@Marvin Is it possible to run prefect deploy --prefect-file prefect.yaml not from the project root dir?

Yes, it is possible to run

prefect deploy --prefect-file prefect.yaml

from a directory that is not the project root. You can specify the path to the

prefect.yaml

file using the

--prefect-file

option. For example:

prefect deploy --prefect-file path/to/your_directory/prefect.yaml

For further details, refer to the official Prefect documentation: https://docs.prefect.io/latest/guides/prefect-deploy/

@Marvin This is my entrypoint main_using_prefect.py:main, when im running the command I get an error saying main_using_prefect.py cant be found. My directories structure is something like this ml_infra -> prefect.yaml, insertion_model-> main_using_prefect.py

It looks like the issue is that the

entrypoint

path in your

prefect.yaml

file must be relative to the root directory of your project. Given your directory structure, you should specify the

entrypoint

as:

insertion_model/main_using_prefect.py:main

Your

prefect.yaml

should have a section like this:

deployments:
  - name: your_deployment_name
    entrypoint: insertion_model/main_using_prefect.py:main
    ...

Ensure that the path provided in

entrypoint

accurately reflects the directory structure relative to the

prefect.yaml

file location. For more details, refer to the official documentation: https://docs.prefect.io/latest/guides/prefect-deploy/

@Marvin Its a k8s deployment so it will run some given image, does that mean I need to align same dir structure in both my project and the image?

Yes, you need to align the directory structures if you are using Kubernetes for deployment. The entrypoint path must match the directory structure within the image that Kubernetes will run. If, within your Docker image, the directory structure is different, you need to adjust the entrypoint accordingly. The file structure on your local machine must reflect what will be present in the Docker image when it is used by Kubernetes. For more information on Kubernetes deployment adjustments with Prefect, you can consult the deployment concept update: https://github.com/PrefectHQ/prefect/pull/10615

@Marvin when im building an image what should be the base image in the Dockerfile?

For Prefect deployments using Docker, you have a couple of options for the base image in your Dockerfile: 1. Use Prefect's Base Image: You can base your image off of a provided Prefect image if your flow relies on dependencies available in those images. Example:

FROM prefecthq/prefect:2-latest
RUN pip install scikit-learn

2. Custom Base Image: You can create a custom base Dockerfile if your requirements are different. Example:

FROM python:3.10
COPY ./requirements.txt /requirements.txt
RUN pip install -r /requirements.txt

For more detailed information, refer to the following guides: - Prefect Deployment Guide: https://docs.prefect.io/latest/guides/prefect-deploy/ - Prefect Docker Guide: https://docs.prefect.io/latest/guides/docker/ Choose the option that best fits your deployment needs.

im running on k8s job @Marvin

When running a Kubernetes job for Prefect, you can either use the default Prefect base image or create a custom Dockerfile based on your specific needs. Here is a recommended base Dockerfile for Kubernetes jobs: 1. Using Prefect Base Image: If your flow can leverage dependencies available in the Prefect base image, use:

FROM prefecthq/prefect:2-latest
RUN pip install -r /path/to/your/requirements.txt
COPY . /path/to/your/project

2. Custom Base Image: If you have custom dependencies, you can build your Dockerfile starting from a Python base image:

FROM python:3.10
COPY ./requirements.txt /requirements.txt
RUN pip install -r /requirements.txt
COPY . /path/to/your/project

Ensure your directory structure and paths in the

Dockerfile

match the entry point specified in your

prefect.yaml

. For more details, refer to the Kubernetes deployment guide: https://docs.prefect.io/latest/guides/deployment/kubernetes/ This guide contains useful information for configuring your Kubernetes jobs, such as setting the namespace, image pull policies, and managing completed jobs.

im getting /bin/sh: 0: cannot open prefect: No such file @Marvin

The error

/bin/sh: 0: cannot open prefect: No such file

typically indicates that the Prefect CLI is not installed or not available in the PATH within your Docker image. To resolve this, ensure you are installing Prefect in your Dockerfile. Here’s an example Dockerfile for a Kubernetes job with Prefect:

FROM python:3.10

# Install Prefect
RUN pip install prefect

# Copy your project files
COPY . /app
WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install -r requirements.txt

# Set the entrypoint
ENTRYPOINT ["prefect", "run", "-p"]

Ensure that you have a

requirements.txt

file including any additional dependencies:

prefect
# other dependencies

For further details, refer to the Kubernetes deployment guide: - Kubernetes deployment guide: https://docs.prefect.io/latest/guides/deployment/kubernetes/ If you encounter any issues, please refer to Prefect's GitHub discussions and issues for more specific troubleshooting.

this is what I have in my Dockerfile

The

ENTRYPOINT ["/bin/sh"]

line in your Dockerfile sets the entrypoint to

/bin/sh

, but it doesn't specify any script or command to run. This is likely why you're encountering the "cannot open prefect: No such file" error. Instead, you should specify the Prefect command as the entrypoint. Here’s an example Dockerfile that sets up Prefect for Kubernetes:

Dockerfile
FROM python:3.10

# Install Prefect
RUN pip install prefect

# Copy your project files
COPY . /app
WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install -r requirements.txt

# Set the entrypoint to run a Prefect flow
ENTRYPOINT ["prefect", "flow-run", "-p", "path/to/your/prefect-flow-file.yaml"]

### Explanation: 1. Base Image:

python:3.10

to have Python installed. 2. Install Prefect:

RUN pip install prefect

to install Prefect CLI. 3. Copy Project Files: Copies your project files into the Docker image. 4. Set Working Directory: Sets the working directory to

/app

. 5. Install Dependencies: Installs dependencies listed in

requirements.txt

. 6. Entry Point: Sets the entrypoint to run Prefect. Ensure the

path/to/your/prefect-flow-file.yaml

matches your file structure. Adjust the paths as needed for your project. For further details, you can consult the Prefect Kubernetes deployment guide: https://docs.prefect.io/latest/guides/deployment/kubernetes/

@Marvin tried prefect flow-run execute but it failed with Invalid value for ‘[ID]’: ‘prefect’ is not a valid UUID.

The error

Invalid value for '[ID]': 'prefect' is not a valid UUID

suggests that you need to provide a valid flow run ID (UUID) when using the

prefect flow-run execute

command. ### Correct Usage The correct command requires a valid flow run ID. Here’s an example:

bash
prefect flow-run execute <VALID_FLOW_RUN_ID>

1. Create a Flow Run: First, you need to create a flow run to obtain its ID.

bash
prefect deployment run <deployment_name>

2. Get the Flow Run ID: After creating the flow run, you can retrieve the flow run ID from the Prefect UI or by using the CLI:

bash
prefect flow-run ls

3. Execute the Flow Run: Now, use the obtained flow run ID to execute it.

bash
prefect flow-run execute <VALID_FLOW_RUN_ID>

Note: Ensure that your

PREFECT_API_KEY

and

PREFECT_API_URL

are correctly configured:

bash
prefect config view

For more details, you can refer to the following guides: - Connecting to Prefect Cloud: https://docs.prefect.io/latest/cloud/connecting/ - Flow Concepts: https://docs.prefect.io/latest/concepts/flows/ Make sure to replace

<VALID_FLOW_RUN_ID>

with the actual UUID of the flow run you obtained.

@Marvin why I dont need to do anything when im using this image as base prefecthq/prefect:2-python3.9-kubernetes

When you use the

prefecthq/prefect:2-python3.9-kubernetes

base image, it comes pre-configured with essential dependencies and configurations for running Prefect flows on Kubernetes. This includes: 1. Prefect Installed: The Prefect library and CLI are already installed. 2. Kubernetes Integration: The image is equipped with tools and libraries required to integrate smoothly with Kubernetes. 3. Optimized Configurations: Default configurations are set to optimize Prefect's performance and compatibility with Kubernetes environments. This pre-setup mitigates the need for additional installations and configurations, allowing you to focus directly on your flow logic and deployments. For further details, refer to the Kubernetes deployment guide: https://docs.prefect.io/latest/guides/deployment/kubernetes/

@Marvin so what is the best practice to create a Dockerfile and run it using k8s job

### Best Practices for Creating a Dockerfile and Running It Using Kubernetes Jobs with Prefect #### 1. Create a Dockerfile Here’s a sample Dockerfile using

prefecthq/prefect

as the base image, which is optimized for Kubernetes:

Dockerfile
# Use Prefect base image for Kubernetes
FROM prefecthq/prefect:2-python3.9-kubernetes

# Set the working directory
WORKDIR /app

# Copy the requirements file and install dependencies
COPY requirements.txt .
RUN pip install -r requirements.txt

# Copy the rest of the application files
COPY . .

# Set the entrypoint to run Prefect commands
ENTRYPOINT ["prefect", "flow-run", "-p", "/app/flows/your_flow_file.py"]

#### 2. Build and Push the Docker Image Build the Docker image:

bash
docker build -t your-docker-repo/your-image-name:tag .

Push the image to your Docker repository:

bash
docker push your-docker-repo/your-image-name:tag

#### 3. Configure Kubernetes Job Create a Kubernetes job manifest file (job.yaml):

yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: prefect-job
spec:
  template:
    spec:
      containers:
      - name: prefect-container
        image: your-docker-repo/your-image-name:tag
        env:
        - name: PREFECT_API_KEY
          valueFrom:
            secretKeyRef:
              name: prefect-api-key
              key: YOUR_API_KEY
        command: ["prefect", "deployment", "run", "your-deployment-name"]
      restartPolicy: Never

#### 4. Deploy the Job to Kubernetes Apply the job manifest:

bash
kubectl apply -f job.yaml

### Additional Tips 1. Namespace: Ensure you set the Kubernetes namespace to create jobs within, such as

prefect

. By default, it's set to

default

. 2. Image Pull Policy: Select from the dropdown options to specify when to pull the image. Make sure to use unique image tags if using the

IfNotPresent

policy to avoid old images getting cached. 3. Environment Variables: Securely manage and use environment variables like

PREFECT_API_KEY

for accessing Prefect Cloud. 4. Finished Job TTL: Set a time-to-live (TTL) for finished jobs to automatically clean them up after completion to manage resources effectively. For more detailed guidance, refer to the official Prefect Kubernetes deployment documentation: https://docs.prefect.io/latest/guides/deployment/kubernetes/ These steps and best practices should help you efficiently create Docker images and run them as Kubernetes jobs with Prefect.

@Marvin how can I add annotation to the k8s job?

To add annotations to a Kubernetes job in your manifest, you can include the

annotations

field under the

metadata

section of the job definition. Here is an example of a Kubernetes job manifest with annotations:

yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: prefect-job
  annotations:
    <http://example.com/annotation-key|example.com/annotation-key>: "annotation-value"
spec:
  template:
    metadata:
      annotations:
        <http://example.com/template-annotation-key|example.com/template-annotation-key>: "template-annotation-value"
    spec:
      containers:
      - name: prefect-container
        image: your-docker-repo/your-image-name:tag
        env:
        - name: PREFECT_API_KEY
          valueFrom:
            secretKeyRef:
              name: prefect-api-key
              key: YOUR_API_KEY
        command: ["prefect", "deployment", "run", "your-deployment-name"]
      restartPolicy: Never

### Explanation: - Job-Level Annotations: These are specified under

metadata

. - Pod-Level Annotations: If you need annotations on the pod template itself (within the job), include them under

template.metadata

. ### Applying the Job To apply this job manifest with the annotations, run:

bash
kubectl apply -f job.yaml

These annotations can be useful for adding metadata, managing logging, monitoring, and other Kubernetes-native functionalities. For more advanced configurations and additional details, you can refer to the Prefect Kubernetes integration guide: - Prefect Kubernetes Worker: https://docs.prefect.io/latest/integrations/prefect-kubernetes/worker/ - Prefect Kubernetes Jobs: https://docs.prefect.io/latest/integrations/prefect-kubernetes/jobs/ These guides will offer further insights and options for customized Kubernetes job configurations.

can you show me this on the work-pool job template @Marvin

To add annotations to a Kubernetes job in a Prefect work-pool job template, you can update the base job template JSON to include the

annotations

field. Here's an example: ### Base Job Template JSON

json
{
  "job_configuration": {
    "apiVersion": "batch/v1",
    "kind": "Job",
    "metadata": {
      "annotations": {
        "<http://example.com/job-annotation-key|example.com/job-annotation-key>": "job-annotation-value"
      }
    },
    "spec": {
      "template": {
        "metadata": {
          "annotations": {
            "<http://example.com/pod-annotation-key|example.com/pod-annotation-key>": "pod-annotation-value"
          }
        },
        "spec": {
          "containers": [
            {
              "name": "prefect-container",
              "image": "your-docker-repo/your-image-name:tag",
              "env": [
                {
                  "name": "PREFECT_API_KEY",
                  "valueFrom": {
                    "secretKeyRef": {
                      "name": "prefect-api-key",
                      "key": "YOUR_API_KEY"
                    }
                  }
                }
              ],
              "command": ["prefect", "deployment", "run", "your-deployment-name"]
            }
          ],
          "restartPolicy": "Never"
        }
      }
    }
  },
  "variables": {
    // other variables if any
  }
}

### Updating the Work Pool To update your work pool with this base job template, save the above JSON to a file, say

base-job-template.json

, and then run:

bash
prefect work-pool update --base-job-template base-job-template.json my-work-pool

Replace

my-work-pool

with the name of your work pool. ### Applying the Job Template This will update the work pool’s base job template to include the specified annotations. Any Kubernetes Job created by this work pool will now have the annotations specified. For more detailed guidance, refer to the Prefect documentation on work-pools: - Prefect Work Pools: https://docs.prefect.io/latest/concepts/work-pools/ This approach helps you manage job annotations effectively while using Prefect's work pools for Kubernetes deployments.

@Marvin what im missing here? { “variables”: { “type”: “object”, “properties”: { “env”: { “type”: “object”, “title”: “Environment Variables”, “description”: “Environment variables to set when starting a flow run.“, “additionalProperties”: { “type”: “string” } }, “name”: { “type”: “string”, “title”: “Name”, “description”: “Name given to infrastructure created by a worker.” }, “image”: { “type”: “string”, “title”: “Image”, “example”: “docker.io/prefecthq/prefect:2-latest”, “description”: “The image reference of a container image to use for created jobs. If not set, the latest Prefect image will be used.” }, “labels”: { “type”: “object”, “title”: “Labels”, “description”: “Labels applied to infrastructure created by a worker.“, “additionalProperties”: { “type”: “string” } }, “command”: { “type”: “string”, “title”: “Command”, “description”: “The command to use when starting a flow run. In most cases, this should be left blank and the command will be automatically generated by the worker.” }, “cpu_limit”: { “type”: “string”, “title”: “cpu_limit”, “description”: “cpu_limit” }, “namespace”: { “type”: “string”, “title”: “Namespace”, “default”: “algo-prefect”, “description”: “The Kubernetes namespace to create jobs within.” }, “cpu_request”: { “type”: “string”, “title”: “cpu_request”, “description”: “cpu_request” }, “memory_limit”: { “type”: “string”, “title”: “memory_limit”, “description”: “memory_limit” }, “stream_output”: { “type”: “boolean”, “title”: “Stream Output”, “default”: true, “description”: “If set, output will be streamed from the job to local standard output.” }, “cluster_config”: { “allOf”: [ { “$ref”: “#/definitions/KubernetesClusterConfig” } ], “title”: “Cluster Config”, “description”: “The Kubernetes cluster config to use for job creation.” }, “memory_request”: { “type”: “string”, “title”: “memory_request”, “description”: “memory_request” }, “finished_job_ttl”: { “type”: “integer”, “title”: “Finished Job TTL”, “default”: 100, “description”: “The number of seconds to retain jobs after completion. If set, finished jobs will be cleaned up by Kubernetes after the given delay. If not set, jobs will be retained indefinitely.” }, “image_pull_policy”: { “enum”: [ “IfNotPresent”, “Always”, “Never” ], “type”: “string”, “title”: “Image Pull Policy”, “default”: “IfNotPresent”, “description”: “The Kubernetes image pull policy to use for job containers.” }, “service_account_name”: { “type”: “string”, “title”: “Service Account Name”, “description”: “The Kubernetes service account to use for job creation.” }, “job_watch_timeout_seconds”: { “type”: “integer”, “title”: “Job Watch Timeout Seconds”, “description”: “Number of seconds to wait for each event emitted by a job before timing out. If not set, the worker will wait for each event indefinitely.” }, “pod_watch_timeout_seconds”: { “type”: “integer”, “title”: “Pod Watch Timeout Seconds”, “default”: 60, “description”: “Number of seconds to watch for pod creation before timing out.” } }, “definitions”: { “KubernetesClusterConfig”: { “type”: “object”, “title”: “KubernetesClusterConfig”, “required”: [ “config”, “context_name” ], “properties”: { “config”: { “type”: “object”, “title”: “Config”, “description”: “The entire contents of a kubectl config file.” }, “context_name”: { “type”: “string”, “title”: “Context Name”, “description”: “The name of the kubectl context to use.” } }, “secret_fields”: [], “block_type_slug”: “kubernetes-cluster-config”, “block_schema_references”: {} } } }, “job_configuration”: { “env”: “{{ env }}“, “name”: “{{ name }}“, “labels”: “{{ labels }}“, “command”: “{{ command }}“, “namespace”: “{{ namespace }}“, “job_manifest”: { “kind”: “Job”, “spec”: { “template”: { “spec”: { “containers”: [ { “env”: “{{ env }}“, “args”: “{{ command }}“, “name”: “prefect-job”, “image”: “{{ image }}“, “resources”: { “limits”: { “cpu”: “{{ cpu_limit }}“, “memory”: “{{ memory_limit }}” }, “requests”: { “cpu”: “{{ cpu_request }}“, “memory”: “{{ memory_request }}” } }, “imagePullPolicy”: “{{ image_pull_policy }}” } ], “completions”: 1, “parallelism”: 1, “restartPolicy”: “Never”, “serviceAccountName”: “{{ service_account_name }}” } }, “backoffLimit”: 0, “ttlSecondsAfterFinished”: “{{ finished_job_ttl }}” }, “metadata”: { “labels”: “{{ labels }}“, “namespace”: “{{ namespace }}“, “annotations”: { “eks.amazonaws.com/role-arn”: “arnawsiam:091061493057role/algo-prefect-dev-use1” }, “generateName”: “{{ name }}-” }, “apiVersion”: “batch/v1” }, “stream_output”: “{{ stream_output }}“, “cluster_config”: “{{ cluster_config }}“, “job_watch_timeout_seconds”: “{{ job_watch_timeout_seconds }}“, “pod_watch_timeout_seconds”: “{{ pod_watch_timeout_seconds }}” } }

Your message was too long by 1224 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:

what im missing here?

{ “variables”: { “type”: “object”, “properties”: { “env”: { “type”: “object”, “title”: “Environment Variables”, “description”: “Environment variables to set when starting a flow run.“, “additionalProperties”: { “type”: “string” } }, “name”: { “type”: “string”, “title”: “Name”, “description”: “Name given to infrastructure created by a worker.” }, “image”: { “type”: “string”, “title”: “Image”, “example”: “docker.io/prefecthq/prefect:2-latest”, “description”: “The image reference of a container image to use for created jobs. If not set, the latest Prefect image will be used.” }, “labels”: { “type”: “object”, “title”: “Labels”, “description”: “Labels applied to infrastructure created by a worker.“, “additionalProperties”: { “type”: “string” } }, “command”: { “type”: “string”, “title”: “Command”, “description”: “The command to use when starting a flow run. In most cases

@Marvin what im missing here “job_configuration”: { “env”: “{{ env }}“, “name”: “{{ name }}“, “labels”: “{{ labels }}“, “command”: “{{ command }}“, “namespace”: “{{ namespace }}“, “job_manifest”: { “kind”: “Job”, “spec”: { “template”: { “spec”: { “containers”: [ { “env”: “{{ env }}“, “args”: “{{ command }}“, “name”: “prefect-job”, “image”: “{{ image }}“, “resources”: { “limits”: { “cpu”: “{{ cpu_limit }}“, “memory”: “{{ memory_limit }}” }, “requests”: { “cpu”: “{{ cpu_request }}“, “memory”: “{{ memory_request }}” } }, “imagePullPolicy”: “{{ image_pull_policy }}” } ], “completions”: 1, “parallelism”: 1, “restartPolicy”: “Never”, “serviceAccountName”: “{{ service_account_name }}” } }, “backoffLimit”: 0, “ttlSecondsAfterFinished”: “{{ finished_job_ttl }}” }, “metadata”: { “labels”: “{{ labels }}“, “namespace”: “{{ namespace }}“, “annotations”: { “eks.amazonaws.com/role-arn”: “arnawsiam:091061493057role/algo-prefect-dev-use1” }, “generateName”: “{{ name }}-” }, “apiVersion”: “batch/v1” }, “stream_output”: “{{ stream_output }}“, “cluster_config”: “{{ cluster_config }}“, “job_watch_timeout_seconds”: “{{ job_watch_timeout_seconds }}“, “pod_watch_timeout_seconds”: “{{ pod_watch_timeout_seconds }}” }

Your message was too long by 142 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:

what im missing here

“job_configuration”: { “env”: “{{ env }}“, “name”: “{{ name }}“, “labels”: “{{ labels }}“, “command”: “{{ command }}“, “namespace”: “{{ namespace }}“, “job_manifest”: { “kind”: “Job”, “spec”: { “template”: { “spec”: { “containers”: [ { “env”: “{{ env }}“, “args”: “{{ command }}“, “name”: “prefect-job”, “image”: “{{ image }}“, “resources”: { “limits”: { “cpu”: “{{ cpu_limit }}“, “memory”: “{{ memory_limit }}” }, “requests”: { “cpu”: “{{ cpu_request }}“, “memory”: “{{ memory_request }}” } }, “imagePullPolicy”: “{{ image_pull_policy }}” } ], “completions”: 1, “parallelism”: 1, “restartPolicy”: “Never”, “serviceAccountName”: “{{ service_account_name }}” } }, “backoffLimit”: 0, “ttlSecondsAfterFinished”: “{{ finished_job_ttl }}” }, “metadata”: { “labels”: “{{ labels }}“,

@Marvin “job_manifest”: { “kind”: “Job”, “spec”: { “template”: { “spec”: { “containers”: [ { “env”: “{{ env }}“, “args”: “{{ command }}“, “name”: “prefect-job”, “image”: “{{ image }}“, “resources”: { “limits”: { “cpu”: “{{ cpu_limit }}“, “memory”: “{{ memory_limit }}” }, “requests”: { “cpu”: “{{ cpu_request }}“, “memory”: “{{ memory_request }}” } }, “imagePullPolicy”: “{{ image_pull_policy }}” } ], “completions”: 1, “parallelism”: 1, “restartPolicy”: “Never”, “serviceAccountName”: “{{ service_account_name }}” } }, “backoffLimit”: 0, “ttlSecondsAfterFinished”: “{{ finished_job_ttl }}” }, “metadata”: { “labels”: “{{ labels }}“, “namespace”: “{{ namespace }}“, “annotations”: { “eks.amazonaws.com/role-arn”: “arnawsiam:091061493057role/algo-prefect-dev-use1” }, “generateName”: “{{ name }}-” },

Your message was too long by 12 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:

“job_manifest”: {

“kind”: “Job”, “spec”: { “template”: { “spec”: { “containers”: [ { “env”: “{{ env }}“, “args”: “{{ command }}“, “name”: “prefect-job”, “image”: “{{ image }}“, “resources”: { “limits”: { “cpu”: “{{ cpu_limit }}“, “memory”: “{{ memory_limit }}” }, “requests”: { “cpu”: “{{ cpu_request }}“, “memory”: “{{ memory_request }}” } }, “imagePullPolicy”: “{{ image_pull_policy }}” } ], “completions”: 1, “parallelism”: 1, “restartPolicy”: “Never”, “serviceAccountName”: “{{ service_account_name }}” } }, “backoffLimit”: 0, “ttlSecondsAfterFinished”: “{{ finished_job_ttl }}” }, “metadata”: { “labels”: “{{ labels }}“, “namespace”: “{{ namespace }}“, “annotations”: { “eks.amazonaws.com/role-arn”: “arnawsiam:091061493057role/algo-prefect-dev-use1” },

@Marvin “job_manifest”: { “kind”: “Job”, “spec”: { “template”: { “spec”: { “containers”: [ { “imagePullPolicy”: “{{ image_pull_policy }}” } ], “completions”: 1, “parallelism”: 1, “restartPolicy”: “Never”, “serviceAccountName”: “{{ service_account_name }}” } }, “backoffLimit”: 0, “ttlSecondsAfterFinished”: “{{ finished_job_ttl }}” }, “metadata”: { “labels”: “{{ labels }}“, “namespace”: “{{ namespace }}“, “annotations”: { “eks.amazonaws.com/role-arn”: “arnawsiam:091061493057role/algo-prefect-dev-use1” }, “generateName”: “{{ name }}-” },

Great! It looks like you have a structure for a work-pool job manifest with annotations. To complete it, I'll format the full job manifest for you, including additional placeholders that can be configured as per your requirements. ### Full Job Manifest with Annotations

json
{
  "job_configuration": {
    "kind": "Job",
    "metadata": {
      "generateName": "{{ name }}-",
      "namespace": "{{ namespace }}",
      "labels": "{{ labels }}",
      "annotations": {
        "<http://eks.amazonaws.com/role-arn|eks.amazonaws.com/role-arn>": "arn:aws:iam::091061493057:role/algo-prefect-dev-use1"
      }
    },
    "spec": {
      "completions": 1,
      "parallelism": 1,
      "backoffLimit": 0,
      "ttlSecondsAfterFinished": "{{ finished_job_ttl }}",
      "template": {
        "metadata": {
          "annotations": {
            "<http://eks.amazonaws.com/role-arn|eks.amazonaws.com/role-arn>": "arn:aws:iam::091061493057:role/algo-prefect-dev-use1"
          }
        },
        "spec": {
          "containers": [
            {
              "name": "prefect-container",
              "image": "your-docker-repo/your-image-name:tag",
              "imagePullPolicy": "{{ image_pull_policy }}",
              "env": [
                {
                  "name": "PREFECT_API_KEY",
                  "valueFrom": {
                    "secretKeyRef": {
                      "name": "prefect-api-key",
                      "key": "YOUR_API_KEY"
                    }
                  }
                }
              ],
              "command": ["prefect", "deployment", "run", "your-deployment-name"]
            }
          ],
          "restartPolicy": "Never",
          "serviceAccountName": "{{ service_account_name }}"
        }
      }
    }
  },
  "variables": {
    // other variables if any
  }
}

### Explanation: - Annotations: Added to both the job’s metadata and the pod’s metadata within the template. - Placeholders: Defined using Jinja templates (e.g.,

{{ name }}

,

{{ namespace }}

) for dynamic substitution. ### Applying the Configuration Save this JSON configuration to a file, say

base-job-template.json

, and update your work pool using the command:

bash
prefect work-pool update --base-job-template base-job-template.json my-work-pool

Adjust the placeholders and specific values as needed for your environment. This will ensure your Kubernetes jobs created by Prefect’s work pool are annotated properly. For additional information, check the Prefect work pools documentation: https://docs.prefect.io/latest/concepts/work-pools/