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# ask-marvin
f
@Marvin I'm using Prefect 3 and trying to deploy my first flow on an ECS fargate push work pool created using the prefect cli. I'm baking the flow and task code directly into a docker image stored on a private ECR registry. Below is my prefect.yaml file where the image name and tag are properly populated (it is stored in ecr).
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name: test
prefect-version: 3.1.6

deployments:
- name: test_flow_x_hourly_deployment
  version: "1"
  tags: null
  description: This deployment runs flow x hourly
  schedules:
    - cron: "0 0 * * *"
      timezone: "Europe/Paris"
      active: "true"
  flow_name: flow_1
  entrypoint: src/projects/test/prefect/flows/flow_1.py:sample_flow
  parameters:
    input_data: "Hello, World!"
  work_pool:
    name: ecs-fargate-workpool
    work_queue_name: default
    job_variables:
      image: '{{ $IMAGE_NAME }}:{{ $IMAGE_TAG }}'
Below is my dockerfile from which the image is built
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FROM prefecthq/prefect:3.1.6-python3.11

ARG PROJECT_PATH

WORKDIR /app

RUN pip install uv

COPY uv.lock .
COPY pyproject.toml .

RUN uv sync

COPY src/common src/common
COPY ${PROJECT_PATH} ${PROJECT_PATH}

ENV PATH=".venv/bin:$PATH"

ENTRYPOINT []
My flow crashes with the error
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Flow run infrastructure exited with non-zero status code:
 Exited with non 0 code. (Error Code: 1)
This may be caused by attempting to run an image with a misspecified platform or architecture.
What could I be missing?
m
Your message was too long by 79 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
I'm using Prefect 3 and trying to deploy my first flow on an ECS fargate push work pool created using the prefect cli.
I'm baking the flow and task code directly into a docker image stored on a private ECR registry. Below is my prefect.yaml file where the image name and tag are properly populated (it is stored in ecr).
Copy code
name: test
prefect-version: 3.1.6

deployments:
- name: test_flow_x_hourly_deployment
  version: "1"
  tags: null
  description: This deployment runs flow x hourly
  schedules:
    - cron: "0 0 * * *"
      timezone: "Europe/Paris"
      active: "true"
  flow_name: flow_1
  entrypoint: src/projects/test/prefect/flows/flow_1.py:sample_flow
  parameters:
    input_data: "Hello, World!"
  work_pool:
    name: ecs-fargate-workpool
    work_queue_name: default
    job_variables:
      image: '{{ $IMAGE_NAME }}:{{ $IMAGE_TAG }}'
Below is my dockerfile from which the image is built ```FROM prefecthq/prefect:3.1.6-python3.11 ARG PROJECT_PATH WORKDIR /app RUN pip install uv COPY uv.lock . COPY pyproject.toml . RUN uv sync COPY src/common src/common COPY ${
Your message was too long by 45 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
I'm using Prefect 3 and deploying a flow on an ECS fargate workpool created with prefect cli.
Flow and task code are baked in a docker image stored on ECR. prefect.yaml file
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name: test
prefect-version: 3.1.6

deployments:
- name: test_flow_x_hourly_deployment
  version: "1"
  tags: null
  description: This deployment runs flow x hourly
  schedules:
    - cron: "0 0 * * *"
      timezone: "Europe/Paris"
      active: "true"
  flow_name: flow_1
  entrypoint: src/projects/test/prefect/flows/flow_1.py:sample_flow
  parameters:
    input_data: "Hello, World!"
  work_pool:
    name: ecs-fargate-workpool
    work_queue_name: default
    job_variables:
      image: '{{ $IMAGE_NAME }}:{{ $IMAGE_TAG }}'
Dockerfile from which the image is built
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FROM prefecthq/prefect:3.1.6-python3.11

ARG PROJECT_PATH

WORKDIR /app

RUN pip install uv

COPY uv.lock .
COPY pyproject.toml .

RUN uv sync

COPY src/common src/common
COPY ${PROJECT_PATH} ${PROJECT_PATH}

ENV PATH=".venv/bin:$PATH"

ENTRYPOINT []
Flow crashes with ```Flow run infrastructure exited with non
Your message was too long by 24 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
Deploying a flow on ECS workpool created with prefect cli.
Flow and task code are in docker image on ECR. prefect.yaml file
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name: test
prefect-version: 3.1.6
deployments:
- name: test_flow_x_hourly_deployment
  version: "1"
  tags: null
  description: This deployment runs flow x hourly
  schedules:
    - cron: "0 0 * * *"
      timezone: "Europe/Paris"
      active: "true"
  flow_name: flow_1
  entrypoint: src/projects/test/prefect/flows/flow_1.py:sample_flow
  parameters:
    input_data: "Hello, World!"
  work_pool:
    name: ecs-fargate-workpool
    work_queue_name: default
    job_variables:
      image: '{{ $IMAGE_NAME }}:{{ $IMAGE_TAG }}'
Dockerfile file
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FROM prefecthq/prefect:3.1.6-python3.11
ARG PROJECT_PATH
WORKDIR /app
RUN pip install uv
COPY uv.lock .
COPY pyproject.toml .
RUN uv sync
COPY src/common src/common
COPY ${PROJECT_PATH} ${PROJECT_PATH}
ENV PATH=".venv/bin:$PATH"
ENTRYPOINT []
Flow crashes with ```Flow run infrastructure exited with non-zero status code: Exited with non 0 code. (Error Code: 1
f
@Marvin Deploying a flow on ECS workpool created with prefect cli. Flow and task code are in docker image on ECR. prefect.yaml file
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name: test
prefect-version: 3.1.6
deployments:
- name: test_flow_x_hourly_deployment
  version: "1"
  flow_name: flow_1
  entrypoint: src/projects/test/prefect/flows/flow_1.py:sample_flow
  parameters:
    input_data: "Hello, World!"
  work_pool:
    name: ecs-fargate-workpool
    work_queue_name: default
    job_variables:
      image: '{{ $IMAGE_NAME }}:{{ $IMAGE_TAG }}'
Dockerfile file
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FROM prefecthq/prefect:3.1.6-python3.11
ARG PROJECT_PATH
WORKDIR /app
RUN pip install uv
COPY uv.lock .
COPY pyproject.toml .
RUN uv sync
COPY src/common src/common
COPY ${PROJECT_PATH} ${PROJECT_PATH}
ENV PATH=".venv/bin:$PATH"
ENTRYPOINT []
Flow crashes with
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Flow run infrastructure exited with non-zero status code
This may be caused by attempting to run an image with a misspecified platform or architecture.
Any advice?
m
It seems you're encountering issues with deploying a flow using ECS in Prefect 3.1.6. Let's break down some potential solutions and insights based on the documentation and typical pitfalls: 1. Platform and Architecture Mismatch: The error message you received suggests a possible mismatch between the image platform and the ECS configuration. Ensure that the ECS task definition's architecture matches the Docker image settings. If you’re using an ARM-based image but deploying to an x86 ECS cluster (or vice versa), this error might occur. 2. Docker Image Verification: Double-check that your Docker image is built with the correct architecture and is pushed properly to ECR. You can verify this by running 
docker inspect <your-image>
to ensure the architecture matches your ECS task specifications. 3. Entrypoint and CMD in Dockerfile: Make sure your 
ENTRYPOINT
and 
CMD
are correctly set. As you have an empty 
ENTRYPOINT []
, it relies heavily on Prefect setting the correct entry point and commands. Verify Prefect is passing correct configurations. 4. Common Dependency Issues: Sometimes, missing or incorrectly specified dependencies can lead to runtime errors. Ensure that all necessary Python packages are installed and available in your Docker image. 5. Checking Logs: Utilize ECS logs to get more specific errors that might help diagnose the issue. Prefect might not capture all error messages, and ECS logs can provide more detailed insights. For further reference, please see the Prefect documentation and this section on Docker infrastructure examples, which walks through common deployment practices and troubleshooting. Deployments can sometimes feel like you're hitchhiking through a galaxy of configurations—without a towel. Keep calm, and check the logs!
f
The image architecture is amd64, how can I check if the ECS task spec matches this?
@Marvin The image architecture is amd64, how can I check if the ECS task spec matches this?
m
To check if your ECS task definition matches the architecture of your Docker image (amd64), follow these steps: 1. Log in to AWS Management Console: Navigate to the Amazon ECS section. 2. Find Your Task Definition: - Go to the "Task Definitions" section in the ECS dashboard. - Locate the task definition that you're using for your Prefect deployment. 3. Review Task Definition: - Click on the specific task definition. - Look for any fields related to architecture or platform version. While older ECS configurations may not explicitly show architecture settings, newer ones might depending on what services or settings are being used (e.g., if you are using Graviton instances which are ARM-based). 4. Check ECS Cluster: - Go to the "Clusters" section. - Find and select the cluster that’s being used. - Check the "Instances" or "Infrastructure" tab to review instance types. Make sure they're compatible with amd64 architecture (x86_64). 5. EC2 Instance Type (if applicable): - If you're using EC2-backed instances for ECS, check the instance type. For example, 
t2.micro
, 
t3.medium
, etc., are compatible with x86_64 (amd64). - If the instance types are ARM-based like 
t4g.micro
or 
a1.medium
, the architecture will not be compatible. Alternatively, if you're using Fargate, double-check the platform version: - Fargate with PC2 (amd64): Default configurations should be fine for amd64. - Fargate support for ARM: Make sure you’re not using settings specific to ARM-based platforms unless intentionally. If things still seem out of place, you might want to create a new task definition with explicit architecture settings where possible, or consult the ECS Platform Versions and Compatibility documentation. If something doesn't add up and feels like it's an outer space issue, you may want to consult your friendly neighboring AWS support.
f
@Marvin I checked and this doesn't seem to be the issue. Can you tell me more about entrypoint? What value should I set in my dockerfile, how will the prefect deployment override it?
m
When dealing with Docker entrypoints and Prefect deployments, here are some important concepts to keep in mind: 1. Docker Entrypoint: An entrypoint in a Dockerfile determines what runs as the container starts. It's essentially the "main" script or executable for your container. You have it set as 
ENTRYPOINT []
, which is an empty entrypoint, meaning Prefect can fully control what command sequence the container will execute. 2. Overriding Entrypoint in Prefect: When you configure a deployment in Prefect, you specify the 
entrypoint
parameter. This isn't directly related to Docker's entrypoint but refers to the Python file and function in your flow. Prefect uses this to determine what to execute. 3. Setting Entrypoint: If you want to set a specific entrypoint behavior in Docker while giving Prefect flexibility to use its parameters, you can use:
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dockerfile
   ENTRYPOINT ["prefect", "execute", "flow-run"]
This setup allows Prefect to invoke the necessary flow with the appropriate orchestrated commands. 4. How Prefect Uses the Entrypoint: Prefect doesn't directly override the Docker entrypoint. Instead, it passes the flow definition and parameters as arguments to whatever the entrypoint command is. Since you've set an empty entrypoint, it relies entirely on Prefect’s orchestration. 5. Adjust Using Prefect Commands Directly: If you wish for Prefect to handle all flow-related commands, you can leave the entrypoint either empty (as you have done) or explicitly include Prefect’s execution commands. This flexibility allows for Runtime Environment Variable Overrides through job settings in Prefect configurations. If you're looking for more detailed instructions on using Prefect with Docker, check out this Prefect Docker documentation. It provides examples and configurations that might closely relate to your use case. Think of Prefect as having the ultimate say in what goes on inside the container, much like a chef running a spaceship canteen—getting the recipes right is half your battle won!
f
@Marvin where should I ensure that the prefect deployment has the permission to pull the image from my private ecr registry?
m
To ensure that your Prefect deployment has permission to pull a Docker image from a private ECR registry, you need to configure AWS permissions and provide credentials appropriately. Here’s how you can do this: 1. IAM Role with ECS Task Execution Role: - Create an IAM Role: In the AWS Management Console, navigate to IAM and create a new role. - Attach Policies: Attach the 
AmazonECSTaskExecutionRolePolicy
policy to this role. This policy allows ECS to pull images from ECR and send logs to CloudWatch. - Trust Relationship: Update the trust relationship of this role to include ECS tasks:
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json
     {
       "Version": "2012-10-17",
       "Statement": [
         {
           "Effect": "Allow",
           "Principal": {
             "Service": "<http://ecs-tasks.amazonaws.com|ecs-tasks.amazonaws.com>"
           },
           "Action": "sts:AssumeRole"
         }
       ]
     }
2. ECS Service Role: - Ensure that your ECS service is configured to use this IAM role as the task execution role. 3. ECR Repository Policies (if needed): - Navigate to your ECR repository and ensure permissions allow the service to pull images. By default, if you're using an IAM role with correct permissions, this should already be covered. 4. IAM User and Credentials (Alternate Method): - Access Keys: If you are using IAM user credentials instead, ensure you have access keys with the right permissions. This is less recommended for ECS but may be relevant in other contexts. - Store Credentials Securely: Use AWS secrets management or an environment variable configuration to securely provide these credentials. 5. Configure Fargate Task Role: If using Fargate, similar configurations apply, where you assign the task execution role with ECR access policies. 6. AWS CLI Access (Testing Locally or Debugging): - Make sure your AWS CLI is configured to use an IAM user or role with permissions to pull from ECR if you are testing locally. 7. VPC and Network Configurations: - Ensure your ECS tasks have network access to ECR, typically allowing through a VPC endpoint or appropriate internet gateway if they're in a public subnet. In summary, having your ECS task execution IAM role configured with the necessary permissions is key, as it's responsible for communicating with ECR to pull the images. If that role is correctly set, Prefect should be able to orchestrate deployments smoothly. When configuring your roles and policies, think of yourself as a meticulous air traffic controller—one wrong permission might lead to a metaphorical landing mishap!
f
@Marvin knowing the flow and task code is baked into my docker image, do I need a pull step in my prefect.yaml deployment file? My dockerfile:
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FROM <http://ghcr.io/astral-sh/uv:python3.11-bookworm-slim|ghcr.io/astral-sh/uv:python3.11-bookworm-slim>

ARG PROJECT_PATH

# Set working directory
WORKDIR /app
# Enable bytecode compilation
ENV UV_COMPILE_BYTECODE=1
# Copy from the cache instead of linking since it's a mounted volume
ENV UV_LINK_MODE=copy

# Install the project's dependencies using the lockfile and settings
RUN --mount=type=cache,target=/root/.cache/uv \
    --mount=type=bind,source=uv.lock,target=uv.lock \
    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
    uv sync --frozen --no-install-project --no-dev


# Then, add the rest of the project source code and install it
# Installing separately from its dependencies allows optimal layer caching
COPY pyproject.toml .
COPY uv.lock .
COPY src/common src/common
COPY ${PROJECT_PATH} ${PROJECT_PATH}
RUN --mount=type=cache,target=/root/.cache/uv \
    uv sync --frozen --no-dev
COPY src/common src/common
COPY ${PROJECT_PATH} ${PROJECT_PATH}

# Place executables in the environment at the front of the path
ENV PATH="/app/.venv/bin:$PATH"

# Reset the entrypoint, don't invoke `uv`
ENTRYPOINT []
My prefect.yaml file
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# Generic metadata about this project
name: test
prefect-version: 3.1.6

# the deployments section allows you to provide configuration for deploying flows
deployments:
- name: test_flow_x_hourly_deployment
  version: "1"
  tags: null
  description: This deployment runs flow x hourly
  schedules:
    - cron: "0 0 * * *"
      timezone: "Europe/Paris"
      active: "true"
  flow_name: flow_1
  entrypoint: src/projects/test/prefect/flows/flow_1.py:sample_flow
  parameters:
    input_data: "Hello, World!"
  work_pool:
    name: ecs-fargate-workpool
    work_queue_name: default
    job_variables:
      image: '{{ $IMAGE_NAME }}:{{ $IMAGE_TAG }}'
m
Your message was too long by 222 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
knowing the flow and task code is baked into my docker image, do I need a pull step in my prefect.yaml deployment file?
My dockerfile: ```FROM ghcr.io/astral-sh/uv:python3.11-bookworm-slim ARG PROJECT_PATH # Set working directory WORKDIR /app # Enable bytecode compilation ENV UV_COMPILE_BYTECODE=1 # Copy from the cache instead of linking since it's a mounted volume ENV UV_LINK_MODE=copy # Install the project's dependencies using the lockfile and settings RUN --mount=type=cache,target=/root/.cache/uv \ --mount=type=bind,source=uv.lock,target=uv.lock \ --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ uv sync --frozen --no-install-project --no-dev # Then, add the rest of the project source code and install it # Installing separately from its dependencies allows optimal layer caching COPY pyproject.toml . COPY uv.lock . COPY src/common src/common COPY ${PROJECT_PATH} ${PROJECT_PATH} RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --frozen --no-dev COPY src/common src/common COPY ${PROJECT_PATH} ${PROJECT_PATH} # Place executables in the
f
@Marvin knowing the flow and task code is baked into my docker image, do I need a pull step in my prefect.yaml deployment file?
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# Generic metadata about this project
name: test
prefect-version: 3.1.6

# the deployments section allows you to provide configuration for deploying flows
deployments:
- name: test_flow_x_hourly_deployment
  version: "1"
  tags: null
  description: This deployment runs flow x hourly
  schedules:
    - cron: "0 0 * * *"
      timezone: "Europe/Paris"
      active: "true"
  flow_name: flow_1
  entrypoint: src/projects/test/prefect/flows/flow_1.py:sample_flow
  parameters:
    input_data: "Hello, World!"
  work_pool:
    name: ecs-fargate-workpool
    work_queue_name: default
    job_variables:
      image: '{{ $IMAGE_NAME }}:{{ $IMAGE_TAG }}'
m
Since your flow and task code are already baked into your Docker image, you don't need an additional pull step for the Python code or dependencies in your 
prefect.yaml
deployment file. The 
pull
step is typically required when your flow code is stored in a remote repository (like GitHub) and needs to be retrieved at runtime. Your deployment is configured to use a Docker image that contains everything necessary to run your flow. As long as the 
job_variables.image
is correctly set in the deployment with the appropriate ECR image name and tag, Prefect will handle executing the flow as the ECS task pulls and runs the image. In this setup, Prefect's orchestration commands within the container should take over and execute the defined entrypoint using the baked-in code. So, you're all set without needing an explicitly configured pull step in the `prefect.yaml`—unless you're longing for a reason to pull things just for nostalgia's sake. Carry on with the orchestration, and let those flows run!
f
@Marvin What about
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prefect.deployments.steps.set_working_directory
Should I use this at all?
m
The 
prefect.deployments.steps.set_working_directory
step is used to set the working directory for the process prior to importing your flow. This step can be helpful if your flow code is not stored in the root of your Docker image and you need to navigate to the appropriate directory to execute the flow. In your specific case with the flow and task code baked into the Docker image, and assuming that the paths in the 
prefect.yaml
correctly point to your code (
entrypoint: src/projects/test/prefect/flows/flow_1.py:sample_flow
), there's typically no need to explicitly use the 
set_working_directory
step unless: 1. Flow Location: Your flow code is stored in a subdirectory that needs to be set as the working directory before execution. 2. Complex Directory Structure: You have a more complex directory structure that requires specific navigation for relative paths in your flow. If everything works without it and your Docker image's work directory defaults to the correct path, you might not need to specify this step. However, if you encounter issues related to file or module paths not being found, revisiting this setting could be helpful. For more insights, refer to this Prefect documentation on infrastructure concepts here. Remember, setting the correct working directory can feel like launching a ship without forgetting the fuel—whenever necessary, of course!
f
@Marvin My deployment run fails with the following logs:
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December 21, 2024 at 18:12 (UTC+1:00)
17:12:35.037 | ERROR | prefect.engine - Engine execution of flow run 'd23bdfa0-586e-42f9-9dae-9ed861d8bd0c' exited with unexpected exception
prefect
December 21, 2024 at 18:12 (UTC+1:00)
Traceback (most recent call last):
prefect
December 21, 2024 at 18:12 (UTC+1:00)
File "/app/.venv/lib/python3.11/site-packages/prefect/deployments/steps/core.py", line 163, in run_steps
prefect
December 21, 2024 at 18:12 (UTC+1:00)
step_output = await run_step(step, upstream_outputs)
prefect
December 21, 2024 at 18:12 (UTC+1:00)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
prefect
December 21, 2024 at 18:12 (UTC+1:00)
File "/app/.venv/lib/python3.11/site-packages/prefect/deployments/steps/core.py", line 119, in run_step
prefect
December 21, 2024 at 18:12 (UTC+1:00)
raise ValueError(
prefect
December 21, 2024 at 18:12 (UTC+1:00)
ValueError: Step has unexpected additional keys: prefect.deployments.steps.set_working_directory
Here is my prefect.yaml file
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# Generic metadata about this project
name: test # UPDATE with project name
prefect-version: 3.1.6

pull:
- prefect.deployments.steps.set_working_directory:
    directory: /app/

# the deployments section allows you to provide configuration for deploying flows
deployments:
- name: test_flow_x_hourly_deployment # UPDATE with deployment name (1 project can have multiple deployments)
  version: "1" # Increment with each deployment
  tags: null
  description: This deployment runs flow x hourly # UPDATE with deployment description
  schedules: # UPDATE with scheduling, options available here <https://docs.prefect.io/v3/deploy/infrastructure-concepts/prefect-yaml#schedule-fields>
    - cron: "0 0 * * *"
      timezone: "Europe/Paris"
      active: "true"
  flow_name: flow_1 # UPDATE with flow name as to appear on Prefect UI
  entrypoint: src/projects/test/prefect/flows/flow_1.py:sample_flow # UPDATE with path to flow file and flow method in the docker container
  parameters: # UPDATE with flow parameters
    input_data: "Hello, World!"
  work_pool:
    name: ecs-fargate-workpool
    work_queue_name: default
    job_variables:
      image: '{{ $IMAGE_NAME }}:{{ $IMAGE_TAG }}'
m
Your message was too long by 346 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
My deployment run fails with the following logs:
```December 21, 2024 at 18:12 (UTC+1:00) 171235.037 | ERROR | prefect.engine - Engine execution of flow run 'd23bdfa0-586e-42f9-9dae-9ed861d8bd0c' exited with unexpected exception prefect December 21, 2024 at 18:12 (UTC+1:00) Traceback (most recent call last): prefect December 21, 2024 at 18:12 (UTC+1:00) File "/app/.venv/lib/python3.11/site-packages/prefect/deployments/steps/core.py", line 163, in run_steps prefect December 21, 2024 at 18:12 (UTC+1:00) step_output = await run_step(step, upstream_outputs) prefect December 21, 2024 at 18:12 (UTC+1:00) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ prefect December 21, 2024 at 18:12 (UTC+1:00) File "/app/.venv/lib/python3.11/site-packages/prefect/deployments/steps/core.py", line 119, in run_step prefect December 21, 2024 at 18:12 (UTC+1:
f
@Marvin My deployment run fails as below:
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17:12:35.037 | ERROR | prefect.engine - Engine execution of flow run 'd23bdfa0-586e-42f9-9dae-9ed861d8bd0c' exited with unexpected exception
Traceback (most recent call last):
File "/app/.venv/lib/python3.11/site-packages/prefect/deployments/steps/core.py", line 163, in run_steps
step_output = await run_step(step, upstream_outputs)
File "/app/.venv/lib/python3.11/site-packages/prefect/deployments/steps/core.py", line 119, in run_step
raise ValueError(
ValueError: Step has unexpected additional keys: prefect.deployments.steps.set_working_directory
My prefect.yaml
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name: test
prefect-version: 3.1.6
pull:
- prefect.deployments.steps.set_working_directory:
    directory: /app/
deployments:
- name: test_flow_x_hourly_deployment
  tags: null
  entrypoint: src/projects/test/prefect/flows/flow_1.py:sample_flow
  work_pool:
    name: ecs-fargate-workpool
    work_queue_name: default
    job_variables:
      image: '{{ $IMAGE_NAME }}:{{ $IMAGE_TAG }}'
m
It looks like the error in your deployment is due to an unexpected use of the 
prefect.deployments.steps.set_working_directory
step in your 
prefect.yaml
file. This step isn't directly supported or expected in the 
pull
or other sections as you've configured it. Here’s how you can address the issue: 1. Remove the 
pull
Step: Since your flow and task code are baked into your Docker image, you don't actually need to use a 
pull
step in this configuration. Prefect expects to execute the flow from the entrypoint specified and doesn't need additional steps to change the working directory unless sourcing from a remote location. 2. Docker Image and Entrypoint: Make sure your Docker image’s default work directory is set correctly, or include a command in your Dockerfile to set the 
WORKDIR
if needed. This ensures that your entrypoint can successfully find and execute the specified flow path. 3. Correct 
prefect.yaml
Configuration: Remove the unnecessary 
pull
step to prevent further errors. Your configuration should focus just on the essential settings like the right image and work pool. Here's how your 
prefect.yaml
might look after removing the pull step:
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yaml
name: test
prefect-version: 3.1.6

deployments:
- name: test_flow_x_hourly_deployment
  tags: null
  entrypoint: src/projects/test/prefect/flows/flow_1.py:sample_flow
  work_pool:
    name: ecs-fargate-workpool
    work_queue_name: default
    job_variables:
      image: '{{ $IMAGE_NAME }}:{{ $IMAGE_TAG }}'
If everything else is properly configured, removing this line should help resolve the issue, enabling Prefect to manage the job execution without encountering unexpected keys. Imagine Prefect as your diligent assistant who doesn't appreciate surprises in the instruction manual!
f
@Marvin When I remove it, I get this error:
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File "/app/.venv/lib/python3.11/site-packages/prefect/deployments/steps/pull.py", line 32, in set_working_directory
os.chdir(directory)
FileNotFoundError: [Errno 2] No such file or directory: '/home/runner/work/opsima-data/opsima-data'
m
The error message you're encountering indicates that the process is attempting to change to a directory that doesn't exist within the container during execution. Here’s how you can address this issue: 1. Verify Entrypoint Path: - Make sure the path in the 
entrypoint
parameter (
src/projects/test/prefect/flows/flow_1.py:sample_flow
) correctly points to the location of your flow in the Docker image. 2. Docker Image Default Work Directory: - Specify the correct working directory in your Dockerfile to match where your code is placed. You can do this using the 
WORKDIR
directive:
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dockerfile
     WORKDIR /app
3. Verify Copied Code Paths: - When building your Docker image, ensure the COPY commands in your Dockerfile are placing the code in the expected directories. Your Dockerfile should look something like this:
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dockerfile
     COPY ${PROJECT_PATH} /app/src/projects/test/prefect/flows/
4. Debugging the Docker Image: - Run your Docker image locally, and use a shell to verify that the files are in the correct location:
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bash
     docker run -it --entrypoint /bin/sh your-image-name
- Once inside the container, navigate to 
/app
or where you expect your code to be, and ensure the paths match. 5. Prefect Configuration Paths: - Double-check your Prefect configuration and entrypoint settings. If necessary, update them to align with your confirmed directory structure. The error suggests that Prefect cannot find the specified directory, so ensuring your Docker setup accurately aligns with the paths in the 
entrypoint
and Dockerfile means that Prefect can successfully navigate to and execute the flow. If everything is verified, it's like finally tuning the knob to unlock seamless orchestration—without any legendary directory treks!
l
@Fabien Punin hello! You got it ? Have you fixed this ?
Hello @Nate can you help us please? I'm having the same issue of Fabien ... Currently my flows are working on ecs, prefect 2. I'm trying to migrate to prefect 3, with the same structure ... but when using prefect 3 image ( amd or arm ) I'm receiving this too ...
f
Hey @Leonardo Perazzini, I managed to make it work but I had a few different issues. What's the error you get at the moment?
l
I'm receiving status code error -1 from ECS log. It should be a mismatch type of container, but I already tried use arm arm and amd images and it's not working yet. In prefect 2 I'm using this same flow ... Build deploy from _source + s3 storage + ECS, there when use prefect image v2 works, when try upgrade to v3, I'm getting error code -1. I don't know more what to do ... if I cancel trying to upgrade to v3 ...
f
Can you try setting both your image entrypoint and cmd to [] in your dockerFile? It might be something else but it did unblock me at some point
n
if you’re using prefect deployments, i generally wouldn’t recommend overriding the entry point / cmd, bc we use a specific value to invoke the prefect engine that exit code usually means 1 of 2 things: • missing 3rd party python reps remotely • bad creds for pulling code
✅ 1
f
I might have got to make it work through some convoluted way but if that helps this is my dockerFile FROM ghcr.io/astral-sh/uv:python3.11-bookworm-slim ARG PROJECT_PATH # Set working directory WORKDIR /app # Enable bytecode compilation ENV UV_COMPILE_BYTECODE=1 # Copy from the cache instead of linking since it's a mounted volume ENV UV_LINK_MODE=copy # Install the project's dependencies using the lockfile and settings RUN --mount=type=cache,target=/root/.cache/uv \ --mount=type=bind,source=uv.lock,target=uv.lock \ --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ uv sync --frozen --no-install-project --no-dev # Then, add the rest of the project source code and install it # Installing separately from its dependencies allows optimal layer caching COPY pyproject.toml . COPY uv.lock . COPY projects/common projects/common COPY ${PROJECT_PATH} ${PROJECT_PATH} RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --frozen --no-dev # Place executables in the environment at the front of the path ENV PATH="/app/.venv/bin:$PATH" # Reset the entrypoint, don't invoke 
uv
ENTRYPOINT [] CMD []
✅ 1
And my prefect.yaml for the deployment: deployments: - name: deployment_name version: null tags: null description: null flow_name: flow_name entrypoint: projects/project_name/src/prefect/flows/flow.py:flow_method parameters: null work_pool: name: ecs-fargate-workpool work_queue_name: default job_variables: image: "{{ $IMAGE_NAME }}:{{ $IMAGE_TAG }}" pull: - prefect.deployments.steps.set_working_directory: directory: /app/
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