests that the file or directory 
/opt/prefect/mini_interp
is not present in the Docker container at runtime, even though your Dockerfile is supposed to copy it there. Here are some potential causes and solutions: 1. Verify the Dockerfile: Double-check your Dockerfile to ensure that the 
COPY
command is correctly specified and that the source path is correct relative to the Docker build context.
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dockerfile
   FROM prefecthq/prefect:3.0.0rc2-python3.12
   WORKDIR /opt/prefect
   COPY ./mini_interp /opt/prefect/mini_interp
2. Build Context: Make sure that the 
mini_interp
file or directory is within the Docker build context. The build context is the directory where you run the 
docker build
command. If 
mini_interp
is outside this directory, Docker won't be able to copy it. 3. Rebuild the Image: If you've made changes to the Dockerfile or the files being copied, ensure you rebuild the Docker image:
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bash
   docker build -t my-custom-image:latest .
4. Check Docker Ignore Files: Ensure there's no 
.dockerignore
file excluding the 
mini_interp
directory from the build context. 5. Inspect the Image: After building the image, you can run a container interactively to verify the presence of the file:
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bash
   docker run -it --rm my-custom-image:latest /bin/sh
Once inside the container, navigate to 
/opt/prefect
and check if 
mini_interp
exists. 6. Volume Mounts: Ensure that no volume mounts are overwriting the 
/opt/prefect
directory, which might cause the copied files to disappear at runtime. By following these steps, you should be able to diagnose and fix the issue with the missing directory or file in your Docker container.
The 
FileNotFoundError
you're encountering indicates that the directory or file 
/opt/prefect/mini_interp
is not present in the Docker container at runtime, despite your Dockerfile setup. Here are some steps to troubleshoot and resolve the issue: 1. Check Dockerfile Syntax: Ensure your Dockerfile correctly specifies the 
COPY
and 
WORKDIR
commands. Here's a basic example:
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dockerfile
   FROM prefecthq/prefect:3.0.0rc2-python3.12
   WORKDIR /opt/prefect
   COPY ./mini_interp /opt/prefect/mini_interp
2. Build Context: Verify that the 
mini_interp
directory is within the Docker build context. The build context is the directory you specify when running 
docker build
. Ensure you are running the build command from the correct directory. 3. Rebuild the Docker Image: If changes were made to the Dockerfile or the files, rebuild the Docker image to ensure the changes are applied:
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bash
   docker build -t my-custom-image:latest .
4. Inspect the Image: After building the image, run a container interactively to verify the presence of the files:
Copy code
bash
   docker run -it --rm my-custom-image:latest /bin/sh
Inside the container, navigate to 
/opt/prefect
and check if 
mini_interp
exists. 5. Volume Mounts: Ensure that no volume mounts are overwriting the 
/opt/prefect
directory when the container runs, as this could cause the copied files to be hidden or replaced. 6. Docker Ignore Files: Check if there is a 
.dockerignore
file excluding the 
mini_interp
directory from the build context. By following these steps, you should be able to pinpoint the issue with the missing directory or file in your Docker container. If the problem persists, it may be helpful to review the Docker build logs for any errors or warnings related to file copying.
n
@Marvin I've tried all those there are no volume mounts and when I exec into the container the directory does exist. I'm very confused
m
If the directory exists when you manually check inside the container, but you're still encountering a 
FileNotFoundError
during runtime, it suggests a possible issue with the path or timing of when the path is accessed. Here are a few things to consider: 1. Path Consistency: Double-check the exact path your code is using to access the directory. Ensure there are no typos or differences in case sensitivity, as these can cause issues in some environments. 2. Working Directory: Confirm that the working directory is correctly set in your Dockerfile and that your code is executing with the expected current working directory. You can add debug prints in your code to log the current working directory and verify it matches your expectations. 3. File Access Timing: If your flow or task tries to access the directory before it is fully set up, it might cause this error. Ensure that any setup code (such as copying files) is completed before the directory is accessed. 4. Environment Variables: Check if there are any environment variables affecting the path resolution in your code. These could inadvertently change the path being accessed. 5. Prefect Task Isolation: If Prefect tasks are running in isolated environments, ensure they have access to the file system as expected. Prefect might be running tasks in a way that impacts file access. 6. Log Additional Information: Add logging to your flow or tasks to capture the state of the file system when the error occurs. This can provide insights into what might be going wrong. If these steps don't resolve the issue, consider sharing more details about how your flow accesses the directory. This might help pinpoint specific areas to investigate further.
n
@Marvin Is there no way I can just update this to mount some local volumes?
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deployment = make_climate_tile.deploy(
        name="test-deployment",
        work_pool_name="nick-test-work-pool",
        image="make-image-py:dev",
        push=False
    )
m
To mount local volumes in your Prefect deployment using the 
docker-container
infrastructure, you can specify the volume mounts directly in your deployment configuration. Here's how you can modify your deployment to include volume mounts: ### Example Deployment with Volume Mounts
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python
from prefect import flow, task

@task
def process_data():
    # Example task that uses a file from the mounted volume
    with open('/app/data/input.txt', 'r') as f:
        data = f.read()
    print(data)

@flow
def make_climate_tile():
    process_data()

if __name__ == "__main__":
    # Deploy the flow with volume mounts
    deployment = make_climate_tile.deploy(
        name="test-deployment",
        work_pool_name="nick-test-work-pool",
        image="make-image-py:dev",
        push=False,
        infrastructure={
            "type": "docker-container",
            "volumes": ["/local/path:/app"]
        }
    )
### Key Points - 
volumes
: In the 
infrastructure
configuration, specify the volumes you want to mount. The format is 
["/local/path:/container/path"]
, where 
/local/path
is the path on your host machine, and 
/container/path
is the path inside the container. - Infrastructure Configuration: Ensure that the 
infrastructure
parameter is set to use a 
docker-container
with the specified image and volume mounts. This setup will allow your Prefect flow to access files on the local file system of the machine running the Docker container, making it easier to manage data dependencies. Adjust the paths as necessary for your specific use case.
n
@Marvin that gives this error 
TypeError: Flow.deploy() got an unexpected keyword argument 'infrastructure'
m
It seems that the 
flow.deploy
method in Prefect 3.x doesn't support the 
infrastructure
keyword directly. Instead, you can define your deployment configuration using a 
prefect.yaml
file, which allows you to specify infrastructure settings such as volume mounts. Here's how you can achieve this: ### Step-by-Step Deployment with Volume Mounts 1. Create a 
prefect.yaml
File: Define your deployment configuration, including the Docker image and volume mounts. prefect.yaml
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yaml
name: test-deployment
description: A deployment for the make_climate_tile flow
flow: file_reader_flow.py
entrypoint: file_reader_flow.py:make_climate_tile
infrastructure:
  type: docker-container
  image: make-image-py:dev
  volumes:
    - /local/path:/app
work_pool:
  name: nick-test-work-pool
2. Build Your Docker Image: Ensure your Docker image is built with the necessary dependencies.
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bash
docker build -t make-image-py:dev .
3. Deploy Using the CLI: Use the Prefect CLI to deploy your flow.
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bash
prefect deploy --name test-deployment
### Explanation - 
prefect.yaml
: This file specifies the deployment configuration, including the Docker image and volume mounts. - Prefect CLI: Deploy the flow using the CLI, which will use the configuration specified in 
prefect.yaml
. This approach allows you to manage and deploy your Prefect flows using a predefined configuration file, making it easier to version control and automate deployments. Adjust the paths and names according to your project structure.
n
@Marvin For some reason, if I deploy using flow.deploy() I don't get this issue 
prefect.deployments.steps.core.StepExecutionError: Encountered error while running prefect.deployments.steps.set_working_directory
but when I use a deploy yaml that appears identical I do get the issue
m
The 
StepExecutionError
related to 
set_working_directory
suggests an issue with the directory setup during deployment. Here are some possible reasons and solutions: 1. Directory Path: Ensure the directory path specified in your deployment YAML is correct and matches the expected structure in your Docker image. 2. Permissions: Check if there are any permission issues that might prevent Prefect from setting the working directory. Ensure the user running the Prefect process has the necessary permissions. 3. Environment Consistency: Confirm that the environment (e.g., file paths, environment variables) is consistent between your local setup and the deployment environment. Differences can lead to unexpected errors. 4. Deployment YAML Specifics: Review the 
prefect.yaml
for any discrepancies. Ensure that the 
set_working_directory
step is correctly configured. 5. Debugging with Logs: Utilize Prefect's logging to get more detailed error messages. This can help identify the exact step or configuration causing the issue. 6. Prefect Version: Ensure you are using a compatible version of Prefect for your deployment setup. Sometimes, newer versions might have bug fixes or changes that could affect deployment behavior. If the problem persists, consider reaching out to the Prefect community or support for more targeted assistance, as they can provide insights based on similar issues encountered by other users.
n
@Marvin are you sure you specify infrastructure in the deployment yaml?
m
In Prefect 3.x, the deployment YAML file, typically named 
prefect.yaml
, is used to define deployment configurations, including infrastructure settings. Here's an example of how you might specify infrastructure in a 
prefect.yaml
file: ### Example 
prefect.yaml
with Infrastructure Specification
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yaml
name: test-deployment
description: A deployment for the make_climate_tile flow
flow: file_reader_flow.py
entrypoint: file_reader_flow.py:make_climate_tile
work_pool:
  name: nick-test-work-pool
infrastructure:
  type: docker-container
  image: make-image-py:dev
  volumes:
    - /local/path:/app
### Key Sections - 
work_pool
: Specifies the work pool to use for this deployment. - 
infrastructure
: Defines the infrastructure type and settings, such as Docker image and volume mounts. The 
infrastructure
section allows you to specify details about the environment in which your flow runs, including Docker configurations. This setup is part of the 
prefect.yaml
file and is used when you deploy your flow using the Prefect CLI. If you encounter issues with the 
prefect.yaml
deployment, ensure that the structure and paths are correctly specified, as discrepancies can lead to errors during deployment.
n
I can't see any mention of infrastrcture in the docs, I think this field is being ignored and its not using the right docker container @Marvin
m
I apologize for the confusion. It seems there might be a misunderstanding regarding the use of infrastructure in the 
prefect.yaml
file for Prefect 3.x. The infrastructure configuration is typically managed through work pools and job templates rather than directly in the YAML file as a separate infrastructure section. ### Correct Approach for Specifying Infrastructure 1. Work Pools and Job Templates: Prefect 3.x uses work pools and job templates to define the infrastructure settings. You can specify these settings in the Prefect UI or through the CLI when creating or updating work pools. 2. Job Variables: Use 
job_variables
in the 
prefect.yaml
file to override default settings in the work pool's job template. This is where you can specify details like the Docker image. ### Example 
prefect.yaml
with Job Variables
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yaml
name: test-deployment
description: A deployment for the make_climate_tile flow
flow: file_reader_flow.py
entrypoint: file_reader_flow.py:make_climate_tile
work_pool:
  name: nick-test-work-pool
  job_variables:
    image: make-image-py:dev
    volumes:
      - /local/path:/app
### Steps to Ensure Correct Deployment - Define Work Pool: Ensure your work pool is configured with the correct base job template that includes Docker settings. - Use Job Variables: Override specific settings like the Docker image in the 
job_variables
section of the 
prefect.yaml
. - Deploy with CLI: Use the Prefect CLI to deploy, ensuring it uses the configurations specified in your YAML. By using job variables and work pools, you can effectively manage the infrastructure settings for your Prefect deployments. If this doesn't resolve the issue, you might want to check the Prefect documentation for any updates or reach out to the Prefect community for further guidance.
n
@Marvin where I can see docs on the work pool job variables?
m
You can find information about work pool job variables in the Prefect documentation under the infrastructure concepts for work pools. Here's a relevant excerpt and a link to the documentation: ### Work Pool Job Variables - Work pools in Prefect allow you to configure and monitor infrastructure settings. They enable you to override default settings on a per-deployment or per-flow run basis using job variables. - These job variables can be specified in the 
deployments.work_pool.job_variables
section of a 
prefect.yaml
file or in the 
job_variables
argument of a Python 
flow.deploy
method. For more detailed information, you can visit the Prefect documentation on work pools. This documentation provides insights into how you can customize job templates and variables for your specific deployment needs, offering flexibility in how your flows are executed within different infrastructure environments.
n
@Marvin okay now I'm getting
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docker.errors.ImageNotFound: 404 Client Error for <http+docker://localhost/v1.45/images/create?tag=latest&fromImage=make-tile-image>: Not Found ("pull access denied for make-tile-image, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")
I'm not pushing this image anywhere but it IS available locally, not sure why this can't find it?
m
The error you're encountering indicates that the Docker image 
make-tile-image
is not being found when Prefect tries to pull it, even though it exists locally. Here are some potential reasons and solutions: 1. Local Image Not Recognized: Ensure that the image is built and tagged correctly. You can check if the image exists locally by running:
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bash
   docker images
2. Correct Tagging: Make sure the image is tagged exactly as you're referencing it in your deployment. For example:
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bash
   docker build -t make-tile-image:latest .
3. Use Local Docker Daemon: Ensure that Prefect is using the local Docker daemon. You might need to configure Prefect or Docker to ensure they're using the same environment. 4. Docker Context: Confirm that the Prefect worker or agent running the flow has access to the local Docker environment. If the worker is running in a different environment (e.g., a remote server), it won't have access to your local Docker images. 5. Docker Login: If there's any chance you're using a private registry or authentication is required, ensure you're logged in:
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bash
   docker login
6. Image Pull Policy: Check if there's a pull policy set that might be forcing a pull from a registry. Ensure that the configuration allows for local images. If you're running Prefect in a containerized environment, ensure that the Docker socket is correctly mounted and accessible to the Prefect container, allowing it to interact with your local Docker daemon. Adjust your setup accordingly to ensure Prefect can access the locally available Docker images.
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