Yes, it is absolutely possible to deploy flows that span multiple Python files to S3 in Prefect 3.x. Let me explain how this works and provide some guidance. Here's how you can approach this: 1. Structure Your Code Your project can be structured with multiple Python files, for example:

my_project/
├── main.py           # Contains your flow definition
├── utils.py          # Helper functions
└── dependencies.py   # Other dependencies

2. Set Up S3 Storage First, you'll need to configure an S3 bucket and set up the necessary Prefect blocks:

python
from prefect_aws import S3Bucket, AwsCredentials

# Create AWS credentials block if not already existing
aws_creds = AwsCredentials(
    aws_access_key_id="YOUR_ACCESS_KEY",
    aws_secret_access_key="YOUR_SECRET_KEY"
)
aws_creds.save("my-aws-creds")

# Create S3 bucket block
s3_block = S3Bucket(
    bucket_name="your-bucket-name",
    credentials=aws_creds
)
s3_block.save("my-s3-bucket")

3. Deploy Your Multi-file Flow When deploying, you'll use the

from_source

pattern. Here's an example:

python
from prefect import flow
from prefect.deployments import Deployment

@flow
def my_flow():
    # Your flow code here
    pass

if __name__ == "__main__":
    my_flow.from_source(
        source="<s3://your-bucket-name/path/to/your/code>",
        entrypoint="main.py:my_flow",  # Points to the flow in your main.py
    ).deploy(
        name="my-s3-deployment",
        work_pool_name="your-work-pool"
    )

Key points to remember: - All necessary files must be available in your S3 bucket - The

entrypoint

parameter should point to the specific file and flow function that serves as the entry point - Make sure your worker has access to the S3 bucket and appropriate AWS credentials - Any dependencies used across files should be properly imported in your code - Include all required packages in your requirements.txt or environment configuration For best practices: 1. Keep your code structure modular and well-organized 2. Use relative imports between your Python files 3. Consider using a

prefect.yaml

file to manage deployment configuration 4. Ensure all dependencies are properly specified in your requirements.txt 5. Use environment variables or Prefect blocks for sensitive information like AWS credentials Remember that when the worker runs your flow, it will pull all necessary files from S3, so make sure your S3 bucket structure matches your project structure and all required files are accessible. Would you like me to provide more specific details about any part of this process, such as the S3 bucket setup or deployment configuration?

I'll help you modify the code to automatically upload all Python files from the same folder. Here's the adjusted version:

python
from pathlib import Path
import os

def deploy_to_process_pool():
    # Create a Minio S3 configuration block at the Prefect server:
    minio_cred = MinIOCredentials(
        minio_root_user=MINIO_ACCESS_KEY,
        minio_root_password=MINIO_SECRET_KEY,
        region_name="us-east-1",
        aws_client_parameters={"endpoint_url": f"http://{MINIO_ENDPOINT}"}
    )

    s3_bucket_block = S3Bucket(
        bucket_name="prefect-flows",
        credentials=minio_cred,
        bucket_folder="pilot",
    )
    s3_bucket_block.save("minio-storage", overwrite=True)

    # Get the directory containing the current script
    current_dir = Path(__file__).parent

    # First, upload all Python files from the directory
    for file_path in current_dir.glob("*.py"):
        # Skip uploading __pycache__ or other hidden files
        if file_path.name.startswith("__"):
            continue
            
        print(f"Uploading {file_path.name} to MinIO...")
        s3_bucket_block.upload_from_path(
            from_path=file_path,
            to_path=file_path.name
        )

    # Then proceed with flow deployments
    for flow_function, flow_file in FLOWS:
        flow_function_name = flow_function.__name__

        # Now deploy the flow (no need to upload again as it's already done)
        flow_function.from_source(
            source=s3_bucket_block,
            entrypoint=f"{flow_file}:{flow_function_name}",
        ).deploy(
            name=f"{flow_function_name} process deployment",
            work_pool_name=PROCESS_WORK_POOL,
        )

Key changes made: 1. Used

Path(__file__).parent.glob("*.py")

to find all Python files in the current directory 2. Added a check to skip

__pycache__

and other hidden Python files 3. Separated the file upload process from the flow deployment process 4. Added a print statement to track which files are being uploaded If you also need to include subdirectories, you can modify the file discovery part to use

rglob

instead of `glob`:

python
# To include subdirectories, replace the glob loop with:
for file_path in current_dir.rglob("*.py"):
    if file_path.name.startswith("__"):
        continue
        
    # Calculate relative path to maintain directory structure in S3
    relative_path = file_path.relative_to(current_dir)
    print(f"Uploading {relative_path} to MinIO...")
    s3_bucket_block.upload_from_path(
        from_path=file_path,
        to_path=str(relative_path)  # Preserves directory structure in S3
    )

This version will: - Upload all Python files in the directory (and optionally subdirectories) - Maintain the directory structure in your MinIO storage - Skip Python system files - Still deploy your flows as specified in the FLOWS list Remember that: 1. All imported modules in your flows should now be available in MinIO 2. The directory structure in MinIO will match your local structure 3. Make sure your FLOWS list still correctly references the flow files with their proper paths Would you like me to explain any part of this in more detail or help with a specific aspect of the implementation?