@Marvin can I change the job template per task? per flow? or per deployment?

You can change the job template for tasks, flows, and deployments in various ways in Prefect 2.x. ### Per Deployment You can override job template variables when deploying a flow by specifying them in the

work_pool.job_variables

section of a

deployment.yaml

. For example, to turn off streaming output for a specific deployment, you could add:

yaml
work_pool:
  name: above-ground
  job_variables:
    stream_output: false

For advanced customization, you can create work pools with fully customizable job templates on the 'Advanced' tab within the UI or via the Prefect CLI. This is useful when your infrastructure supports a high degree of customization. ### Per-task/Per-flow Customization Job templates allow for interpolating template variables into the job configuration. Template variables can be complex types like dictionaries and lists, and can be provided at flow run time. See Developing a new worker type for more intricate guidance. ### Relevant GitHub Issues - Add workers to work pool concept docs - Expand projects to support multiple deployments - Add deep dive on overriding job variables - Flow run job variables docs These resources should guide you in customizing job templates at the desired level. If you have specific use cases or further customization needs, feel free to ask. طراحی هر چیزی که می‌خواهید، فقط بگویید.

@Marvin can I change any job parameter defined in the job template when running a job with run_deployment?

Yes, you can change job parameters defined in the job template when running a job with

run_deployment

. The

job_variables

parameter in the deployment configuration allows you to override default values defined in the work pool's base job template. This includes parameters such as

image_pull_policy

, environment variables (e.g.,

EXTRA_PIP_PACKAGES

), and

image

attributes. Here's an example:

python
from prefect.deployments import run_deployment

run_deployment(
    deployment_name="my-deployment",
    parameters=dict(name="Prefect"),
    work_pool_name="my-docker-pool",
    job_variables={"image_pull_policy": "Never", "env": {"EXTRA_PIP_PACKAGES": "boto3"} },
    image="my-image:my-tag",
)

In this example,

image_pull_policy

is set to "Never", and the environment variable

EXTRA_PIP_PACKAGES

is set to "boto3". The

image

attribute is overridden with "my-image:my-tag". For more details, you can refer to the Prefect Deployment Guide and Overriding Job Variables. If you need further customization or have more specific requirements, feel free to ask. I'll be here for all your dreary questions.

@Marvin how can I change the location of ACI instance with job variables?

To change the location of an Azure Container Instance (ACI) using job variables in Prefect, you can specify the location in the job template configuration. Below are the detailed steps: ### Prerequisites 1. Install Azure-related libraries if not already installed:

bash
    pip install prefect-azure

2. Set up ACI credentials using Prefect's credential blocks:

python
    from prefect.infrastructure.azure import AzureContainerInstanceCredentials
    aci_credentials = AzureContainerInstanceCredentials(
        resource_group="your-resource-group",
        subscription_id="your-subscription-id",
        tenant_id="your-tenant-id",
        client_id="your-client-id",
        client_secret="your-client-secret"
    )
    aci_credentials.save("my-aci-creds")

### Creating a Deployment with ACI Here is an example of how you can define and override the location in the job variables:

python
from prefect.deployments import Deployment
from prefect.infrastructure.azure import AzureContainerInstanceJob

# Define the ACI job
container_instance_job = AzureContainerInstanceJob(
    aci_credentials=AzureContainerInstanceCredentials.load("my-aci-creds"),
    resource_group_name="azure_resource_group.example.name",
    subscription_id="your-subscription-id",
)

container_instance_job.save("aci-job-location")

# Deployment configuration
deployment = Deployment.build_from_flow(
    flow=your_flow_function,
    name="aci-deployment",
    infrastructure=container_instance_job,
    parameters={
        "param1": "value1"
    },
    work_pool_name="my-aci-work-pool",
    job_variables={
        "location": "eastus"  # Change the ACI location here
    }
)

deployment.apply()

### Starting the Worker Create a work pool and start a worker that pulls from it:

bash
prefect work-pool create -t azure-container-instance my-aci-work-pool
prefect worker start -n my-aci-worker -p my-aci-work-pool

This setup will start a worker that uses your custom job template with the specified ACI location. ### Reference Links - Prefect Azure Documentation - Overriding Job Variables I hope this helps you set up your ACI instance with job variables in Prefect. If you require further assistance, feel free to ask.

@Marvin can I pass any parameter to job_variables inside run_deployment? Or only env variables?

You can pass various parameters to

job_variables

inside

run_deployment

, not just environment variables. The job variables are infrastructure-related values configurable on a work pool, and they may include parameters such as

location

,

image_pull_policy

,

environment variables

, and custom settings specific to the infrastructure. ### Example: Passing Various Job Variables with

run_deployment

Here is an example showing how you can pass different types of job variables:

python
from prefect.deployments import run_deployment
from prefect.infrastructure.azure import AzureContainerInstanceJob

run_deployment(
    deployment_name="my-deployment",
    parameters=dict(name="Prefect"),
    work_pool_name="my-aci-work-pool",
    job_variables={
        "image_pull_policy": "Never",
        "env": {"EXTRA_PIP_PACKAGES": "boto3"},
        "location": "eastus",
    },
    image="my-image:my-tag",
)

### Key Points - Environment variables: You can set environment variables using the

env

key inside

job_variables

. - Infrastructure-specific settings: You can set other infrastructure-specific settings (like

image_pull_policy

and

location

). - Custom job variables: The specific job variables available depend on the type of work pool you’re using. ### Documentation Reference For a deeper dive into overriding job variables, you can refer to the Overriding Job Variables Guide. You're free to configure the job settings according to your needs by passing appropriate key-value pairs in

job_variables

. If you have any more questions or need further details, please feel free to ask.

@Marvin is there a known issue where job_variables are ignored when passed to run_deployment?

It appears there are no known issues reported in the Prefect GitHub repository specifically about job variables being ignored when passed to the

run_deployment

function. If you are encountering this problem, here are a few troubleshooting steps to consider: 1. Ensure Correct Syntax: Double-check your syntax for passing

job_variables

. Make sure it's correctly formatted and aligned with the examples provided in the documentation. 2. Check Documentation: Refer to the Prefect Deployment Guide and Overriding Job Variables Guide to ensure all necessary fields are correctly specified. 3. Upgrade Prefect: Ensure you are running the latest version of Prefect, as issues might have been resolved in newer releases. You can upgrade Prefect using:

bash
    pip install --upgrade prefect

4. Debug Logs: Increase the logging level to

DEBUG

to get more detailed output and see if there are any hints about why the job variables are being ignored.

python
    import logging
    logging.basicConfig(level=logging.DEBUG)

5. Prefect Community: If the problem persists, consider asking for help in the Prefect Slack community or on the Prefect Discourse forum. If you can provide additional details or a code snippet where the issue occurs, it might help identify the problem more precisely.