Hello! How can I assist you today?

Hello! How can I assist you today?

@Marvin how to pass job_variables to deploy so that i can rename the container definition to something else. this is for an ecs work pool

To pass job variables to the deploy command for renaming the container definition in an ECS work pool, you can specify these overrides in the

work_pool.job_variables

section of a

deployment.yaml

file. Here's an excerpt from the Prefect documentation that explains this:

You can override each of these attributes on a per-deployment basis. When deploying a flow, you can specify these overrides in the work_pool.job_variables section of a deployment.yaml.

For more details, you can refer to the Prefect documentation on Work Pools and Infrastructure.

@Marvin answer for python sdk and not with .yaml

In the Python SDK, you can pass job variables to the deploy command for an ECS work pool by using the

job_configuration

section of the

Flow

object. Here's an example:

from prefect import Flow

flow = Flow(
    "my-flow",
    job_configuration={
        "job_variables": {
            "container_definitions": [
                {
                    "name": "my-container",
                    # other container definition parameters...
                }
            ]
            # other job variables...
        }
    }
)

In this example, the

job_variables

dictionary contains a

container_definitions

key, which is a list of dictionaries. Each dictionary in this list represents a container definition, and you can specify the name of the container in the

name

key. Please note that this is a simplified example and you might need to adjust it according to your specific use case. For more details, you can refer to the Prefect documentation on Developing a New Worker Type.

@Marvin how does enhanced cancellation work?

Enhanced cancellation is a feature in Prefect that aims to improve the robustness of the cancellation process. It is currently in an experimental phase and can be enabled by setting the

PREFECT_EXPERIMENTAL_ENABLE_ENHANCED_CANCELLATION

configuration on your worker or agents:

bash
prefect config set PREFECT_EXPERIMENTAL_ENABLE_ENHANCED_CANCELLATION=True

The cancellation process in Prefect is designed to be robust, but there are a few issues that can occur: - If the infrastructure block for the flow run has been removed or altered, cancellation may not work. - If the infrastructure block for the flow run does not have support for cancellation, cancellation will not work. - If the identifier scope does not match when attempting to cancel a flow run the worker will be unable to cancel the flow run. Another worker may attempt cancellation. - If the infrastructure associated with the run cannot be found or has already been killed, the worker will mark the flow run as cancelled. - If the

infrastructre_pid

is missing from the flow run, it will be marked as cancelled but cancellation cannot be enforced. - If the worker runs into an unexpected error during cancellation the flow run may or may not be cancelled depending on where the error occurred. The worker will try again to cancel the flow run. Another worker may attempt cancellation. For more details, you can refer to the Prefect documentation on Flows.

@Marvin how do i reset my self hosted server database

To reset your self-hosted Prefect server database, you can use the Prefect CLI command:

bash
prefect server database reset -y

This command will clear all data and reapply the schema. The database is located at

~/.prefect/prefect.db

by default. Please note that this operation will delete all your data, so make sure to backup any important information before proceeding. For more details, you can refer to the Prefect documentation on Hosting.

@Marvin with python and get_client() can i get the last log published by a flow?

In Prefect, when you're working with an ECS work pool, you can define multiple containers in the task definition. This is done by providing a list of container definitions in the

containerDefinitions

parameter of the task definition. Here's an example:

task_definition = {
    "family": "my-task-definition",
    "networkMode": "bridge",
    "containerDefinitions": [
        {
            "name": "container1",
            "image": "my-image1",
            "cpu": 256,
            "memory": 512,
            "essential": True,
            # other container parameters...
        },
        {
            "name": "container2",
            "image": "my-image2",
            "cpu": 256,
            "memory": 512,
            "essential": True,
            # other container parameters...
        },
        # add more containers as needed...
    ],
    # other task definition parameters...
}

In this example, the

containerDefinitions

parameter is a list of dictionaries, where each dictionary represents a container. You can specify the name of the container in the

name

key, and the Docker image to use in the

image

key. You can also specify other parameters for each container, such as the CPU and memory requirements, and whether the container is essential. Please note that this is a simplified example and you might need to adjust it according to your specific use case. For more details, you can refer to the AWS documentation on Task Definitions.

@Marvin create an ecs base job template with two containers, the first one being the image passed in deploy and the second container with an ubuntu image

Creating an ECS-based job template with two containers can be done by defining a task definition with two container definitions. The first container will use the image passed during deployment, and the second container will use an Ubuntu image. Here's an example of how you can define such a task definition:

task_definition = {
    "family": "my-task-definition",
    "networkMode": "bridge",
    "containerDefinitions": [
        {
            "name": "container1",
            "image": "my-deploy-image",  # replace with your image
            "cpu": 256,
            "memory": 512,
            "essential": True,
            # other container parameters...
        },
        {
            "name": "container2",
            "image": "ubuntu:latest",
            "cpu": 256,
            "memory": 512,
            "essential": True,
            # other container parameters...
        },
    ],
    # other task definition parameters...
}

In this example, the

containerDefinitions

parameter is a list of dictionaries, where each dictionary represents a container. The first container uses the image passed during deployment (replace

"my-deploy-image"

with your image), and the second container uses the latest Ubuntu image. Please note that this is a simplified example and you might need to adjust it according to your specific use case. For more details, you can refer to the AWS documentation on Task Definitions.