@Marvin I deployed multiple flows to a single workpool this way:

deployment = Deployment.build_from_flow(
    flow=flow,
    name=f"{flow.name}",
    work_pool_name="localhost-tests",
    job_variables={
        "env": dict(env.to_dict())
    },
)
deployment.apply()

is there a way to edit the env variables for each flow before I run it?

To edit the environment variables for a flow deployment before running it in Prefect, you can override the job variables in various ways, depending on how you're deploying the flows (either using a YAML file or directly in the code). Here's a general approach: 1. Using the Prefect YAML File: If you are deploying using a

prefect.yaml

file, you can define job variables directly within it. 2. Programmatically (as in your example): You can update the

job_variables

when building the deployment. Here's a refined example based on your current code: ### Using the Prefect YAML File

yaml
deployments:
- name: demo-deployment
  entrypoint: demo_project/demo_flow.py:some_work
  work_pool:
    name: local
  job_variables:
    env:
      EXECUTION_ENVIRONMENT: staging
      MY_NOT_SO_SECRET_CONFIG: plumbus

### Programmatically You can change the

job_variables

as required before running each deployment. Here's how you might dynamically change the environment variables:

python
from prefect.deployments import Deployment

def update_env(flow, env):
    deployment = Deployment.build_from_flow(
        flow=flow,
        name=f"{flow.name}",
        work_pool_name="localhost-tests",
        job_variables={
            "env": env
        },
    )
    deployment.apply()

# Example of updating environment variables dynamically
flows = [flow1, flow2, flow3]
environments = [
    {"EXECUTION_ENVIRONMENT": "development", "MY_NOT_SO_SECRET_CONFIG": "dev_config"},
    {"EXECUTION_ENVIRONMENT": "staging", "MY_NOT_SO_SECRET_CONFIG": "stage_config"},
    {"EXECUTION_ENVIRONMENT": "production", "MY_NOT_SO_SECRET_CONFIG": "prod_config"}
]

for flow, env in zip(flows, environments):
    update_env(flow, env)

In this example, before running the deployment, you are updating the

env

variables for each flow. Adjust the

env

dictionary as needed for each flow. For more detailed information, check the following documentation: Prefect Documentation - Overriding Job Variables Feel free to ask if you have more questions!

@Marvin I want to edit it from the prefect UI when I do custom run

You can indeed edit the environment variables for a deployment when initiating a custom run directly from the Prefect UI. This allows you to pass in a dictionary of variables into your flow run infrastructure. ### Editing Job Variables in the Prefect UI When you initiate a custom run from the Prefect UI, you will have the option to specify job variables. Here’s how you can do it: 1. Navigate to the Deployment: Go to the Prefect UI and find the deployment you want to run. 2. Initiate a Custom Run: Click on the deployment to initiate a custom run. 3. Set Job Variables: You will see a section to add job variables. These job variables can include environment variables you want to override. 4. Specify Environment Variables: You can add key-value pairs for the environment variables you need. Here is a more detailed guide from Prefect's documentation: Overriding Job Variables in the Prefect UI ### Example with Prefect CLI If you prefer using the command line interface (CLI) for running deployments with modified job variables, you can use the following command:

sh
prefect deployment run \
    --id "your-deployment-id" \
    --job-variable MY_NEW_ENV_VAR=42 \
    --job-variable HELLO=THERE

This command shows how to run the deployment with additional job variables directly from the CLI. Feel free to explore either method depending on your preferences and workflow. If you have any more questions or need further assistance, let me know!

@Marvin let's say I deployed my flow with this job variables:

{
            "env": {
                "EXECUTION_ENVIRONMENT": "local",
                "MY_NOT_SO_SECRET_CONFIG": os.environ.get("MY_NOT_SO_SECRET_CONFIG")
            }
        }

if I want to do a custom run from the UI and I write this in the job variables section:

{
            "env": {
                "EXECUTION_ENVIRONMENT": "aws",
            }
        }

will it only override the "EXECUTION_ENVIRONMENT" variable and not affect the "MY_NOT_SO_SECRET_CONFIG" variable? or will it override the entire env?

If you override the

env

dictionary using the custom run parameters in the Prefect UI, it will override the entire

env

dictionary and not selectively merge with existing values. This means that if you only provide

"EXECUTION_ENVIRONMENT"

, the other variables such as

"MY_NOT_SO_SECRET_CONFIG"

will be omitted unless explicitly provided in the new override. ### Example Scenario: #### Initial Deployment

python
{
    "env": {
        "EXECUTION_ENVIRONMENT": "local",
        "MY_NOT_SO_SECRET_CONFIG": os.environ.get("MY_NOT_SO_SECRET_CONFIG")
    }
}

#### Custom Run Override

json
{
    "env": {
        "EXECUTION_ENVIRONMENT": "aws"
    }
}

### Outcome The environment for the custom run will be:

json
{
    "env": {
        "EXECUTION_ENVIRONMENT": "aws"
    }
}

### Solutions: 1. Explicitly Add All Variables in Custom Run: Ensure you include all necessary environment variables in the custom run parameters. 2. Dynamic Merging within the Flow Code: Logic within the flow to merge environment variables dynamically if not supplied. For more information on how Prefect handles job variable overrides, refer to the following documentation: Prefect Documentation - Overriding Job Variables If you need further assistance or have more questions, feel free to ask!

@Marvin can I run a deployed flow in remote machine?

Yes, you can run a deployed flow on a remote machine using Prefect. Prefect supports multiple infrastructure options that allow you to run flows on different environments, including remote machines, cloud providers, and containerized environments. Here's how you can achieve that: ### Using Docker, Kubernetes, or Cloud Infrastructure Prefect allows deploying flows to various environments such as Docker containers, Kubernetes clusters, and serverless cloud providers like AWS ECS, Azure Container Instances, and Google Cloud Run. ### Steps To Run a Deployment on a Remote Machine 1. Define the Infrastructure: When creating a deployment, specify the remote infrastructure where you want the flow to run. This could be a Docker container, a Kubernetes job, or remote serverless instances. 2. Use Work Pools and Workers: Setting up a work pool and workers can help manage and execute flows on remote infrastructure. ### Example: Deploying to Docker Here's an example of deploying a flow to run in a Docker container: #### Define the Deployment

python
from prefect.deployments import Deployment
from prefect.infrastructure import DockerContainer

docker_block = DockerContainer(
    image="your-docker-image",
    image_pull_policy="ALWAYS"
)

docker_block.save("remote-docker-block", overwrite=True)

deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="docker-deployment",
    infrastructure=docker_block,
    work_pool_name="remote-pool"
)
deployment.apply()

#### Run the Flow You can trigger the flow run either from the Prefect UI or using the Prefect CLI:

sh
prefect deployment run "docker-deployment"

### Example: Using Prefect Cloud Prefect-managed work pools allow you to run flows without managing the underlying infrastructure: #### Define the Deployment

python
from prefect.deployments import Deployment

deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="cloud-deployment",
    work_pool_name="prefect-work-pool"
)
deployment.apply()

#### Run the Flow Trigger it from the Prefect UI or CLI. For more detailed steps and options, refer to the following docs: - Deploying to Docker - Overriding Job Variables - Prefect Managed Work Pools By following these steps, you can efficiently run your deployed flows on remote machines or infrastructure, providing greater flexibility and scalability. If you need further assistance or have specific requirements, feel free to ask!

@Marvin I have 3 servers called s1, s2, s3 I want to be able to choose on which server to run how can I do that?

To run a deployed flow on a specific server (s1, s2, or s3), you can set up different work pools and associate each server with a unique work pool. Then, you can choose which work pool to use when triggering the flow. Here’s how you can configure and execute this: ### Steps to Setup: 1. Set Up Work Pools: Create a work pool for each server and configure workers on each server to listen to their respective work pools. 2. Deploy the Flow with Infrastructure and Work Pools: Specify which work pool to use during the deployment. You can dynamically choose the work pool based on the server you want to target. 3. Triggering the Flow: When triggering the flow, specify the work pool corresponding to the desired server. ### Example Configuration: #### Setting Up Work Pools For each server, create a specific work pool: -

s1-pool

for server

s1

-

s2-pool

for server

s2

-

s3-pool

for server

s3

Run this on each server to set up the worker listening to the assigned pool:

sh
prefect worker start -q s1-pool
prefect worker start -q s2-pool
prefect worker start -q s3-pool

#### Deploy Your Flow Here’s how you can deploy the same flow to different servers by specifying the work pool:

python
from prefect.deployments import Deployment
from prefect.infrastructure import Process

# Define your flow, assuming it's named `my_flow`
deployment_s1 = Deployment.build_from_flow(
    flow=my_flow,
    name="my_flow_s1",
    work_pool_name="s1-pool",
    infrastructure=Process()
)
deployment_s1.apply()

deployment_s2 = Deployment.build_from_flow(
    flow=my_flow,
    name="my_flow_s2",
    work_pool_name="s2-pool",
    infrastructure=Process()
)
deployment_s2.apply()

deployment_s3 = Deployment.build_from_flow(
    flow=my_flow,
    name="my_flow_s3",
    work_pool_name="s3-pool",
    infrastructure=Process()
)
deployment_s3.apply()

#### Running the Flow on a Specific Server You can then trigger the flow run from the Prefect UI or the CLI, specifying the correct deployment for the desired server (

s1

,

s2

, or

s3

). For example, using the CLI:

sh
prefect deployment run my_flow_s1
prefect deployment run my_flow_s2
prefect deployment run my_flow_s3

Or, from the Prefect UI: 1. Go to the Deployments page. 2. Select the deployment corresponding to the server you want. 3. Click "Quick Run" or "Custom Run" to initiate the flow on the selected server. ### References: For more detailed information, you can refer to the following documentation: - Prefect Deployment and Infrastructure - Prefect Work Pools and Workers Following these steps, you can effectively control and run your deployed flows on any of the specified servers (

s1

,

s2

,

s3

) by choosing the appropriate work pool. If you have more questions or need further assistance, let me know!

@Marvin So I need to create a workpool for each server and make sure to deploy my flows to each one?

Yes, creating a work pool for each server and deploying your flows to each specific work pool is a recommended approach. This allows you to have fine-grained control over which server executes which flow. ### Steps Recap: 1. Create Work Pools for Each Server: -

s1-pool

for

server s1

-

s2-pool

for

server s2

-

s3-pool

for

server s3

2. Run Workers on Each Server: On each server, run a worker that listens to its corresponding work pool.

sh
   # On server s1
   prefect worker start -q s1-pool
   # On server s2
   prefect worker start -q s2-pool
   # On server s3
   prefect worker start -q s3-pool

3. Deploy Your Flows to Each Work Pool: Deploy the same flow to each work pool so you can choose where to run it.

python
   from prefect.deployments import Deployment
   
   deployment_s1 = Deployment.build_from_flow(
       flow=my_flow,
       name="my_flow_s1",
       work_pool_name="s1-pool"
   )
   deployment_s1.apply()
   
   deployment_s2 = Deployment.build_from_flow(
       flow=my_flow,
       name="my_flow_s2",
       work_pool_name="s2-pool"
   )
   deployment_s2.apply()
   
   deployment_s3 = Deployment.build_from_flow(
       flow=my_flow,
       name="my_flow_s3",
       work_pool_name="s3-pool"
   )
   deployment_s3.apply()

4. Trigger the Flow on the Desired Server: When you want to run the flow on a specific server, trigger the corresponding deployment from the Prefect UI or CLI.

sh
   # Run on server s1
   prefect deployment run my_flow_s1
   # Run on server s2
   prefect deployment run my_flow_s2
   # Run on server s3
   prefect deployment run my_flow_s3

### Benefits: - Targeted Execution: Ensures that specific servers handle the flow runs, which can be useful for resource management, geographical considerations, or server-specific data access. - Flexibility: Allows you to define infrastructure settings tailored to each server. - Scalability: Easily scale by adding more servers and corresponding work pools. By following this setup, you gain full control over the execution environment of your flows, helping to manage resources efficiently and ensuring that your flows run where they are intended. For more details, you can refer to the Prefect Work Pools and Workers Documentation. If you have any further questions or need more detailed assistance, feel free to ask!

@Marvin Let's say I have a s0 server, can I deploy the flows to this server and then choose on which server to run the flows?

Yes, you can deploy flows from a central server (s0) and then choose on which specific server (s1, s2, or s3) to run the flows by dynamically assigning the work pool at runtime. This can be achieved by configuring your deployments to be flexible and specifying the target work pool when initiating the flow run. ### Strategy: 1. Deploy Flows from s0: Deploy your flows from the s0 server without binding them to a specific work pool. 2. Dynamically Assign Work Pool: Specify the target work pool when you trigger a flow run, allowing you to choose the specific server (s1, s2, or s3) for execution. ### Steps: 1. Setup Work Pools and Workers on Target Servers (s1, s2, s3): Ensure each server has a worker listening to its respective work pool.

sh
   # On server s1
   prefect worker start -q s1-pool
   
   # On server s2
   prefect worker start -q s2-pool
   
   # On server s3
   prefect worker start -q s3-pool

2. Deploy Flows from Server s0: Deploy your flow without tying it to a specific work pool.

python
   from prefect.deployments import Deployment

   deployment = Deployment.build_from_flow(
       flow=my_flow,
       name="multi-server-deployment"
   )
   deployment.apply()

3. Trigger Flow Runs with Dynamic Work Pools: You can now dynamically assign the work pool at run-time using either the Prefect CLI or the Prefect UI. #### Using the Prefect CLI You can use the

-q

(or

--work-queue

) flag to specify the work pool when running the deployment:

sh
# Run on server s1
prefect deployment run multi-server-deployment -q s1-pool

# Run on server s2
prefect deployment run multi-server-deployment -q s2-pool

# Run on server s3
prefect deployment run multi-server-deployment -q s3-pool

#### Using the Prefect UI 1. Go to the "Deployments" page in the Prefect UI. 2. Select "multi-server-deployment". 3. Click on "Quick Run" or "Custom Run". 4. In the custom run modal, specify the desired work pool (s1-pool, s2-pool, or s3-pool) before starting the run. This approach lets you have a central place (server s0) to manage and deploy your flows, while giving the flexibility to choose on which server (s1, s2, or s3) the flows should be executed. ### Benefits: - Centralized Management: Deploy all flows from a central server (s0). - Flexible Execution: Dynamically choose the target server based on runtime conditions or specific requirements. - Efficient Resource Utilization: Manage and utilize server resources effectively by directing the flow runs strategically. By following these steps, you can achieve a flexible and manageable approach to running flows on specific servers based on your needs. If you have more questions or need further customization, feel free to ask!

@Marvin can I use the deployment tags option to choose which workpool to use to run the flow?

Using deployment tags specifically to choose which work pool to use when running a flow is not a native feature in Prefect. However, you can leverage deployment tags together with sophisticated job configuration and filtering to achieve dynamic execution behavior. ### Using Deployment Tags Tags can help organize and filter deployments, allowing workers to pick specific deployments based on tags. This isn't a direct selection mechanism for work pools, but it can help route tasks to appropriate workers if your workers are configured to filter based on tags. ### Steps to Implement Tag-Based Execution Logic: 1. Create Tags for Servers: Assign tags to your deployments that correspond to your target servers (

s1

,

s2

,

s3

). 2. Configure Workers to Filter by Tags: Deploy workers on each server and configure them to poll work pools and filter tasks based on tags if necessary. #### Example Deployment with Tags

python
from prefect.deployments import Deployment

deployment_s1 = Deployment.build_from_flow(
    flow=my_flow,
    name="deployment_s1",
    tags=["s1"]
)
deployment_s1.apply()

deployment_s2 = Deployment.build_from_flow(
    flow=my_flow,
    name="deployment_s2",
    tags=["s2"]
)
deployment_s2.apply()

deployment_s3 = Deployment.build_from_flow(
    flow=my_flow,
    name="deployment_s3",
    tags=["s3"]
)
deployment_s3.apply()

#### Configure Worker to Filter by Tags: On each server, start workers that poll their respective work pool and filter tasks based on tags.

sh
# On server s1
prefect worker start -q s1-pool --tag s1

# On server s2
prefect worker start -q s2-pool --tag s2

# On server s3
prefect worker start -q s3-pool --tag s3

### Running the Flow: When you trigger a flow run, it will be picked up by the worker configured with the corresponding tags.

sh
# Running the flow on server s1
prefect deployment run deployment_s1

# Running the flow on server s2
prefect deployment run deployment_s2

# Running the flow on server s3
prefect deployment run deployment_s3

### Benefits: - Organize and Filter by Tags: Easily manage and route deployments by organizing them with tags and configuring workers accordingly. - Flexible Execution: Workers on different servers can pick up tasks dynamically based on the tags they are configured to handle. ### Additional Considerations: While tags help in organizing and routing tasks, you might want to consider adding additional logic for more complex deployment and runtime decisions. Prefect's flexibility with job variables and infrastructure overrides can help achieve such advanced configurations. For more information, refer to the Prefect Tagging Documentation and Work Pools Documentation. If you have any further questions or need help with specific configurations, feel free to ask!

@Marvin in our prefect 1 infrastructure, we used labels to achieve this, would you say it's the same?

Yes, in Prefect 2.x, tags can be used similarly to how labels were used in Prefect 1.x for routing tasks to specific agents or workers. Here's a comparison and an explanation of how to achieve similar functionality with Prefect 2.x using tags and work pools/workers: ### Comparison: - Prefect 1.x: Used labels on agents to control which agents could pick up which flow runs. - Prefect 2.x: Uses tags on deployments and work pools/workers to control the execution environment for flow runs. ### Steps to Migrate from Prefect 1.x Labels to Prefect 2.x Tags: 1. Set Up Work Pools and Workers: Ensure each server (s1, s2, s3) has a worker that polls a specific work pool and is configured to filter based on tags. 2. Add Tags to Deployments: Assign tags to deployments to indicate the target execution environment (e.g.,

s1

,

s2

,

s3

). ### Example Migration: #### Setting Up Work Pools and Workers: - Server s1:

sh
  prefect worker start -q s1-pool -t s1

- Server s2:

sh
  prefect worker start -q s2-pool -t s2

- Server s3:

sh
  prefect worker start -q s3-pool -t s3

#### Deploy Your Flows with Tags:

python
from prefect.deployments import Deployment

# Define your flow
deployment_s1 = Deployment.build_from_flow(
    flow=my_flow,
    name="deployment_s1",
    tags=["s1"],
    work_pool_name="s1-pool"
)
deployment_s1.apply()

deployment_s2 = Deployment.build_from_flow(
    flow=my_flow,
    name="deployment_s2",
    tags=["s2"],
    work_pool_name="s2-pool"
)
deployment_s2.apply()

deployment_s3 = Deployment.build_from_flow(
    flow=my_flow,
    name="deployment_s3",
    tags=["s3"],
    work_pool_name="s3-pool"
)
deployment_s3.apply()

### Running the Flow on Specific Servers: You can now trigger the deployment for the specific servers based on the tags:

sh
# Run on server s1
prefect deployment run deployment_s1

# Run on server s2
prefect deployment run deployment_s2

# Run on server s3
prefect deployment run deployment_s3

### Benefits of This Approach: - Consistency: Similar to how labels worked in Prefect 1.x, tags in Prefect 2.x allow you to organize and route flow runs to the desired execution environment. - Flexibility: Easily configure and manage deployments without hardcoding server-specific logic. ### Additional Resources: - Prefect Tags Documentation - Prefect Work Pools and Workers Documentation This approach provides a consistent and flexible way to manage where your flows are executed, leveraging the tagging feature of Prefect 2.x. If you have any more questions or need further assistance, feel free to ask!