@Marvin When I use a custom Docker image to deploy my prefect flows using ECS push work pools, I get the following error when I try to run the deployment:

Flow could not be retrieved from deployment.
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/site-packages/prefect/engine.py", line 427, in retrieve_flow_then_begin_flow_run
    else await load_flow_from_flow_run(
  File "/usr/local/lib/python3.10/site-packages/prefect/client/utilities.py", line 100, in with_injected_client
    return await fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/site-packages/prefect/deployments/deployments.py", line 290, in load_flow_from_flow_run
    basepath = deployment.path or Path(deployment.manifest_path).parent
  File "/usr/local/lib/python3.10/pathlib.py", line 960, in __new__
    self = cls._from_parts(args)
  File "/usr/local/lib/python3.10/pathlib.py", line 594, in _from_parts
    drv, root, parts = self._parse_args(args)
  File "/usr/local/lib/python3.10/pathlib.py", line 578, in _parse_args
    a = os.fspath(a)
TypeError: expected str, bytes or os.PathLike object, not NoneType

My Dockerfile is as such:

FROM prefecthq/prefect:2-python3.10

# Copy all code
COPY . /opt/prefect

# Set working directory
WORKDIR /opt/prefect

If I set my build_docker_image step in my

prefect.yaml

to be

auto

, however, I can successfully run my deployment. What are the differences that arise from these 2 approaches such that it causes the error above?

Your message was too long by 95 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:

When I use a custom Docker image to deploy my prefect flows using ECS push work pools, I get the following error when I try to run the deployment:

Flow could not be retrieved from deployment.
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/site-packages/prefect/engine.py", line 427, in retrieve_flow_then_begin_flow_run
    else await load_flow_from_flow_run(
  File "/usr/local/lib/python3.10/site-packages/prefect/client/utilities.py", line 100, in with_injected_client
    return await fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/site-packages/prefect/deployments/deployments.py", line 290, in load_flow_from_flow_run
    basepath = deployment.path or Path(deployment.manifest_path).parent
  File "/usr/local/lib/python3.10/pathlib.py", line 960, in __new__
    self = cls._from_parts(args)
  File "/usr/local/lib/python3.10/pathlib.py", line 594, in _from_parts
    drv, root, parts = self._parse_args(args)
  File "/usr/local/lib/python3.10/pathlib.py", line 578, in _parse_args
    a = os.fspath(a)
TypeError: expected str, bytes or os.PathLike object, not NoneType

My Dockerfile

@Marvin What are the differences that arise from setting

Dockerfile

in my

build_docker_image

step of my

prefect.yaml

to be

auto

vs setting it to use a custom Docker image to deploy my prefect flows using ECS push work pools. My Dockerfile is as such:

FROM prefecthq/prefect:2-python3.10

# Copy all code
COPY . /opt/prefect

# Set working directory
WORKDIR /opt/prefect

I get an error when running my deployment using the custom Docker image but not when using the auto generated Docker image

Setting the

Dockerfile

to

auto

utilizes a temporary Dockerfile created by Prefect to build the image. When the

Dockerfile

is set to a path, Prefect uses your custom Dockerfile. Here are the key differences: 1. Flexibility and Control: - Custom: Allows you to control the specifics of the image, including dependencies and configurations. - Auto: Prefect handles the creation of the Dockerfile and determines the necessary image setup. 2. Sources of Error: - Custom: Errors may arise from misconfigurations or missing components in your Dockerfile. - Auto: Prefect ensures all necessary components are included, reducing setup errors but limiting customization. 3. Deployment: - Custom: If your deployment fails with a custom Dockerfile, it could be due to specifics in your setup. - Auto: If deployment works, it suggests that the Prefect default configuration may include necessary steps that your custom Dockerfile may be missing. Make sure you have Docker running locally and review Prefect's Docker Deployment Guide for details on building Docker images, and check the Push Work Pools Guide for ECS work pools. If you're encountering specific errors with your custom image, you might glean valuable insights from Prefect's issues page on GitHub, specifically focusing on image-building concerns like setup, environment variables, and module installations.

@Marvin Can I integrate my python dependencies into the image that gets built by the temporary Dockerfile?

Yes, you can integrate your Python dependencies into the Docker image built by the temporary Dockerfile. This can be achieved by specifying the dependencies in a

requirements.txt

file, and then feeding this into the Docker build process using build arguments or specifying it within the Prefect configuration. Here is a step-by-step guide to achieve this: ### Step 1: Create a

requirements.txt

File List all your dependencies in a

requirements.txt

file in your project directory. For example:

pandas
numpy

### Step 2: Update your

prefect.yaml

In your

prefect.yaml

file, you need to specify the path to your

requirements.txt

file and adjust the

build_docker_image

step to include these dependencies. ### Using the

build_kwargs

with the

auto

Dockerfile: Make sure to use the

build_kwargs

to pass the

requirements.txt

to the build process.

yaml
build:
  - prefect_docker.deployments.steps.build_docker_image:
      requires: prefect-docker
      image_name: "your-image-name"
      tag: "latest"
      dockerfile: auto
      build_kwargs:
        buildargs:
          REQUIREMENTS_FILE: "requirements.txt"

### Step 3: Customize Docker Build The Prefect's auto Dockerfile needs to be instructed to use the

requirements.txt

. Unfortunately, directly modifying the auto-generated Dockerfile is not straightforward. You could create a custom Prefect function to append this during the build process. ### Example Prefect Function for Custom Dockerfile Content Create a custom Prefect build step function to include your `requirements.txt`:

python
from prefect_docker.deployments.steps import build_docker_image

def build_custom_docker_image():
    return build_docker_image(
        image_name="your-image-name",
        tag="latest",
        dockerfile="auto",
        build_kwargs={
            "buildargs": {"REQUIREMENTS_FILE": "requirements.txt"}
        },
        commands=[
            "COPY requirements.txt /tmp/",
            "RUN pip install -r /tmp/requirements.txt"
        ]
    )

Using this function within your `prefect.yaml`:

yaml
build:
  - your_module.build_custom_docker_image

### Step 4: Run Deployment Finally, run the deployment as usual:

sh
prefect deployment build your-flow.py:your_flow -n "your-deployment-name" -t "latest"
prefect deployment apply your-deployment.yaml

For more detailed information, refer to the Prefect Docker Integration Guide and relevant GitHub Issues on dependency handling. This approach ensures your dependencies are included in the built Docker image, streamlining deployment and execution within your ECS push work pools.

@Marvin give me a sample of a prefect.yaml that has the following: 1. deployment with 1 parameter and 1 schedule 2. deployment with 3 parameters and 3 schedules

Here is a sample

prefect.yaml

file with two deployments: - Deployment 1 has 1 parameter and 1 schedule. - Deployment 2 has 3 parameters and 3 schedules.

yaml
name: my-flow
description: An example flow with deployments

entrypoint: flows/my_flow.py:my_flow

deployments:
  - name: single-parameter-and-schedule
    version: 1
    parameters:
      param1: value1
    schedule:
      interval: "0 9 * * *" # Daily at 9 AM

  - name: multiple-parameters-and-schedules
    version: 1
    parameters:
      param1: value1
      param2: value2
      param3: value3
    schedule: 
      - interval: "0 9 * * *"  # Daily at 9 AM
      - cron: "0 12 * * MON"  # Every Monday at 12 PM
      - interval: "0/30 * * * *"  # Every 30 minutes
      
infrastructure:
  - type: docker
    url: yes
  - type: docker
    url: no    

  - name: both-parameters-and-schedule
    parameters:
      param1: Hello
    schedule:
      cron: 0 0 * * 0
    labels:
      - dev
    infrastructure:
      type: local-process
      env:
        PREFECT_LOGGING_LEVEL: "ERROR"

Ensure that: - The

entrypoint

points to the correct path for your flow. - The

schedule

section uses appropriate intervals and cron expressions as needed. Refer to the Prefect documentation for more details. You can use this as a template and adjust the parameters and schedules to fit your flow requirements.

@Marvin amend this to use only cron for the schedules with timezone set to Asia/Singapore

Here's the amended

prefect.yaml

file with all schedules using cron expressions and the timezone set to Asia/Singapore:

yaml
name: my-flow
description: An example flow with deployments

entrypoint: flows/my_flow.py:my_flow

deployments:
  - name: single-parameter-and-schedule
    version: 1
    parameters:
      param1: value1
    schedule:
      cron: "0 9 * * *"  # Daily at 9 AM
      timezone: "Asia/Singapore"

  - name: multiple-parameters-and-schedules
    version: 1
    parameters:
      param1: value1
      param2: value2
      param3: value3
    schedule:
      - cron: "0 9 * * *"  # Daily at 9 AM
        timezone: "Asia/Singapore"
      - cron: "0 12 * * 1"  # Every Monday at 12 PM
        timezone: "Asia/Singapore"
      - cron: "*/30 * * * *"  # Every 30 minutes
        timezone: "Asia/Singapore"

infrastructure:
  - type: docker
    url: "yes"
  - type: docker
    url: "no"

  - name: both-parameters-and-schedule
    parameters:
      param1: Hello
    schedule:
      cron: "0 0 * * 0"  # Every Sunday at 12 AM
      timezone: "Asia/Singapore"
    labels:
      - dev
    infrastructure:
      type: local-process
      env:
        PREFECT_LOGGING_LEVEL: "ERROR"

### Key Changes: - All schedules use cron expressions. - The

timezone

field is set to

Asia/Singapore

for each schedule. This configuration ensures that all schedules use cron expressions and are aligned with the Asia/Singapore timezone. Adjust the cron expressions as needed for your specific use case.

@Marvin Add on a third deployment that passes cpu and memory override to my ecs push work pool

Here's the updated

prefect.yaml

file with a third deployment that includes CPU and memory overrides for the ECS push work pool:

yaml
name: my-flow
description: An example flow with deployments

entrypoint: flows/my_flow.py:my_flow

deployments:
  - name: single-parameter-and-schedule
    version: 1
    parameters:
      param1: value1
    schedule:
      cron: "0 9 * * *"  # Daily at 9 AM
      timezone: "Asia/Singapore"

  - name: multiple-parameters-and-schedules
    version: 1
    parameters:
      param1: value1
      param2: value2
      param3: value3
    schedule:
      - cron: "0 9 * * *"  # Daily at 9 AM
        timezone: "Asia/Singapore"
      - cron: "0 12 * * 1"  # Every Monday at 12 PM
        timezone: "Asia/Singapore"
      - cron: "*/30 * * * *"  # Every 30 minutes
        timezone: "Asia/Singapore"

  - name: ecs-deployment-with-overrides
    version: 1
    parameters:
      param1: value1
      param2: value2
    schedule:
      - cron: "0 0 * * SUN"  # Every Sunday at 12 AM
        timezone: "Asia/Singapore"
    infrastructure:
      type: ecs
      overrides:
        cpu: 512
        memory: 2048

infrastructure:
  - type: docker
    url: "yes"
  - type: docker
    url: "no"

  - name: both-parameters-and-schedule
    parameters:
      param1: Hello
    schedule:
      cron: "0 0 * * SUN"  # Every Sunday at 12 AM
      timezone: "Asia/Singapore"
    labels:
      - dev
    infrastructure:
      type: local-process
      env:
        PREFECT_LOGGING_LEVEL: "ERROR"

### Key Additions: - Third Deployment:

ecs-deployment-with-overrides

- Parameters:

param1

and

param2

- Schedule: Cron expression set for every Sunday at 12 AM, with timezone specified as

Asia/Singapore

. - ECS Specific Overrides: - CPU: 512 - Memory: 2048 ### Relevant Documentation Links: - Prefect Work Pools Documentation - Prefect AWS ECS Guide - Prefect Push Work Pools Guide This configuration ensures that the third deployment is tailored to utilize ECS with specific CPU and memory requirements. Adjust the parameters and schedules as needed for your specific use case.

@Marvin is it possible to set different parameter values for different schedules in a single deployment?

No, within a single deployment in Prefect, you cannot set different parameter values for different schedules. Each deployment has a set of parameters that remains constant across all the schedules associated with that deployment. If you need to run the same flow with different parameter values on different schedules, you will need to create separate deployments for each set of parameters and corresponding schedules. ### Separate Deployments for Different Parameter Values and Schedules Here's an example of how to structure

prefect.yaml

to handle different parameter values for different schedules by using separate deployments:

yaml
name: my-flow
description: An example flow with deployments

entrypoint: flows/my_flow.py:my_flow

deployments:
  - name: deployment-1
    version: 1
    parameters:
      param1: value1
      param2: value2
    schedule:
      cron: "0 9 * * *"  # Daily at 9 AM
      timezone: "Asia/Singapore"

  - name: deployment-2
    version: 1
    parameters:
      param1: value3
      param2: value4
    schedule:
      cron: "0 12 * * 1"  # Every Monday at 12 PM
      timezone: "Asia/Singapore"

  - name: deployment-3
    version: 1
    parameters:
      param1: value5
      param2: value6
    schedule:
      cron: "*/30 * * * *"  # Every 30 minutes
      timezone: "Asia/Singapore"

infrastructure:
  - type: docker
    url: "yes"
  - type: docker
    url: "no"

  - name: ecs-deployment
    parameters:
      param1: value1
      param2: value2
    schedule:
      cron: "0 0 * * SUN"  # Every Sunday at 12 AM
      timezone: "Asia/Singapore"
    infrastructure:
      type: ecs
      overrides:
        cpu: 512
        memory: 2048

### Key Points: - Separate Deployments: Each deployment (

deployment-1

,

deployment-2

,

deployment-3

) has its own set of parameter values and schedules. - Individual Schedules: Each deployment has its own schedule. This approach ensures that you can manage different parameter sets and associated schedules effectively. For more details, refer to the Prefect documentation on deployments.