@Marvin I have k8s job template im using as part of k8s workpool, How can I add to the pool or the job aws s3 block

To add an AWS S3 block to your Kubernetes job template in a Prefect work pool, you'll need to follow these steps for either Prefect 2.x or 3.x. ### Prefect 2.x: 1. Configure Your Kubernetes Job Template: Modify your job template to include the AWS S3 block configuration. You can find an example template here. 2. Use AWSCredentials and S3Bucket Block: - Create and save an

AWSCredentials

block. - Create and save an

S3Bucket

block using the

AWSCredentials

block. Example:

python
from prefect_aws import S3Bucket, AWSCredentials

aws_creds = AWSCredentials(
    aws_access_key_id="YOUR_ACCESS_KEY",
    aws_secret_access_key="YOUR_SECRET_KEY"
)
aws_creds.save("my_aws_credentials")

s3_bucket = S3Bucket(
    bucket_name="my_s3_bucket",
    credentials=AWSCredentials.load("my_aws_credentials")
)
s3_bucket.save("my_s3_bucket")

You can use these blocks in your flow to interact with S3. More details can be found here. ### Prefect 3.x: 1. Setup Work Pool Configuration: For configuring Kubernetes work pools and job templates, refer to the guide here. 2. Create Blocks: - Follow similar steps as in Prefect 2.x to create and save your blocks. Example:

python
from prefect_aws import S3Bucket, AWSCredentials

aws_creds = AWSCredentials(
    aws_access_key_id="YOUR_ACCESS_KEY",
    aws_secret_access_key="YOUR_SECRET_KEY"
)
aws_creds.save("my_aws_credentials")

s3_bucket = S3Bucket(
    bucket_name="my_s3_bucket",
    credentials=AWSCredentials.load("my_aws_credentials")
)
s3_bucket.save("my_s3_bucket")

3. Modify Kubernetes Configuration: Add CPU requests or other needed configurations under the

containers

section, as detailed here. For additional configurations and details on setting up blocks, you can check the documentation for blocks in Prefect 3.x here. If you have further questions or need more detailed guidance, feel free to ask!

@Marvin I'm running using prefect-server which I deployed on my aws eks cluster, I'm using IAM role annotations on the different prefect pods in order to get access to different aws resources. Do I still need to provide creds in order for prefect to use my bucket as storage?

If you're using IAM role annotations on your EKS pods, these roles should provide the necessary permissions for accessing AWS resources, such as S3 buckets, without the need to explicitly provide credentials in your Prefect configurations. Here is how you can proceed: ### Prefect 2.x: Ensure that your Kubernetes pods have the correct IAM roles attached. Prefect will use the IAM roles to access the S3 bucket without needing explicit AWS credentials in the Prefect block configurations. However, if you still want to configure it using Prefect blocks for any reasons, you can follow these steps: 1. Create S3 and AWSCredentials Blocks: Create the blocks without providing credentials explicitly, as they will be auto-resolved by the IAM role associated with the pods. Example:

python
from prefect_aws import S3Bucket, AWSCredentials

# No need to provide AWS access key and secret if using IAM roles
s3_bucket = S3Bucket(bucket_name="my_s3_bucket").save("my_s3_bucket")

For more information, see Prefect S3 Integration.

### Prefect 3.x: The approach is quite similar. Ensure your Kubernetes cluster is correctly configured with the necessary IAM roles. 1. Using S3Bucket Block: - Create S3Bucket block without providing explicit credentials. Example:

python
from prefect_aws import S3Bucket

# Credentials are automatically resolved via IAM roles on EKS
s3_bucket = S3Bucket(bucket_name="my_s3_bucket").save("my_s3_bucket")

For details, refer to the related example in Prefect 3.x documentation.

In both versions, the key is to ensure your EKS pods have the necessary IAM roles. Then, Prefect will manage access based on these roles. If you need more detailed advice or run into specific issues, feel free to ask!

@Marvin can you show me an example for k8s job template with the s3 bucket block?

To include an S3 bucket block within a Kubernetes job template in Prefect, you will define the S3 bucket block and reference it in your job template. Below is an example for both Prefect 2.x and 3.x. ### Prefect 2.x Kubernetes Job Template Example: 1. Define and Save S3 Bucket Block:

python
   from prefect_aws import S3Bucket

   # Define the S3 bucket block without credentials, as IAM roles are used
   s3_bucket = S3Bucket(bucket_name="my_s3_bucket").save("my_s3_bucket")

2. Kubernetes Job Template: Here’s an example job template with S3 bucket integration. Modify the job spec as needed.

yaml
   apiVersion: batch/v1
   kind: Job
   metadata:
     labels: "your-labels"
     namespace: "your-namespace"
     generateName: "your-job-name-"
   spec:
     ttlSecondsAfterFinished: 300
     template:
       spec:
         parallelism: 1
         completions: 1
         restartPolicy: Never
         serviceAccountName: "your-service-account"
         containers:
           - name: your-container-name
             image: your-image
             env:
               # Use environment variables to pass block references if needed
               - name: PREFECT__CONTEXT__S3_BUCKET
                 value: "my_s3_bucket"
             resources:
               requests:
                 cpu: "500m"
                 memory: "512Mi"
               limits:
                 cpu: "1000m"
                 memory: "1024Mi"

### Prefect 3.x Kubernetes Job Template Example: 1. Define and Save S3 Bucket Block:

python
   from prefect_aws import S3Bucket

   # Define the S3 bucket block without credentials, as IAM roles are used
   s3_bucket = S3Bucket(bucket_name="my_s3_bucket").save("my_s3_bucket")

2. Kubernetes Job Template: Modify the Kubernetes job template JSON to include settings specific to your use case.

json
   {
     "apiVersion": "batch/v1",
     "kind": "Job",
     "metadata": {
       "labels": "{{ labels }}",
       "namespace": "{{ namespace }}",
       "generateName": "{{ name }}-"
     },
     "spec": {
       "ttlSecondsAfterFinished": "{{ finished_job_ttl }}",
       "template": {
         "spec": {
           "parallelism": 1,
           "completions": 1,
           "restartPolicy": "Never",
           "serviceAccountName": "your-service-account",
           "containers": [
             {
               "name": "your-container-name",
               "image": "your-image",
               "env": [
                 {
                   "name": "PREFECT__CONTEXT__S3_BUCKET",
                   "value": "my_s3_bucket"
                 }
               ],
               "resources": {
                 "requests": {
                   "cpu": "500m",
                   "memory": "512Mi"
                 },
                 "limits": {
                   "cpu": "1000m",
                   "memory": "1024Mi"
                 }
               }
             }
           ]
         }
       }
     }
   }

In the job template, substitute

"your-service-account"

,

"your-container-name"

, and

"your-image"

with appropriate values for your use case. The

env

variable

PREFECT__CONTEXT__S3_BUCKET

is an example of how you might pass the S3 bucket block reference to the job. For more information, refer to the Prefect 2.x documentation and Prefect 3.x documentation. If you need further assistance or run into any issues, feel free to ask!

@Marvin so prefect doesn't utilize the s3 automatically, I need to reference it in my code and save or load the data?

Yes, that's correct. Prefect doesn't automatically utilize the S3 bucket; you need to explicitly reference it and write or read data within your flow code. Here’s a step-by-step guide on how you can achieve this: ### Steps: 1. Define and Save the S3 Bucket Block: First, create and save the S3 bucket block. This step only needs to be done once until you need to update configs.

python
   from prefect_aws import S3Bucket

   # Define the S3 bucket block without credentials, as IAM roles are used
   s3_bucket = S3Bucket(bucket_name="my_s3_bucket").save("my_s3_bucket")

2. Reference and Use the Block in Your Flow Code: When you want to interact with the S3 bucket in a flow, load the block and use its methods to read/write from/to the bucket.

python
   from prefect import flow, task
   from prefect_aws import S3Bucket

   @task
   def upload_data_to_s3(data: bytes, key: str):
       s3_bucket = S3Bucket.load("my_s3_bucket")
       s3_bucket.write(key=key, data=data)

   @task
   def download_data_from_s3(key: str) -> bytes:
       s3_bucket = S3Bucket.load("my_s3_bucket")
       return s3_bucket.read(key=key)

   @flow
   def my_flow():
       # Example data to upload
       data = b"Hello, Prefect S3!"
       key = "example/key.txt"

       upload_data_to_s3(data, key)

       # Download and print the data
       downloaded_data = download_data_from_s3(key)
       print(downloaded_data.decode())

   if __name__ == "__main__":
       my_flow()

### Explanation: 1. Define and Save Block: The block is defined and saved once using the

S3Bucket

class, which contains the bucket configuration. When using IAM roles, the credentials are automatically resolved. 2. Tasks to Upload and Download Data: - `upload_data_to_s3`: This task uploads data to a specified key in the S3 bucket. - `download_data_from_s3`: This task downloads data from a specified key in the S3 bucket. 3. Flow Definition: In your flow, you call these tasks to upload data and download it back from S3. By defining these tasks and using blocks, you ensure that your code is modular and the S3 interactions are isolated. Prefect’s block mechanism helps manage these configurations centrally and securely. For additional guidance on using blocks in Prefect, you can refer to: - Prefect 2.x S3 Documentation - Prefect 3.x Block Usage Feel free to ask if you need any more help!

@Marvin so why not use boto3?

Using Prefect's S3 bucket block provides several advantages over using

boto3

directly, especially in the context of Prefect flows. Here are some reasons why you might prefer using Prefect's S3 bucket block: ### Reasons to Use Prefect's S3 Bucket Block: 1. Modularity and Reusability: - Prefect blocks encapsulate configuration and connection details, making them easily reusable across multiple tasks and flows. - Blocks can be saved and loaded by name, promoting DRY (Don't Repeat Yourself) principles. 2. Centralized Configuration: - Blocks allow you to centralize configuration and credentials, which can be managed and audited separately from your code. - Updating a block configuration (e.g., changing the S3 bucket name) does not require changes to individual flows. 3. Security: - Prefect's block system provides a mechanism for securely handling sensitive information, such as AWS credentials, by integrating with Prefect Secret and other secure storage options. - IAM roles and Prefect’s Secrets management provide an additional layer of security. 4. Integration with Prefect's Environment: - Prefect blocks are designed to integrate seamlessly with Prefect's orchestration, scheduling, and monitoring features. - Prefect's UI/CLI can manage and visualize blocks, making it easier to handle infrastructure configurations. ### When to Use boto3 Directly: 1. Fine-Grained Control: - If you need very specific S3 operations or configurations not encapsulated by Prefect's S3 block,

boto3

provides direct access to AWS API features. 2. Complex Operations: - For complex operations or services not supported by Prefect blocks,

boto3

is necessary. ### Example of Using

boto3

in Prefect: If you decide that

boto3

better suits your needs, here's how you can integrate it within a Prefect flow, leveraging IAM roles for authentication: #### Using boto3 Directly:

python
import boto3
from prefect import flow, task

@task
def upload_to_s3(bucket_name: str, key: str, data: bytes):
    # Using boto3 client with IAM role authentication
    s3 = boto3.client("s3")
    s3.put_object(Bucket=bucket_name, Key=key, Body=data)

@task
def download_from_s3(bucket_name: str, key: str) -> bytes:
    # Using boto3 client with IAM role authentication
    s3 = boto3.client("s3")
    response = s3.get_object(Bucket=bucket_name, Key=key)
    return response['Body'].read()

@flow
def my_flow():
    bucket_name = "my-s3-bucket"
    key = "example/key.txt"
    data = b"Hello, boto3 with Prefect!"

    # Upload data to S3
    upload_to_s3(bucket_name, key, data)

    # Download and print data from S3
    downloaded_data = download_from_s3(bucket_name, key)
    print(downloaded_data.decode())

if __name__ == "__main__":
    my_flow()

### Conclusion: Choosing between Prefect's S3 block and

boto3

depends on your specific use case and the benefits you want to leverage. Prefect's block system offers modularity, centralized configuration, and security enhancements, which integrate well within the Prefect ecosystem. However,

boto3

provides direct access to AWS services if you need more control or specific functionalities. Feel free to ask if you need more detailed guidance or if there's any specific functionality you're trying to achieve!

@Marvin created a s3 block getting this when trying to load 155143.418 | ERROR | GlobalEventLoopThread | prefect._internal.concurrency - Service ‘EventsWorker’ failed with 1 pending items.

The error you're encountering,

GlobalEventLoopThread | prefect._internal.concurrency - Service 'EventsWorker' failed with 1 pending items.

, has been reported by other users. It seems to be a known issue, particularly after upgrading to Prefect 3.x, and might not directly impact the functionality but could cause other downstream issues or task visibility problems. ### Troubleshooting Steps: 1. Ensure Compatibility: Ensure that all Prefect packages and dependencies are compatible with the version you are using. Sometimes upgrading or downgrading Prefect might resolve the issue. 2. Initialize Prefect Blocks Properly: Make sure that you're properly initializing and loading the S3 block in your flow code. 3. Check Prefect Server and Agent Logs: Monitor the Prefect server and agent logs to gather more insights into what might be causing the error. ### Example Code to Enrich Logs and Test Block Initialization: Here's an enriched version of your flow to provide more debugging information:

python
import boto3
import logging
from prefect import flow, task
from prefect_aws import S3Bucket

logging.basicConfig(level=<http://logging.INFO|logging.INFO>)

@task
def initialize_s3_bucket_block():
    try:
        # Load the block
        s3_bucket = S3Bucket.load("my_s3_bucket")
        <http://logging.info|logging.info>("Successfully loaded S3 bucket block.")
        return s3_bucket
    except Exception as e:
        logging.error(f"Error loading S3 bucket block: {e}")
        raise

@task
def upload_data_to_s3(s3_bucket, key: str, data: bytes):
    try:
        s3_bucket.write(key=key, data=data)
        <http://logging.info|logging.info>("Successfully uploaded data to S3.")
    except Exception as e:
        logging.error(f"Error uploading data to S3: {e}")
        raise

@task
def download_data_from_s3(s3_bucket, key: str) -> bytes:
    try:
        data = s3_bucket.read(key=key)
        <http://logging.info|logging.info>("Successfully downloaded data from S3.")
        return data
    except Exception as e:
        logging.error(f"Error downloading data from S3: {e}")
        raise

@flow
def my_flow():
    s3_bucket = initialize_s3_bucket_block()
    data = b"Hello, Prefect with S3!"
    key = "example/key.txt"

    upload_data_to_s3(s3_bucket, key, data)
    downloaded_data = download_data_from_s3(s3_bucket, key)
    print(downloaded_data.decode())

if __name__ == "__main__":
    my_flow()

### Known Issue and Workaround: According to known issues and task run visibility issues, users encountered similar

GlobalEventLoopThread

errors and had success downgrading Prefect to resolve the issues temporarily. 1. Prefect 3.x Known Issue: Users have reported that downgrading Prefect to an earlier version (e.g., 3.0.0rc18) has helped resolve the issues. Consider testing this workaround.

bash
   pip install prefect==3.0.0rc18

2. Prefect 2.x Usage: If you're open to using Prefect 2.x, you might not encounter this issue. Ensure that you modify your code according to the compatible APIs and blocks. ### Summary: - Ensure all dependencies are compatible with your Prefect version. - Properly initialize and handle Prefect blocks within your flow. - Monitor Prefect server and agent logs for more details. - Consider downgrading to an earlier version of Prefect if using 3.x, or use Prefect 2.x as an alternative. For further assistance, you might want to track the issues on the Prefect GitHub repository and report any new findings or patterns you observe. If you need more help, feel free to ask!

@Marvin whihc prefect image do I need to use for my k8s prefect worker for is to use prefect-k8s and prefect-aws. (im using prefect 2

For running a Prefect worker on Kubernetes (Prefect 2.x), you'll need an image that includes both

prefect-kubernetes

and

prefect-aws

to ensure that the Prefect worker can interact with both Kubernetes and AWS services. ### Recommended Docker Image: You can start with the official Prefect Docker image and extend it to include the necessary packages. Here is how you can create a custom Docker image: 1. Create a Dockerfile: Here’s an example of a Dockerfile that includes

prefect-kubernetes

and

prefect-aws

with Prefect 2.x:

dockerfile
   # Start with the official Prefect 2.x image
   FROM prefecthq/prefect:2.7.7-python3.9

   # Install the necessary packages
   RUN pip install prefect-kubernetes prefect-aws

   # You can also set an environment variable for the Prefect API key if needed
   # ENV PREFECT_API_KEY=your_prefect_api_key

2. Build and Push the Docker Image: Build the Docker image and push it to your container registry.

sh
   # Build the image
   docker build -t your-registry/prefect-k8s-aws:latest .

   # Push the image to your container registry
   docker push your-registry/prefect-k8s-aws:latest

3. Update Your Kubernetes Worker Configuration: Use the custom image in your Kubernetes worker configuration when you deploy it. 4. Example Kubernetes Job Template: Update your Kubernetes job template to use the custom Docker image.

yaml
   apiVersion: batch/v1
   kind: Job
   metadata:
     labels: "{{ labels }}"
     namespace: "{{ namespace }}"
     generateName: "{{ name }}-"
   spec:
     ttlSecondsAfterFinished: "{{ finished_job_ttl }}"
     template:
       spec:
         parallelism: 1
         completions: 1
         restartPolicy: Never
         serviceAccountName: "{{ service_account_name }}"
         containers:
           - name: prefect-job
             image: your-registry/prefect-k8s-aws:latest
             env:
               - name: PREFECT__CONTEXT__S3_BUCKET
                 value: "my_s3_bucket"
               # Other environment variables as needed
             resources:
               requests:
                 memory: "{{ memory }}Mi"
               limits:
                 cpu: "500m"
                 memory: "512Mi"
         serviceAccountName: "your-service-account"  # Replace with your service account

### Running the Kubernetes Worker: Ensure that your worker is set up to use this custom image:

sh
prefect worker start --pool 'my-work-pool' --type kubernetes

For more specific details, you can refer to the Prefect Kubernetes Worker documentation and the Kubernetes deployment guide. If you encounter any issues or need further assistance, feel free to ask!

so there isnt an official prefect image whihc contain both packages @Marvin

There isn't a single official Prefect image that contains both the

prefect-kubernetes

and

prefect-aws

packages pre-installed. Prefect provides a base image, which you can extend to include the additional packages you need. ### Creating a Custom Docker Image To create a custom Docker image that includes both the

prefect-kubernetes

and

prefect-aws

packages, follow these steps: 1. Create a Dockerfile: This Dockerfile extends the official Prefect base image to include the additional required packages.

dockerfile
   # Use the official Prefect base image
   FROM prefecthq/prefect:2.7.7-python3.9

   # Install the prefect-kubernetes and prefect-aws packages
   RUN pip install prefect-kubernetes prefect-aws

   # Optionally, set environment variables if needed
   # ENV PREFECT_API_KEY=your_prefect_api_key

2. Build Your Docker Image: Open a terminal, navigate to the folder containing your Dockerfile, and build the image.

sh
   docker build -t your-registry/prefect-k8s-aws:latest .

3. Push the Docker Image to Your Container Registry: Push the built image to your container registry.

sh
   docker push your-registry/prefect-k8s-aws:latest

### Using the Custom Image in Kubernetes Once you have your custom Docker image, you can use it in your Kubernetes worker and job templates. ### Example Kubernetes Job Template: Here's an updated example of a Kubernetes job template using your custom Docker image:

yaml
apiVersion: batch/v1
kind: Job
metadata:
  labels: "{{ labels }}"
  namespace: "{{ namespace }}"
  generateName: "{{ name }}-"
spec:
  ttlSecondsAfterFinished: "{{ finished_job_ttl }}"
  template:
    spec:
      parallelism: 1
      completions: 1
      restartPolicy: Never
      serviceAccountName: "{{ service_account_name }}"
      containers:
        - name: prefect-job
          image: your-registry/prefect-k8s-aws:latest
          env:
            - name: PREFECT__CONTEXT__S3_BUCKET
              value: "my_s3_bucket"
            # Other environment variables as needed
          resources:
            requests:
              memory: "{{ memory }}Mi"
            limits:
              cpu: "500m"
              memory: "512Mi"
    serviceAccountName: "your-service-account"  # Replace with your service account

### Running the Kubernetes Worker: Ensure that your worker is configured to use the custom image:

sh
prefect worker start --pool 'my-work-pool' --type kubernetes

By following these steps, you can create a custom Docker image tailored to your needs using the official Prefect image as a base and adding the necessary integrations. This approach lets you leverage both

prefect-kubernetes

and

prefect-aws

packages seamlessly in your deployment.

@Marvin can you create me the docker file and make sure the aws and k8s packages are for prefect 2

Sure! Below is the Dockerfile that extends the Prefect 2.x base image to include both

prefect-kubernetes

and

prefect-aws

packages tailored for Prefect 2.x. ### Dockerfile:

dockerfile
# Use the official Prefect 2.x base image
FROM prefecthq/prefect:2.7.7-python3.9

# Install the prefect-kubernetes and prefect-aws packages
RUN pip install prefect-kubernetes==0.2.15 prefect-aws==0.2.1

# Optionally, set environment variables if needed
# ENV PREFECT_API_KEY=your_prefect_api_key

### Explanation: - Base Image: We're using the official

prefect:2.7.7-python3.9

image, which ensures compatibility with Prefect 2.x. - Installing Packages: We install

prefect-kubernetes

and

prefect-aws

using the

pip install

command. Version

0.2.15

for

prefect-kubernetes

and

0.2.1

for

prefect-aws

are specified to ensure compatibility with Prefect 2.x. ### Building Your Docker Image: 1. Save the Dockerfile: Save the above Dockerfile content into a file named

Dockerfile

. 2. Build the Docker Image: Open a terminal, navigate to the folder containing your Dockerfile, and run the build command:

sh
   docker build -t your-registry/prefect-k8s-aws:latest .

3. Push the Docker Image to Your Container Registry: Push the built image to your container registry:

sh
   docker push your-registry/prefect-k8s-aws:latest

### Using the Image in Kubernetes: Once the image is built and pushed to your registry, you can reference it in your Kubernetes job template. ### Kubernetes Job Template Example:

yaml
apiVersion: batch/v1
kind: Job
metadata:
  labels: "{{ labels }}"
  namespace: "{{ namespace }}"
  generateName: "{{ name }}-"
spec:
  ttlSecondsAfterFinished: "{{ finished_job_ttl }}"
  template:
    spec:
      parallelism: 1
      completions: 1
      restartPolicy: Never
      serviceAccountName: "{{ service_account_name }}"
      containers:
        - name: prefect-job
          image: your-registry/prefect-k8s-aws:latest
          env:
            - name: PREFECT__CONTEXT__S3_BUCKET
              value: "my_s3_bucket"
            # Other environment variables as needed
          resources:
            requests:
              memory: "{{ memory }}Mi"
            limits:
              cpu: "500m"
              memory: "512Mi"
    serviceAccountName: "your-service-account"  # Replace with your service account

After following these steps, your custom Docker image will be ready to use in your Prefect-Kubernetes environment with both AWS and Kubernetes integrations. Feel free to ask if you need further assistance or if there are other specific requirements you have in mind!

@Marvin I have this env section in my k8s job template “env” : “{{ env }}“, how can I add a default env which will be present in all k8s jobs?

To add a default environment variable to all Kubernetes jobs, you can modify the job template to include both the default environment variables and any additional ones that might be passed dynamically. Here's a common approach using Jinja2 templating, assuming the rest of your Kubernetes job template uses Jinja2: 1. Define your default environment variables in the template. 2. Merge these with any additional environment variables provided by

env

. Here’s an example of how you might adjust your template:

yaml
env:
  - name: DEFAULT_VAR
    value: "default_value"
{% for key, value in env.items() %}
  - name: {{ key }}
    value: {{ value }}
{% endfor %}

In this example,

DEFAULT_VAR

is your default environment variable, and the template will loop over

env

to add any additional ones provided at runtime. Ensure that the placeholders match whatever templating engine or syntax you're using. If your environment is more complex, or if you're using Prefect to manage these job templates, please let me know which version of Prefect you're using so I can provide more specific advice! If you're using a Prefect feature, there could be relevant capabilities or integrations for handling such templating.

getting Base Job Template must be valid JSON @Marvin

The error "Base Job Template must be valid JSON" suggests that the Kubernetes job template you're using is expected to be in JSON format, and there's likely a formatting issue in the template. Here are a few steps to ensure your template is correctly formatted: 1. Check JSON Format: Ensure that your template is a valid JSON. You can use online tools like JSONLint to validate the format. 2. Convert YAML to JSON: If your template is currently written in YAML, you will need to convert it to JSON. YAML is often more human-readable, but tools that require JSON won't accept YAML directly. Python's PyYAML library can convert YAML to JSON easily if needed. 3. Include Default Env Variables: When you include default environment variables, make sure they are enclosed in curly braces and match the JSON format. Here's an example:

json
   {
     "env": [
       {
         "name": "DEFAULT_VAR",
         "value": "default_value"
       }
       {{ env | default([]) }}
     ]
   }

4. Template Placeholders: Ensure that your placeholders (

{{ }}

) are correctly positioned and comply with any JSON formatting requirements. They should integrate seamlessly with the rest of the JSON structure. If you're managing this with Prefect, please confirm your Prefect version, as there might be specific tools or best practices associated with managing Kubernetes jobs within Prefect flows. This will allow me to provide more tailored advice.