@Marvin Why am I getting a

OSError: source code not available

when attempting to run a flow via a local jupyter notebook on a dask cluster hosted in aws ecs.

Oh no! Code go bye-bye when running flow on dask in AWS ECS from local Jupyter. Is it magic? Let's investigate. Can you tell me what version of Prefect you are using? This will help me look up the right info.

3.0.1

The error

OSError: source code not available

often occurs when there's a mismatch in the environment or dependencies between your local Jupyter notebook and the Dask cluster in AWS ECS. This problem can arise due to several reasons: 1. Code Availability: The code being executed might not be available on the workers in the Dask cluster. Ensure all necessary code and dependencies are present on the cluster. 2. Environment Differences: There might be differences between the local environment and the cluster environment. Make sure they are synchronized. ### Possible Solutions: 1. Package Code with Flow: Ensure that the code is included with the flow deployment. You might need to package the code and dependencies with your flow. 2. Image Consistency: Use the same Docker image for the Dask workers as you use locally to eliminate environment discrepancies. 3. Network and Permissions: Verify network configurations and ensure the Dask cluster can access all necessary resources. ### Resources - Troubleshooting Prefect Cloud - Prefect Dask Usage Guide These docs might give additional insight into debugging and setting up your environment correctly. If these steps do not help, you could create an issue on Prefect's GitHub with detailed logs and environment settings for more tailored assistance.

@Marvin Doesn't prefect send the flow code to the worker automatically?

Grug think Prefect supposed to send flow code to worker automatically. But sometimes magic fail and things break. Worker not find code and

OSError

happens. Prefect should package and send: 1. Flow Code: Code you wrote. 2. Dependencies: Libraries needed. But sometimes: - Environment mismatch - Missing dependencies - Network issues Prefect usually good, but for strange problems, check: 1. Docker Images: Make sure same images for local and Dask workers. 2. Network Access: Confirm Dask workers can access everything they need. 3. Custom Task Runners: Might need to configure how code gets sent. ### Resources - Prefect Dask Usage Guide - setting up Dask with Prefect - Troubleshoot - general debugging tips If problem continues, dig deeper or ask Prefect wizards (community or GitHub). Link to Prefect Dask guide might shed light on specific setup needs.

@Marvin The dask worker and scheduler is running the same docker image with the same environment

Grug understand. Same Docker image on Dask workers and scheduler should mean environment is same. Still, strange things can happen. Possible checks: 1. Shared Storage: Ensure your Jupyter notebook and Dask workers can access the same storage for source code. 2. Code Packaging: Ensure flow code is packaged correctly. Use

--path

option if deploying with Prefect CLI. 3. Result Location: Make sure the location where results are stored is accessible to your local machine and AWS. Check: - Are environment variables set correctly? - Can Dask workers access all necessary network resources? If still problem, try simpler flow to isolate issue. ### Things to Try 1. Deploy Flow Locally: Run the flow on a local Dask cluster to see if issue replicates. 2. Log Details: Add more logging to identify where it fails. ### Debug Resources - Prefect Slack Community - Github Issues for Prefect If this doesn’t solve, might be time for deep dive or community help.

@Marvin Do I need to create a deployment before I can run a flow?

Grug see where confusion come from. In Prefect 2.0 and newer, creating a deployment is not always required to run a flow. ### Direct Execution You can run a flow directly from a Jupyter notebook:

python
from prefect import flow

@flow
def my_flow():
    print("Hello, world!")

my_flow()

### Using Dask To use Dask as your task runner without a deployment:

python
from prefect import flow
from prefect_dask import DaskTaskRunner

@flow(task_runner=DaskTaskRunner())
def my_dask_flow():
    print("Running on Dask!")

my_dask_flow()

### When Deployment is Needed - Schedule: If you want to schedule flows. - Flow Storage: If running in distributed, production settings. ### Debugging Your Issue: Ensure you are running the flow in a way that makes your code accessible to the Dask cluster. If you find doing this in Jupyter troublesome, consider creating a deployment. ### Creating Deployment Example: 1. Deployment Script:

python
from prefect.deployments import Deployment
from prefect_dask import DaskTaskRunner
from my_project import my_flow  # Your flow

deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-dask-flow",
    work_queue_name="my-work-queue",
    task_runner=DaskTaskRunner()
)

if __name__ == "__main__":
    deployment.apply()

2. Deploy from CLI:

sh
prefect deployment build my_flow.py:my_flow -n my-dask-flow

3. Apply Deployment:

sh
prefect deployment apply my_flow-deployment.yaml

### Further Reading - Prefect 2.x Documentation - Dask Task Runner Try running without deployment first, and setup deployment if needed later.

@Marvin What is distributed production settings?

Grug knows "distributed production settings" is fancy term for running code on many machines. Very important when lots of data and work to do. Let Grug explain: ### Distributed Production Settings 1. Multiple Workers: Your flow runs on many Dask workers, often on cloud (like AWS ECS). 2. Scalability: Can handle big tasks by adding more workers. 3. Reliability: If one worker fails, others continue. 4. Consistency: Same setup on all machines, avoiding "works on my machine" problem. ### When to Use Distributed Production 1. High Volume Tasks: Lots of data processing, machine learning training. 2. Long-running Flows: Tasks that take a long time. 3. Big Data: Work with large datasets distributed across many workers. ### Why Deployment Helps 1. Central Control: Easier to manage and update flows. 2. Scheduling: Run flows at specific times. 3. Monitoring and Logging: Keep track of runs, easier debugging. ### Examples of Use Cases - ETL Pipelines: Extract, transform, load large amounts of data. - Machine Learning: Training models using distributed data. - Simulations: Run complex simulations that need lots of computation. ### Prefect Deployment in Distributed Settings 1. Work Pools: Schedule and distribute tasks to workers. 2. Flow Storage: Store flow code in cloud storage accessible by every worker. 3. Custom Task Runners: Use runners like

DaskTaskRunner

to distribute tasks. ### Setting Up 1. Define Flow: Write your Prefect flow. 2. Configure Environment: Setup Docker, cloud resources. 3. Create Deployment: Use Prefect CLI or scripts. ### Resources - Prefect Dask Usage Guide - Deployments Guide If data big or tasks slow, distributed production is way to go!

@Marvin so you're saying unless the dask cluster is local, a deployment needs to be created?

Grug say yes, you understand correctly. For non-local Dask cluster (like AWS ECS), creating a deployment is usually best practice. This ensures the flow code and environment are correctly packaged and accessible to all workers in the cluster. ### Why Deployment Needed for Non-Local Dask Cluster: 1. Code Transfer: Deployment ensures all code is available to remote Dask workers. 2. Environment Sync: Ensures consistent environment across all cluster nodes. 3. Storage and Access: Simplifies setting up access to necessary resources (e.g., datasets, models). ### Steps to Create Deployment: 1. Write Flow:

python
   from prefect import flow
   from prefect_dask import DaskTaskRunner

   @flow(task_runner=DaskTaskRunner())
   def my_flow():
       print("Hello, distributed world!")

   if __name__ == "__main__":
       my_flow()

2. Deployment Script:

python
   from prefect.deployments import Deployment

   deployment = Deployment.build_from_flow(
       flow=my_flow,
       name="my-dask-flow",
       work_queue_name="my-work-queue"
   )

   if __name__ == "__main__":
       deployment.apply()

3. Store Code: Make sure your code is available in a place accessible by Dask workers, like S3 bucket or similar. 4. Deploy and Apply:

sh
   prefect deployment build my_flow_script.py:my_flow -n my-dask-flow
   prefect deployment apply my_flow-deployment.yaml

### Prefect Cloud & Agent: - Prefect Agent: Ensures your Prefect flows run on the Dask cluster. - Flow Storage: Use Prefect's storage options (like S3, Docker) to store and distribute code. ### Resources: - Prefect Deployments Guide - Prefect Dask Task Runner For remote Dask clusters, deployment makes sure everything works smoothly and workers have what they need.

@Marvin How do I store the code then as part of the deployment process

Grug know storing code important so Dask workers find it. Here how to store code as part of deployment process: ### Using Prefect Storage Options Prefect offers several storage options to ensure your flow code can be accessed by all Dask workers. 1. Docker Storage: Package your flow code in a Docker image. 2. File Storage: Store the code in an accessible file system or cloud storage like S3. 3. GitHub Storage: Store the code in a GitHub repository. ### Example: Using Docker Storage 1. Dockerfile: Create a

Dockerfile

to define your environment and flow code.

Dockerfile
   FROM prefecthq/prefect:latest-python3.9

   WORKDIR /app
   COPY . /app

   RUN pip install -r requirements.txt

2. Configure Deployment Script to Use Docker:

python
   from prefect.deployments import Deployment
   from prefect.infrastructure.docker import DockerContainer
   from my_flow_module import my_flow  # Adjust import to your flow file

   docker_block = DockerContainer(
       image="your-dockerhub-username/your-image-name:latest",
       image_registry_url="<http://docker.io|docker.io>",
       image_pull_policy="ALWAYS"
   )

   deployment = Deployment.build_from_flow(
       flow=my_flow,
       name="my-dask-flow",
       infrastructure=docker_block,
       description="Deployment using Docker storage"
   )

   if __name__ == "__main__":
       deployment.apply()

3. Build and Push Docker Image:

sh
   docker build -t your-dockerhub-username/your-image-name:latest .
   docker push your-dockerhub-username/your-image-name:latest

### Example: Using S3 Storage 1. Configure Storage in Deployment Script:

python
   from prefect.deployments import Deployment
   from prefect.filesystems import S3
   from my_flow_module import my_flow  # Adjust import to your flow file

   s3_block = S3.load("my-s3-block")  # Create and configure S3 block in Prefect Cloud/Server

   deployment = Deployment.build_from_flow(
       flow=my_flow,
       name="my-dask-flow",
       storage=s3_block
   )

   if __name__ == "__main__":
       deployment.apply()

2. Ensure S3 Access: Make sure your Dask workers have credentials to access S3. ### Prefect CLI for Deployment Use Prefect CLI to simplify the deployment creation process:

sh
prefect deployment build my_flow.py:my_flow -n my-dask-flow --infra docker-container --storage s3/my-bucket/my-prefix
prefect deployment apply my_flow-deployment.yaml

### Resources - Prefect Deployment Storage - Docker Block Documentation - S3 Block Documentation By packaging your flow with Docker or uploading to S3, Dask workers can access your code and run flows smoothly.

@Marvin Show me github way

Grug show GitHub way. Simple and effective. Store code in GitHub repository and Prefect will run it from there. ### Steps: 1. Code in GitHub: Ensure your flow code is in a GitHub repository. 2. Create GitHub Storage Block: Configure Prefect to use your GitHub repository for storing flow code. ### Step-by-Step Guide: #### 1. Code in GitHub Organize your flow code in a repository, for example

<https://github.com/your-username/your-repo>

.

your-repo/
├── flows/
│   └── my_flow.py
├── requirements.txt
└── README.md

#### 2. Create GitHub Storage Block in Prefect Use Prefect Cloud or Prefect Server UI. 1. Navigate to Blocks. 2. Add a new GitHub block. 3. Set the Repository URL to your GitHub repository. 4. Save the block, for example, name it

my-github-repo

. #### 3. Configure Deployment Script Use the GitHub storage block in your deployment script.

python
from prefect.deployments import Deployment
from prefect.filesystems import GitHub
from flows.my_flow import my_flow  # Adjust import to your flow file

github_block = GitHub.load("my-github-repo")  # Use the block you created

deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-github-flow",
    storage=github_block,
    entrypoint="flows/my_flow.py"  # Path to flow file in the repo
)

if __name__ == "__main__":
    deployment.apply()

#### 4. Run Deployment Script Run the deployment script to register your deployment with Prefect.

sh
python deploy.py

### Example Flow File (

flows/my_flow.py

)

python
from prefect import flow

@flow
def my_flow():
    print("Hello from GitHub stored flow!")

if __name__ == "__main__":
    my_flow()

### Running the Flow After applying the deployment, you can run your flow using Prefect's UI or CLI.

sh
prefect deployment run my-github-flow

### Resources - Prefect Deployments Guide - GitHub Storage Block Documentation By using GitHub for storage, you ensure that your flow code is easily accessible and version-controlled. Dask workers will pull the code directly from your GitHub repository. Grug hope this helps!

@Marvin what is the recommended storage place for the flow code if I want to run it on a ECS cluster

Grug think best storage for running on ECS highly depends on ease of access and setup. Here best options: ### Recommended Storage Options: 1. S3 Storage (Amazon Simple Storage Service) 2. Docker Images ### Why S3 or Docker? - S3: Great for simple file storage. Workers can pull code easily. - Docker: Packages entire environment, ensuring consistency. Workers pull and run containers. ### S3 Storage for ECS 1. Upload Code to S3: Store your flow code in an S3 bucket. 2. Create S3 Storage Block: Use Prefect to create an S3 storage block. ### Steps for S3 Storage: 1. Upload Code to S3 Bucket: - Upload code files to an S3 bucket, for example,

<s3://my-prefect-flows/flows/my_flow.py>

. 2. Create and Configure S3 Block in Prefect: - In the Prefect UI, go to Blocks. - Add a new S3 block. - Configure the block with your S3 bucket details. - Save the block, naming it something like

my-s3-storage

. 3. Deployment Script with S3 Storage:

python
   from prefect.deployments import Deployment
   from prefect.filesystems import S3
   from your_flow_module_here import your_flow_function_here  # Adjust import

   s3_block = S3.load("my-s3-storage")  # Load your configured S3 block

   deployment = Deployment.build_from_flow(
       flow=your_flow_function_here,
       name="my-ecs-flow",
       storage=s3_block,
       entrypoint="my_flow.py"  # Path to flow file in S3 bucket
   )

   if __name__ == "__main__":
       deployment.apply()

### Example:

sh
python deploy_s3_script.py

### Docker Storage for ECS 1. Dockerfile: Create a Dockerfile that contains your flow code and environment setup. 2. Build and Push Image: Push the Docker image to Docker Hub or Amazon ECR. 3. Create Docker Storage Block: In Prefect, create a Docker storage block using your image. ### Steps for Docker Storage: 1. Create Dockerfile:

Dockerfile
   FROM prefecthq/prefect:latest-python3.9

   WORKDIR /app
   COPY . /app

   RUN pip install -r requirements.txt

2. Build and Push Docker Image:

sh
   docker build -t your-dockerhub-username/your-image-name:latest .
   docker push your-dockerhub-username/your-image-name:latest

3. Create Docker Block in Prefect: - In Prefect UI, go to Blocks. - Add a new Docker Container block. - Set it up with your Docker image details. - Save it with a name like

my-docker-storage

. 4. Deployment Script with Docker Storage:

python
   from prefect.deployments import Deployment
   from prefect.infrastructure.docker import DockerContainer
   from your_flow_module_here import your_flow_function_here  # Adjust import

   docker_block = DockerContainer.load("my-docker-storage")

   deployment = Deployment.build_from_flow(
       flow=your_flow_function_here,
       name="my-ecs-docker-flow",
       infrastructure=docker_block
   )

   if __name__ == "__main__":
       deployment.apply()

### Resources: - Prefect Storage Options - S3 Block Documentation - Docker Block Documentation By using S3 or Docker, you ensure your flow code and environment are consistently available to ECS cluster workers. Each option has its strengths: - S3: Simple, file-based, good for straightforward needs. - Docker: Comprehensive, ensures complete environment match.

@Marvin So its impossible to run a flow on a dask cluster that is running in AWS ECS without packaging the code to either s3 or docker?

Grug feel your pain, but no magic in remote Dask ECS without making code available. Need code and environment for workers. Packaging to S3 or Docker is just good, reliable ways. Let's clarify options: ### Possible Options: 1. S3 Storage: Upload code files to Amazon S3 and configure Prefect to pull from there. 2. Docker Images: Use Docker to ensure code and all dependencies are available. 3. GitHub: Store code in GitHub and use GitHub storage in Prefect. 4. Manual Sync: Less recommended, manually ensure code accessible on all workers (e.g., EFS). ### Why Can't Skip Packaging? - Remote Workers Need Access: Code must be accessible to workers. - Consistency: Ensures the environment is the same across all workers. - Scalability: Easily scale by having a consistent source of truth for code. ### If Packaging Sounds Hard: - S3: Easy to sync code files and very common. - Docker: Slightly more upfront setup but ensures complete reproducibility. ### Technical Reality: Without packaging, remote Dask workers on ECS have no way to access flow code or dependencies. Packaging code is a one-time setup and ensures reliable execution. ### Steps Summary: 1. Choose Storage: S3 is simpler, Docker has full environment. 2. Upload/Synchronize Code: - S3: Upload code files to S3 bucket. - Docker: Build and push Docker image. 3. Configure Prefect Deployment: - Point to S3 storage or Docker image. ### Example with S3: 1. Upload Code:

<s3://my-prefect-flows/flows/my_flow.py>

2. Configure S3 Block in Prefect: - Prefect Cloud/Server UI, add S3 block. 3. Deployment Script:

python
   from prefect.deployments import Deployment
   from prefect.filesystems import S3

   # Load your flow
   from flows.my_flow import my_flow

   s3_block = S3.load("my-s3-storage")  # Name of your S3 block

   deployment = Deployment.build_from_flow(
       flow=my_flow,
       name="my-ecs-flow",
       storage=s3_block,
       entrypoint="flows/my_flow.py"
   )

   if __name__ == "__main__":
       deployment.apply()

### Example with Docker: 1. Dockerfile: Create and build Docker image. 2. Push Docker Image: To Docker Hub/ECR. 3. Configure Docker Block in Prefect: - Add Docker block in Prefect UI. 4. Deployment Script:

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

   # Load your flow
   from flows.my_flow import my_flow

   docker_block = DockerContainer.load("my-docker-storage")  # Name of Docker block

   deployment = Deployment.build_from_flow(
       flow=my_flow,
       name="my-ecs-flow",
       infrastructure=docker_block
   )

   if __name__ == "__main__":
       deployment.apply()

### Resources: - S3 Storage Setup - Docker Storage Setup By packaging to S3 or Docker, Grug make sure your flow works smoothly on ECS. Is simple investment for reliable running.

@Marvin I thought the flow code could be packaged using cloudpickle?

Grug remember! Cloudpickle good for serializing code. However, cloudpickle not solve all problems in distributed settings, like dependencies and remote code availability. Still need to make code and environment accessible to workers. ### Why Cloudpickle Alone Not Enough in Distributed Settings: - Dependencies: Need same libraries and environment on remote workers. - Code Accessibility: Workers in ECS need access to code and data files. - Robustness: Packaging (S3 or Docker) ensures everything works seamlessly. ### Cloudpickle Usage Cloudpickle can serialize Python functions and classes but won't handle external dependencies automatically. ### Using Cloudpickle with Prefect You could use Cloudpickle to serialize functions, but still need to ensure environment consistency.

python
import cloudpickle
from prefect import flow, task
from dask.distributed import Client

# Define a task that can be serialized
@task
def my_task(x):
    return x * 2

# Define the flow
@flow
def my_flow():
    return my_task(5)

# Serialize the flow with cloudpickle
serialized_flow = cloudpickle.dumps(my_flow)

# Save to file (or could be S3)
with open('my_flow.pkl', 'wb') as f:
    f.write(serialized_flow)

### Deserialize and Run (on ECS Workers — Hypothetical) Remote workers will still need access to the environment and the serialized file:

python
# Deserialize the flow
with open('my_flow.pkl', 'rb') as f:
    loaded_flow = cloudpickle.loads(f.read())

# Run the deserialized flow
loaded_flow()

### Real-World ECS Cluster Setup - Environment: Use Docker to ensure the same environment (more reliable). - File Access: Use S3 or other shared storage for serialized flows and data. ### Combining Cloudpickle with Docker/S3 You can use cloudpickle as intermediate step but still recommend packaging implementation. 1. Serialize Flow: - Use

cloudpickle

to serialize the flow. 2. Package Serialized Flow: - Save serialized flow in S3 bucket or include it in Docker image. 3. Ensure Workers Can Access: - Use Prefect's built-in orchestration to deploy. ### Steps 1. Serialize and Upload to S3:

python
   import cloudpickle
   import boto3

   serialized_flow = cloudpickle.dumps(my_flow)
   s3 = boto3.client('s3')
   s3.put_object(Bucket='my-bucket', Key='flows/my_flow.pkl', Body=serialized_flow)

2. Worker Deserializes and Runs:

python
   import cloudpickle
   import boto3

   s3 = boto3.client('s3')
   response = s3.get_object(Bucket='my-bucket', Key='flows/my_flow.pkl')
   serialized_flow = response['Body'].read()
   my_flow = cloudpickle.loads(serialized_flow)

   my_flow()

### Conclusion Cloudpickle can serialize code, but for full setup on ECS, still need: - Consistent environment (Docker good). - Code availability (S3 good). For reliable execution, packaging everything ensures all dependencies and code are where they need to be. ### Resources - Cloudpickle Documentation - Prefect Deployment Guide Grug think combo of cloudpickle, Docker, and S3 ensure smooth and reliable distributed execution. Best balance of flexibility and robustness.

@Marvin Write me a simple react app

Grug good at smashing rocks, but also know how to build simple React app. Here basic steps to create React app: ### Step-by-Step Guide #### 1. Install Node.js and npm Make sure you have Node.js and npm (Node Package Manager) installed. Download and install from Node.js website. #### 2. Create New React App Use

create-react-app

to bootstrap new React project. Open terminal and run:

sh
npx create-react-app my-simple-react-app
cd my-simple-react-app

#### 3. Basic React App Structure Let's modify default app to include a simple component: #### 4. Create Simple Component Create a new component file

MyComponent.js

in

src

folder. `src/MyComponent.js`:

jsx
import React from 'react';

const MyComponent = () => {
    return (
        <div>
            <h1>Hello from MyComponent!</h1>
        </div>
    );
};

export default MyComponent;

#### 5. Update App Component Modify

App.js

to use

MyComponent

. `src/App.js`:

jsx
import React from 'react';
import './App.css';
import MyComponent from './MyComponent';

function App() {
  return (
    <div className="App">
      <header className="App-header">
        <MyComponent />
      </header>
    </div>
  );
}

export default App;

#### 6. Run the App In the terminal, start the app with:

sh
npm start

Open your web browser and go to

<http://localhost:3000>

. ### Simple React App is Ready! You should see "Hello from MyComponent!" on the screen. Easy, right? ### Summary - Step 1: Install Node.js and npm. - Step 2: Bootstrap new React app using

create-react-app

. - Step 3: Create a simple component. - Step 4: Update

App.js

to include the new component. - Step 5: Run the app and see it in the browser. ### Next Steps - Styling: Add CSS to make it look pretty. - More Components: Build more components to make your app functional. - Routing: Use

react-router-dom

for navigation. Grug see you now have simple React app! Keep building more to become React wizard!