@Marvin How to set Kubernetes Job resource limits on Prefect3

To set Kubernetes Job resource limits in Prefect 3, you can modify the base job template when deploying flows to a Kubernetes cluster. This can be done by configuring the Kubernetes cluster settings via the advanced settings tab and editing the JSON representation of the base job template. Here is a brief guideline on how to do this: 1. Access Advanced Settings: - Navigate to the advanced settings tab where you can modify the default base job template. 2. Edit the Job Template JSON: - Within the template, you can add or modify fields to set the desired resource limits. For example, if you wish to set a CPU request, you can include a section similar to this:

"cpu_request": {
       "title": "CPU Request",
       "description": "The CPU allocation to request for this pod.",
       "default": "default",
       "type": "string"
     }

These settings are part of Kubernetes cluster configuration and can be adjusted to match your specific resource requirements. You can read more about setting up and managing these configurations in the Prefect 3 documentation here: Kubernetes Infrastructure Examples.

@Marvin can you give a Job Template JSON file with cpu/memory limits to override?

To create a Kubernetes job template JSON with CPU and memory limits that you can use to override defaults in Prefect 3, you'll need to define these resources within the

resources

section of the Kubernetes pod specification. Here's an example of how such a job template might look:

json
{
  "apiVersion": "batch/v1",
  "kind": "Job",
  "metadata": {
    "name": "example-job"
  },
  "spec": {
    "template": {
      "spec": {
        "containers": [
          {
            "name": "example-container",
            "image": "your-image:tag",
            "resources": {
              "requests": {
                "cpu": "500m",
                "memory": "256Mi"
              },
              "limits": {
                "cpu": "1",
                "memory": "512Mi"
              }
            }
          }
        ],
        "restartPolicy": "Never"
      }
    },
    "backoffLimit": 4
  }
}

### Explanation: - `requests`: This section specifies the minimum amount of CPU and memory that the container will allocate. In this example, it requests 500 milliCPU and 256Mi of memory. - `limits`: This section sets the maximum amount of CPU and memory that the container can use. Here it limits to 1 CPU and 512Mi of memory. - `image`: Ensure to replace

"your-image:tag"

with the actual image you want to use. - `restartPolicy`: Usually set to

"Never"

for jobs that should finish once without restart. You can add this JSON configuration to your Kubernetes deployment scripts in Prefect 3 to override the default resource settings when executing a job. Adjust the resource requests and limits according to your requirements. If you need more details on how to implement this in Prefect or further customization, you can refer to the Kubernetes Infrastructure Examples in Prefect 3.

@Marvin How can I assign this job template to a specific deployment?

To assign a Kubernetes job template to a specific deployment in Prefect 3, you can use the

job_variables

section in your deployment definition within the

prefect.yaml

file to override the base job template defaults. Here's a step-by-step guide: 1. Configure `prefect.yaml`: - Open the

prefect.yaml

file for the deployment you want to configure. - Under the deployments section, specify the job variables to override the defaults. Here's an example configuration snippet:

yaml
deployments:
  - name: your-deployment-name
    entrypoint: path_to_your_flow.py:flow_name
    work_pool:
      name: your-work-pool
      job_variables:
        resources:
          cpu: "1"
          memory: "512Mi"
    schedule: null

2. Use Job Variables: - The

job_variables

key allows you to specify resource requests and limits, such as CPU and memory, that override the defaults in the specified work pool’s base job template. 3. Deploy the Configuration: - After defining your configuration, deploy it using the

prefect deploy

command. This will apply the job template changes to the deployment. By using this approach, you can customize specific deployments with different resource requirements, effectively utilizing Prefect's infrastructure overrides. For more detailed information, you can refer to the Prefect YAML Documentation.

@Marvin how I can add pip library to my job image via deployment api

To add a pip library to your job image via the Prefect 3 deployment API, you have a couple of options depending on whether you're using a Prefect-maintained image or a custom Docker image. 1. Using Prefect-Maintained Image: - You can use the

EXTRA_PIP_PACKAGES

environment variable to specify additional Python packages to be installed at runtime. For instance, to install the

pandas

library, you would set the environment variable as follows:

yaml
     job_variables:
       env:
         EXTRA_PIP_PACKAGES: "pandas"

2. Using a Custom Docker Image: - You can create a custom Dockerfile to include your Python packages. This involves building an image with the necessary dependencies baked in. Here is a basic example Dockerfile:

dockerfile
     FROM prefecthq/prefect:3-latest
     COPY requirements.txt .
     RUN pip install -r requirements.txt

- This method allows you to leverage Docker's image caching mechanism for efficiency, especially if you have a

requirements.txt

file holding multiple dependencies. 3. Specify in prefect.yaml: - Make sure these settings are reflected in your

prefect.yaml

deployment file to manage and version-control your deployment setup. Here is a useful source for further guidance: Kubernetes Infrastructure Examples in Prefect 3 Documentation.

@Marvin how to inject env variables to prefect k8s job from configmap

To inject environment variables from a ConfigMap into a Kubernetes job in Prefect 3, you need to modify the job template to use the ConfigMap as a source for environment variables. Here's a general approach to achieve this: 1. Define the ConfigMap: First, ensure your ConfigMap is defined in your Kubernetes cluster. An example ConfigMap might look like this:

yaml
   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: my-config
   data:
     MY_VAR1: "value1"
     MY_VAR2: "value2"

2. Modify Job Template: Update your job template within Prefect to use this ConfigMap. In your Kubernetes job template (which could be specified in your

prefect.yaml

or another configuration method), add an

envFrom

section to include the ConfigMap:

yaml
   spec:
     containers:
     - name: example-container
       image: your-image:tag
       envFrom:
       - configMapRef:
           name: my-config

3. Update Deployment Configuration: Ensure these job template changes are reflected in your Prefect deployment configuration, typically by making the adjustments within the

prefect.yaml

file if you are using Prefect's deployment setup. 4. Deploy the Configuration: After making these adjustments, deploy the updated configuration using the

prefect deploy

command to ensure the environment variables from the ConfigMap are utilized. You can read more on customizing deployments and managing job environment settings in the Prefect Documentation on Infrastructure Customization.