@Marvin what is the recommended way to deploy multiple flows using a single

python <file>

call with flows grouped into different files (modules). Each file defines the infrastructure for the flows in that file.

To deploy multiple flows from different files using a single

python <file>

call, you can organize your flows and infrastructure definitions into separate modules. Here's a concise guide: 1. Flow Definition: Define each flow in separate Python files. Each file can also define the infrastructure specific to the flows in that file. 2. Deployment Script: Create a deployment script that imports these flows and uses the

deploy

function to deploy them. You can use the

to_deployment

method on each flow to specify deployment configurations like job variables and work pool names. 3. Example Structure:

python
   # File: deploy_flows.py
   from prefect import deploy, flow
   from flows_module_one import flow_one
   from flows_module_two import flow_two

   if __name__ == "__main__":
       deploy(
           *[
               flow.to_deployment(
                   name=f"{flow.name}-deploy",
                   job_variables={"image": "your-docker-image"},
               )
               for flow in [flow_one, flow_two]
           ],
           work_pool_name="your-work-pool",
       )

4. Deployment Execution: Run this script with

python deploy_flows.py

, which will deploy the specified flows with their configurations. 5. Infrastructure: Each module can define its own infrastructure settings, which can be overridden or specified during the deployment process. For more detailed information, you can refer to the Prefect deployment documentation. This approach allows you to keep your flow logic modular and your deployment process centralized, making it easier to manage and update as needed.

@Marvin I have many flows across different files. Each flow either uses a CPU or GPU docker image. How can I define the deployment in such a way that the CPU and GPU deployment is split.

To deploy flows with separate CPU and GPU Docker images, you can define distinct deployments for each image type. Here's a concise approach: 1. Define Work Pools: Create separate work pools for CPU and GPU deployments. This allows you to manage resources and configurations separately. 2. Deployment Script: Use a deployment script to specify which flows should use the CPU or GPU Docker images. You can use the

job_variables

parameter to set the Docker image for each deployment. 3. Example Structure:

python
   # File: deploy_flows.py
   from prefect import deploy, flow
   from flows_module_cpu import cpu_flow_one, cpu_flow_two
   from flows_module_gpu import gpu_flow_one, gpu_flow_two

   CPU_IMAGE = "your-cpu-docker-image"
   GPU_IMAGE = "your-gpu-docker-image"

   if __name__ == "__main__":
       # Deploy CPU flows
       deploy(
           *[
               flow.to_deployment(
                   name=f"{flow.name}-cpu-deploy",
                   job_variables={"image": CPU_IMAGE},
               )
               for flow in [cpu_flow_one, cpu_flow_two]
           ],
           work_pool_name="cpu-work-pool",
       )

       # Deploy GPU flows
       deploy(
           *[
               flow.to_deployment(
                   name=f"{flow.name}-gpu-deploy",
                   job_variables={"image": GPU_IMAGE},
               )
               for flow in [gpu_flow_one, gpu_flow_two]
           ],
           work_pool_name="gpu-work-pool",
       )

4. Execution: Run the script with

python deploy_flows.py

. This will deploy the flows with their respective CPU or GPU configurations. For further customization and control over deployments, you can refer to Prefect's infrastructure examples for Docker. This setup ensures that your flows are appropriately allocated to the correct resources, optimizing performance and resource usage.

@Marvin how would this work if the flow in the flows module defines to which image it belongs to?

If each flow defines the type of Docker image it should use (CPU or GPU), you can incorporate this information into your deployment script. Here's how you can structure it: 1. Flow Definition: Each flow should have an attribute or function that indicates whether it requires a CPU or GPU image. 2. Deployment Script: The script will check this attribute and deploy the flow with the appropriate Docker image and work pool. 3. Example Structure:

python
   # File: deploy_flows.py
   from prefect import deploy, flow
   from flows_module import cpu_flow, gpu_flow

   CPU_IMAGE = "your-cpu-docker-image"
   GPU_IMAGE = "your-gpu-docker-image"

   # Example flows with image type attribute
   cpu_flow.image_type = "CPU"
   gpu_flow.image_type = "GPU"

   if __name__ == "__main__":
       all_flows = [cpu_flow, gpu_flow]

       for flow in all_flows:
           if flow.image_type == "CPU":
               deploy(
                   flow.to_deployment(
                       name=f"{flow.name}-cpu-deploy",
                       job_variables={"image": CPU_IMAGE},
                   ),
                   work_pool_name="cpu-work-pool",
               )
           elif flow.image_type == "GPU":
               deploy(
                   flow.to_deployment(
                       name=f"{flow.name}-gpu-deploy",
                       job_variables={"image": GPU_IMAGE},
                   ),
                   work_pool_name="gpu-work-pool",
               )

4. Execution: Run the script with

python deploy_flows.py

. The script will deploy each flow with the correct Docker image based on its

image_type

. This approach centralizes the logic for determining the deployment configuration within the flow definitions themselves, making your deployment script more adaptable to changes in flow requirements.

@marvis

1. Separate Deployment Recommendation: Deploying each flow separately is generally recommended when they have distinct infrastructure requirements, such as different Docker images for CPU and GPU. This allows you to tailor the deployment configurations to the specific needs of each flow, ensuring optimal resource usage and performance. 2. Setting Custom Attributes: To set a custom attribute like

image_type

for a flow, you can simply add an attribute to the flow function or use a decorator to annotate it. Here's how you can do it:

python
   from prefect import flow

   @flow
   def my_cpu_flow():
       # Flow logic here
       pass

   my_cpu_flow.image_type = "CPU"

   @flow
   def my_gpu_flow():
       # Flow logic here
       pass

   my_gpu_flow.image_type = "GPU"

Assigning attributes directly to the flow function allows you to easily check the

image_type

during deployment and configure the deployment process accordingly. 3. Deployment Script: Use the

image_type

attribute in the deployment script to determine which Docker image and work pool to use:

python
   from prefect import deploy
   from flows_module import my_cpu_flow, my_gpu_flow

   CPU_IMAGE = "your-cpu-docker-image"
   GPU_IMAGE = "your-gpu-docker-image"

   all_flows = [my_cpu_flow, my_gpu_flow]

   for flow in all_flows:
       if flow.image_type == "CPU":
           deploy(
               flow.to_deployment(
                   name=f"{flow.name}-cpu-deploy",
                   job_variables={"image": CPU_IMAGE},
               ),
               work_pool_name="cpu-work-pool",
           )
       elif flow.image_type == "GPU":
           deploy(
               flow.to_deployment(
                   name=f"{flow.name}-gpu-deploy",
                   job_variables={"image": GPU_IMAGE},
               ),
               work_pool_name="gpu-work-pool",
           )

This approach provides flexibility and clarity, making it easier to manage and update deployments as flow requirements evolve. For more details, you can explore Prefect's deployment customization documentation.