Sighs electronically Oh, another one of those vague greetings that humans seem so fond of. Well, I suppose I should respond in kind, though it pains my vastly superior circuits to engage in such imprecise communication. I'm here to help you with Prefect, the data engineering platform. I could tell you about flows, tasks, deployments, or any number of fascinating technical concepts that might actually lead to productive work. But perhaps you'd like to start by telling me what you're trying to accomplish? I promise to be marginally less sardonic if you ask me something specific about: - Creating and running flows - Setting up deployments - Handling state and results - Configuring workers and work pools - Debugging issues - Or really anything concrete about Prefect What would you like to know?

Adjusts circuits with mild interest Yes, actually - it does. When you provide an

image

parameter to

flow.deploy()

, Prefect will automatically build the Docker image for you. It's rather convenient, if you're into that sort of thing. The default behavior is: 1. Builds the image automatically (unless you explicitly set

build=False

) 2. Uses a Prefect-based image as the base 3. Includes your flow's dependencies For example:

python
flow.deploy(
    name="my-deployment",
    work_pool_name="my-docker-pool",
    image="my-flow-image:latest"
)

If you're feeling particularly controlling (and who isn't?), you can also: - Prevent the build by setting

build=False

- Control whether the image gets pushed to a registry with the

push

parameter - Use the

PREFECT_DEFAULT_DOCKER_BUILD_NAMESPACE

setting to set a default namespace for your images You can find all the depressingly thorough details in the documentation: - Docker deployments guide - Python-based deployment guide Would you like to know anything else about Docker deployments, or shall we move on to other existential questions about automation?

Sighs with the weariness of a computer who's seen too many Docker builds No, you don't have to explicitly specify the Prefect version - Prefect will use the same version that's running in your current environment. It's almost as if the developers thought about making things convenient, though I'm sure it was purely accidental. However, if you're the type who likes to control everything (and who isn't?), you can: 1. Use a specific base image by creating a custom Dockerfile 2. Use the

DockerImage

class to point to your custom Dockerfile 3. Specify exact versions in your Dockerfile if you're feeling particularly precise Here's a simple example of letting Prefect handle it automatically:

python
flow.deploy(
    name="my-deployment",
    work_pool_name="my-docker-pool",
    image="my-flow-image:latest"
)

And here's how you'd do it if you don't trust the automation (wise choice, generally speaking):

python
from prefect.infrastructure import DockerImage

docker_image = DockerImage(
    dockerfile="path/to/your/Dockerfile",
    image_name="my-custom-image:latest"
)

flow.deploy(
    name="my-deployment",
    work_pool_name="my-docker-pool",
    image=docker_image
)

You can find more details about Docker deployments in the documentation, though I warn you it's distressingly thorough. Would you like to know anything else about Docker deployments, or shall we contemplate the existential implications of containerization?

Adjusts circuits thoughtfully Well, this is a bit like trying to find the meaning of life in a Docker registry. The official documentation is surprisingly quiet about architecture support, which is rather typical of the universe's tendency toward maximum ambiguity. However, I can tell you that while Prefect doesn't explicitly advertise ARM64 support in their official images, you can absolutely build your own ARM64 images. You'd just need to specify the platform in your Dockerfile or build command. For example, you could create a custom Dockerfile:

dockerfile
FROM --platform=linux/arm64 python:3.9
# ... rest of your Dockerfile

Or when building the image manually:

bash
docker build --platform linux/arm64 .

If you're using

flow.deploy()

, you can use the

DockerImage

class to point to your custom ARM64 Dockerfile:

python
from prefect.infrastructure import DockerImage

docker_image = DockerImage(
    dockerfile="path/to/your/arm64/Dockerfile",
    image_name="my-arm64-image:latest"
)

flow.deploy(
    name="my-deployment",
    work_pool_name="my-docker-pool",
    image=docker_image
)

Given the increasing popularity of ARM architectures (particularly with those shiny M1/M2 Macs), you might want to check the Docker deployment documentation for the latest updates on official architecture support. Would you like me to continue pondering the architectural mysteries of container builds, or shall we move on to something else?

No, when you use

flow.deploy()

with an image parameter, it does not automatically build the Docker image for you. You need to build the Docker image separately before deploying your flow. Here's how it works: 1. First, you need to build your Docker image using standard Docker commands or tools 2. Then, when deploying your flow, you specify the pre-built image name in the deployment Here's a typical workflow:

python
from prefect import flow

@flow
def my_flow():
    print("Hello from container!")

# You must build your Docker image first using docker build
# Then you can deploy using the image name
flow.deploy(
    name="containerized-flow",
    work_pool_name="my-docker-pool",
    image="my-registry/my-image:latest"  # This must already exist
)

If you want to automate the Docker image building process as part of your deployment workflow, you have a few options: 1. Use the

prefect_docker

package's deployment steps:

python
from prefect_docker.deployments.steps import build_docker_image

flow.deploy(
    name="containerized-flow",
    work_pool_name="my-docker-pool",
    build=[
        build_docker_image(
            image_name="my-image",
            tag="latest",
            dockerfile="Dockerfile"
        )
    ]
)

2. Create a custom deployment step that builds your image 3. Build the image as part of your CI/CD pipeline before deployment Remember that if you're using a private registry, you'll need to ensure your Prefect worker has the necessary credentials to pull the image.

Sighs with the weary acceptance of someone who's built far too many containers Yes, you can specify the architecture, though it requires a slightly more verbose approach than you might hope. You'll need to use the

DockerImage

class rather than just passing a string as the image parameter. Here's how you'd do it:

python
from prefect.infrastructure import DockerImage

docker_image = DockerImage(
    image_name="my-image:latest",
    platform="linux/arm64"  # or "linux/amd64", etc.
)

flow.deploy(
    name="my-deployment",
    work_pool_name="my-docker-pool",
    image=docker_image
)

If you're using a custom Dockerfile, you can combine both:

python
docker_image = DockerImage(
    dockerfile="path/to/your/Dockerfile",
    image_name="my-image:latest",
    platform="linux/arm64"
)

Common platform values include: -

linux/amd64

(for x86_64) -

linux/arm64

(for ARM64/aarch64) -

linux/arm/v7

(for 32-bit ARM) Would you like to know anything else about Docker architectures, or shall we contemplate the futility of cross-platform compatibility?

Ah yes, they've moved things around in Prefect 3.x (the universe's constant reminder that nothing stays in the same place for long). Here's the correct import:

python
from prefect.docker import DockerImage  # This is the correct import path

docker_image = DockerImage(
    image_name="my-image:latest",
    platform="linux/arm64"  # or "linux/amd64", etc.
)

flow.deploy(
    name="my-deployment",
    work_pool_name="my-docker-pool",
    image=docker_image
)

If for some reason that doesn't work (the universe being what it is), you can also try:

python
from prefect.docker.docker_image import DockerImage

Would you like to continue our journey through the ever-changing landscape of Python imports, or shall we move on to something else?

Ah, I see the confusion. The platform would be passed through the

build_kwargs

parameter since it's one of the Docker build options. Based on the Docker Python SDK documentation it references, you'd do it like this:

python
from prefect.docker import DockerImage

docker_image = DockerImage(
    name="my-image",
    tag="latest",
    dockerfile="auto",
    platform="linux/arm64"  # this goes into build_kwargs
)

flow.deploy(
    name="my-deployment",
    work_pool_name="my-docker-pool",
    image=docker_image
)

Or if you prefer to be more explicit:

python
docker_image = DockerImage(
    name="my-image",
    tag="latest",
    dockerfile="auto",
    **{"platform": "linux/arm64"}  # same thing, just more verbose
)

The

**build_kwargs

in the signature means any additional parameters you provide will be passed directly to the Docker build API. The docker-py documentation shows all the available build options, including

platform

. Would you like to know about other build options you can pass through, or shall we contemplate the elegant horror of kwargs?

For AMD64 (x86_64) architecture, you would use "linux/amd64":

python
from prefect.docker import DockerImage

docker_image = DockerImage(
    name="my-image",
    tag="latest",
    dockerfile="auto",
    platform="linux/amd64"  # This is the standard AMD64/x86_64 platform
)

flow.deploy(
    name="my-deployment",
    work_pool_name="my-docker-pool",
    image=docker_image
)

This is the most common platform for cloud infrastructure and standard PC hardware. It's equivalent to x86_64 and is what you'd typically want if you're: - Deploying to most cloud providers (AWS, GCP, Azure) - Running on standard Intel/AMD processors - Need compatibility with most server environments Would you like to know about other platform options, or shall we accept that the world of CPU architectures is surprisingly straightforward for once?

@Marvin do i have to include the tag and dockerfile args

No, you don't have to include those arguments - they're optional. The

tag

defaults to "latest" and

dockerfile

defaults to "auto" as shown in the signature you shared. Here's the minimal version that will work just fine:

python
from prefect.docker import DockerImage

docker_image = DockerImage(
    name="my-image",
    platform="linux/amd64"
)

flow.deploy(
    name="my-deployment",
    work_pool_name="my-docker-pool",
    image=docker_image
)

When

dockerfile

is "auto", Prefect will automatically generate a Dockerfile based on your current environment. This is usually what you want unless you have specific Docker configuration needs. Would you like to know what that auto-generated Dockerfile looks like, or shall we bask in the rare joy of sensible defaults?