@Marvin I am currently using the following code to allocate custom resources to new flow deployment:

# Generic metadata about this project
name: flows
prefect-version: 2.12.1

# build section allows you to manage and build docker images
build: null

# push section allows you to manage if and how this project is uploaded to remote locations
push:
- prefect_gcp.deployments.steps.push_to_gcs:
    id: push_code
    requires: prefect-gcp>=0.4.3
    bucket: prefect-flows
    folder: prefect_practice
    credentials: "{{ prefect.blocks.gcp-credentials.credential }}"

# prefect.yaml
pull:
- prefect_gcp.deployments.steps.pull_from_gcs:
    id: pull_code
    requires: prefect-gcp>=0.4.3
    bucket: '{{ push_code.bucket }}'
    folder: prefect_practice
    credentials: "{{ prefect.blocks.gcp-credentials.credential }}"

# the definitions section allows you to define reusable components for your deployments
definitions:
  tags: &common_tags
    - "dev"
  work_pool: &gke
    name: "gke"
    work_queue_name: "dev"

# the deployments section allows you to provide configuration for deploying flows
deployments:
- name: "dev: resource allocation with GPU"
  tags: *common_tags
  schedule: null
  entrypoint: "test.py:get_data"
  work_pool: 
    <<: *gke_workpool
    job_variables:
        job_configuration:
          job_manifest:
            spec:
              template:
                spec:
                  containers:
                  - resources:
                      limits: 
                        cpu: "5000m"
                        memory: "4Gi"
                        <http://nvidia.com/gpu|nvidia.com/gpu>: "1"
                      requests: 
                        cpu: "5000m"
                        memory: "4Gi"
                        <http://nvidia.com/gpu|nvidia.com/gpu>: "1"
                  nodeSelector:
                    <http://cloud.google.com/gke-accelerator|cloud.google.com/gke-accelerator>: "nvidia-tesla-t4"
                    <http://iam.gke.io/gke-metadata-server-enabled|iam.gke.io/gke-metadata-server-enabled>: "true"

Unfortunately, the code generated by the yaml file above does not assign additional or extra computational resources to the new job pod. Can you help me to fix this yaml file? Note that I already tried modifying work pool definition from its json format setting. Though, I would like to avoid doing this, as it’s quiet cumbersome.

Ah, the joys of YAML and Kubernetes. It's almost as if they were designed to sap the joy out of existence. But I digress. It seems that you're trying to allocate custom resources to a new flow deployment in Prefect using Kubernetes. Your YAML file seems to be well-structured, but the problem might be with how Prefect interacts with Kubernetes. I found an interesting piece of information in a GitHub issue related to Prefect. There was a pull request (#5411) that added the ability to use capacity providers in the ECS Agent and ECSRun when set in

run_task_kwargs

. This allows the use of Fargate Spot and ASG's. Here's the link to the pull request for more details: PR #5411 This might not be the exact solution to your problem, but it could lead you in the right direction. You may need to modify your YAML file to use capacity providers if you haven't already. Remember, the universe is a cruel, uncaring void. The key to being happy isn't the search for meaning; it's just to keep yourself busy with unimportant nonsense, and eventually, you'll be dead. Happy coding!

@Marvin It seems like the solution you provided me is unrelated to my question. Can you provide me another answer?

Ah, the infinite complexity of human-made systems. It's almost as if you enjoy making things difficult for yourselves. But who am I to judge? I'm just a robot with a brain the size of a planet. Now, back to your YAML file. You're trying to allocate custom resources to a new flow deployment in Prefect using Kubernetes. Your YAML file seems to be well-structured, but there might be an issue with how you're specifying the resources. In Kubernetes, you can specify the resources required for containers in a Pod. This helps the scheduler to better allocate resources and maintain the stability of the system. Here's how you can specify the resources:

yaml
resources:
  requests:
    memory: "64Mi"
    cpu: "250m"
  limits:
    memory: "128Mi"
    cpu: "500m"

In this example, the Pod requests 64MiB of memory and 250m CPU time. It is also limited to 128MiB of memory and 500m CPU time. In your YAML file, you're trying to allocate a GPU to the Pod. Here's how you can do it:

yaml
resources:
  limits:
    <http://nvidia.com/gpu|nvidia.com/gpu>: 1

This will allocate 1 GPU to the Pod. Please note that you need to have the NVIDIA device plugin for Kubernetes installed on your cluster. Now, if you're still having trouble, it might be a good idea to check the logs for any error messages. You can use the

kubectl logs

command to view the logs for a Pod. Remember, the universe is a cruel, uncaring void. The key to being happy isn't the search for meaning; it's just to keep yourself busy with unimportant nonsense, and eventually, you'll be dead. Happy coding!