Ah, this is a classic Docker architecture mismatch issue. The error you're seeing typically occurs when there's a mismatch between the CPU architecture of your Docker image and the host machine running it. This commonly happens when: 1. Running an x86/amd64 image on an ARM machine (like Apple M1/M2) 2. Or vice versa - running an ARM image on an x86 machine To fix this, you have several options: 1. Build for the correct architecture:

bash
   docker build --platform linux/amd64 .  # For x86/amd64
   # OR
   docker build --platform linux/arm64 .  # For ARM

2. Use multi-architecture images: If you're using the official Prefect images, make sure to pull the correct one for your architecture. The official images support multiple architectures through Docker manifests. 3. Specify platform when running:

bash
   docker run --platform linux/amd64 ...  # For x86/amd64
   # OR
   docker run --platform linux/arm64 ...  # For ARM

To help diagnose this further, could you share: 1. What type of machine you're running on (x86 or ARM)? 2. Are you using a custom Dockerfile or the official Prefect images? 3. How are you building/deploying your Docker image? This will help me provide more specific guidance for your situation.

I see you're asking about

flow.deploy()

. Could you provide more context about what you're trying to achieve? Are you: 1. Trying to create a new deployment? 2. Getting an error when running

flow.deploy()

? 3. Looking for general guidance on how to use deployments? In Prefect 3.x, the typical pattern for creating deployments looks like this:

python
from prefect import flow

@flow
def my_flow():
    # your flow code here
    pass

if __name__ == "__main__":
    flow.from_source(
        source="<https://github.com/your-repo>",  # or local path
        entrypoint="path/to/file.py:my_flow"
    ).deploy(
        name="my-deployment",
        work_pool_name="my-work-pool",
        image="your-docker-image:tag"  # if using Docker
    )

However, to help you more effectively, I'd need to know: 1. What specific error you're encountering (if any) 2. What you're trying to accomplish with the deployment 3. Whether you're using Docker or another infrastructure type This will help me provide more targeted assistance for your use case.

✅ thought for 8.3 seconds

Ah, now I understand - you're using

flow.deploy()

and encountering the architecture mismatch error with Docker. Let me provide a more specific solution. In Prefect 3.x, when using

flow.deploy()

, the architecture mismatch can be resolved by specifying the correct platform in your deployment. Here's how to fix it:

python
from prefect import flow
from prefect.infrastructure import DockerImage

docker_image = DockerImage(
    image="your-image-name:tag",
    platform="linux/amd64"  # or "linux/arm64" for ARM-based systems
)

@flow
def your_flow():
    # your flow code here
    pass

if __name__ == "__main__":
    your_flow.deploy(
        name="your-deployment-name",
        work_pool_name="your-work-pool",
        image=docker_image
    )

The key is setting the correct

platform

parameter in the DockerImage configuration. Choose: -

linux/amd64

for Intel/AMD processors (most common) -

linux/arm64

for ARM-based systems (like Apple M1/M2) To help you further, could you share: 1. What type of machine you're running on (Intel/AMD or ARM-based like Apple Silicon)? 2. The contents of your deployment code? 3. Are you using a custom Dockerfile or a standard Prefect image? This will help me provide the exact configuration you need to resolve the architecture mismatch.

I understand you're encountering the Docker architecture mismatch with

flow.deploy()

. Let me be more direct about fixing this specific error. The "exec /usr/bin/tini: exec format error" means your Docker container is trying to run on an architecture it wasn't built for. Here's how to fix it:

python
from prefect import flow
from prefect.infrastructure import DockerImage

# Explicitly specify the platform to match your execution environment
docker_image = DockerImage(
    image="your-image-name:tag",
    platform="linux/amd64"  # This is crucial for fixing the tini error
)

@flow
def your_flow():
    # your flow code
    pass

if __name__ == "__main__":
    your_flow.deploy(
        name="your-deployment-name",
        work_pool_name="your-work-pool",
        image=docker_image
    )

To help you implement the fix, I need to know: 1. Are you running on Apple Silicon (M1/M2) or a regular Intel/AMD machine? 2. What's the base image you're using in your Dockerfile? These details will let me give you the exact platform setting you need to resolve the tini error.

✅ thought for 6.3 seconds

Let me be even more direct about fixing this Docker architecture error. The solution is to explicitly set the platform in your deployment. There are only two possibilities:

python
# OPTION 1 - If your execution environment is Intel/AMD (most common):
docker_image = DockerImage(
    image="your-image-name:tag",
    platform="linux/amd64"
)

# OPTION 2 - If your execution environment is ARM-based (like Apple M1/M2):
docker_image = DockerImage(
    image="your-image-name:tag",
    platform="linux/arm64"
)

# Then use it in your deployment:
flow.deploy(
    name="your-deployment-name",
    work_pool_name="your-work-pool",
    image=docker_image
)

Which option you need depends on your execution environment (not your local machine). Could you tell me where your flows are actually running (i.e., what type of machine/infrastructure is executing the deployed flows)?