Let me help you organize your test and production deployments more efficiently. There are several recommended approaches for managing this split: 1. Using Deployment Names and Tags

python
from prefect import flow
from prefect.deployments import deploy

@flow
def my_flow():
    pass

# Deploy the same flow with different names/tags for test and prod
deploy(
    my_flow,
    name="test/my-flow",  # Using a prefix pattern
    tags=["test"],
    work_pool="test-pool"
)

deploy(
    my_flow,
    name="prod/my-flow",
    tags=["prod"],
    work_pool="prod-pool"
)

2. Batch Deployment of Multiple Flows

python
from prefect import flow
from prefect.deployments import deploy

@flow
def flow1(): pass
@flow
def flow2(): pass
@flow
def flow3(): pass

# Deploy all test flows at once
flows = [flow1, flow2, flow3]
for f in flows:
    deploy(
        f,
        name=f"test/{f.__name__}",
        tags=["test"],
        work_pool="test-pool"
    )

3. Using Different Work Pools - Create separate work pools for test and production - Assign deployments to their respective work pools - This provides isolation and different infrastructure configurations Best Practices for Test/Prod Split: 1. Consistent Naming Convention - Use prefixes like "test/" and "prod/" in deployment names - This keeps your deployments organized and easily filterable 2. Tags for Environment - Always tag deployments with their environment ("test" or "prod") - Makes it easy to filter and manage deployments 3. Separate Work Pools - Use different work pools for test and production - Allows for different infrastructure and scaling requirements 4. Environment-Specific Variables - Use different parameters and job variables for test and prod environments - Can be set at the deployment level While you can't have a single deployment running multiple different flows (each deployment is tied to a specific flow), you can: - Use consistent naming patterns - Deploy multiple flows at once with shared configuration - Use tags and work pools to organize and manage your deployments effectively The key is to use a combination of these approaches to create a clean, maintainable separation between your test and production environments. This gives you the organization you're looking for while maintaining the flexibility to manage each flow independently when needed. You can find more detailed examples and patterns in the deployment documentation.

Here's how to manage test and production deployments using the CLI approach. First, you'll need a

prefect.yaml

file that defines your deployment configurations. Here's how to structure it:

yaml
# prefect.yaml
deployments:
  - name: test/my-flow
    entrypoint: flows/my_flow.py:my_flow
    work_pool: 
      name: test-pool
    tags: [test]
    schedule: null

  - name: prod/my-flow
    entrypoint: flows/my_flow.py:my_flow
    work_pool:
      name: prod-pool
    tags: [prod]
    schedule: null

  # Additional flows
  - name: test/another-flow
    entrypoint: flows/another_flow.py:another_flow
    work_pool:
      name: test-pool
    tags: [test]
    schedule: null

  - name: prod/another-flow
    entrypoint: flows/another_flow.py:another_flow
    work_pool:
      name: prod-pool
    tags: [prod]
    schedule: null

Then deploy using the CLI:

bash
# Deploy all flows
prefect deploy

# Or deploy specific flows
prefect deploy --name 'test/*'  # Deploy all test flows
prefect deploy --name 'prod/*'  # Deploy all prod flows

# Deploy a specific flow
prefect deploy --name 'test/my-flow'

You can also use different YAML files for different environments:

yaml
# test.yaml
deployments:
  - name: my-flow
    entrypoint: flows/my_flow.py:my_flow
    work_pool: 
      name: test-pool
    tags: [test]
    schedule: null

  - name: another-flow
    entrypoint: flows/another_flow.py:another_flow
    work_pool:
      name: test-pool
    tags: [test]
    schedule: null

yaml
# prod.yaml
deployments:
  - name: my-flow
    entrypoint: flows/my_flow.py:my_flow
    work_pool: 
      name: prod-pool
    tags: [prod]
    schedule: null

  - name: another-flow
    entrypoint: flows/another_flow.py:another_flow
    work_pool:
      name: prod-pool
    tags: [prod]
    schedule: null

Then deploy using:

bash
prefect deploy --file test.yaml  # Deploy test environment
prefect deploy --file prod.yaml  # Deploy prod environment

Best Practices for CLI Deployments: 1. Consistent File Structure

project/
   ├── flows/
   │   ├── my_flow.py
   │   └── another_flow.py
   ├── prefect.yaml  # or separate test.yaml and prod.yaml
   └── requirements.txt

2. Use Environment-Specific Work Pools - Create separate work pools for test and production - Reference them in your YAML configurations 3. Naming Conventions - Use consistent prefixes in deployment names (test/, prod/) - Keep entrypoint paths relative to your project root 4. Tags and Organization - Use tags consistently to identify environments - Group related deployments together in your YAML files 5. Version Control - Keep your YAML configurations in version control - Consider using environment variables for sensitive values The CLI approach with YAML configuration provides a declarative way to manage your deployments and makes it easy to version control your deployment configurations. You can find more details about the YAML configuration options in the Prefect documentation.

Yes, you can absolutely share configuration between flows using YAML anchors and aliases! This is a great way to keep your configuration DRY and maintainable. Here's how to do it:

yaml
# prefect.yaml
definitions:
  # Define common configurations
  work_pools:
    test: &test_pool
      name: test-pool
      work_queue_name: default
      job_variables:
        env: test
        memory: 1GB
    
    prod: &prod_pool
      name: prod-pool
      work_queue_name: default
      job_variables:
        env: prod
        memory: 2GB

  # Common configurations that can be shared
  configs:
    test_base: &test_base
      work_pool: *test_pool
      tags: [test]
      schedule: null
      description: "Test deployment"

    prod_base: &prod_base
      work_pool: *prod_pool
      tags: [prod]
      schedule: null
      description: "Production deployment"

deployments:
  # Test deployments
  - name: test/flow-1
    entrypoint: flows/flow1.py:flow1
    <<: *test_base  # Inherit test base config
    description: "Flow 1 Test"  # Override specific fields

  - name: test/flow-2
    entrypoint: flows/flow2.py:flow2
    <<: *test_base
    description: "Flow 2 Test"

  # Production deployments
  - name: prod/flow-1
    entrypoint: flows/flow1.py:flow1
    <<: *prod_base
    description: "Flow 1 Production"

  - name: prod/flow-2
    entrypoint: flows/flow2.py:flow2
    <<: *prod_base
    description: "Flow 2 Production"

You can also create more specific configurations by combining and extending base configurations:

yaml
definitions:
  # ... previous definitions ...

  configs:
    # Base configurations
    common: &common_base
      schedule: null
      parameters: {}
      
    test_base: &test_base
      <<: *common_base  # Inherit common configuration
      work_pool: *test_pool
      tags: [test]
      
    # Specific configurations for different types of flows
    etl_test: &etl_test
      <<: *test_base  # Inherit test base
      tags: [test, etl]  # Add additional tags
      parameters:
        batch_size: 100
        
    ml_test: &ml_test
      <<: *test_base
      tags: [test, ml]
      parameters:
        model_version: "test"

deployments:
  - name: test/etl-flow-1
    entrypoint: flows/etl1.py:etl_flow
    <<: *etl_test  # Use ETL-specific configuration
    
  - name: test/ml-flow-1
    entrypoint: flows/ml1.py:train_model
    <<: *ml_test  # Use ML-specific configuration

Some useful patterns for sharing configurations: 1. Environment-Specific Settings

yaml
definitions:
  env_vars:
    test: &test_env
      DATABASE_URL: "<postgresql://test-db:5432>"
      API_KEY: "test-key"
    
    prod: &prod_env
      DATABASE_URL: "<postgresql://prod-db:5432>"
      API_KEY: "prod-key"

  configs:
    test_base: &test_base
      work_pool: *test_pool
      job_variables:
        <<: *test_env  # Include all test environment variables

2. Resource Configurations

yaml
definitions:
  resources:
    small: &small_resources
      memory: "1GB"
      cpu: "0.5"
    
    large: &large_resources
      memory: "4GB"
      cpu: "2"

  configs:
    test_base: &test_base
      work_pool:
        name: test-pool
        job_variables:
          <<: *small_resources  # Include resource configuration

3. Schedule Patterns

yaml
definitions:
  schedules:
    hourly: &hourly
      interval: 3600
      
    daily: &daily
      cron: "0 0 * * *"

deployments:
  - name: test/hourly-flow
    <<: *test_base
    schedule: *hourly
    
  - name: test/daily-flow
    <<: *test_base
    schedule: *daily

Key benefits of this approach: - Reduces duplication in your configuration - Makes it easier to maintain consistent settings across deployments - Allows for easy updates to shared configurations - Keeps your

prefect.yaml

file organized and readable - Makes it clear which configurations are being used where

Remember that you can always override specific fields even when using shared configurations. The more specific configuration (the one directly under the deployment) takes precedence over the shared configuration.