Yes, there are two main ways to retrieve the next scheduled run time for a deployment: 1. Using the CLI:

bash
prefect deployment list-schedules

This command will show you all scheduled deployments and their next run times. 2. Using the Python API:

python
from prefect.client import get_client

async with get_client() as client:
    # Replace deployment_id with your actual deployment ID
    scheduled_runs = await client.get_scheduled_flow_runs_for_deployments(
        deployment_ids=["your-deployment-id"]
    )

The

list-schedules

command is particularly useful for a quick overview, while the API method gives you more programmatic control and additional filtering options. For more detailed information about a specific deployment's schedule, you can also use:

bash
prefect deployment inspect "deployment-name/deployment-tag"

You can find more details about working with schedules in the schedules documentation and deployment schedules guide.

Based on my research, here's what you need to know about flow parameter size limits in Prefect: 1. There is no explicit size limit imposed by Prefect itself for flow parameters. 2. However, there are some practical considerations: - Parameters are stored in the Prefect database, so very large parameters can impact database performance - Python's memory limitations will apply since parameters are held in memory - For UI and API performance reasons, it's recommended to keep parameters reasonably sized Best Practices: 1. For large datasets, instead of passing the data directly as parameters, pass references (like file paths, URLs, or database queries) 2. Use blocks or external storage solutions for handling large data 3. Consider breaking down flows that need to process large amounts of data into smaller, more manageable pieces Example of recommended approach:

python
@flow
def process_data(data_url: str):  # Pass reference instead of data
    # Download or access data inside the flow
    data = download_data(data_url)
    # Process the data
    process(data)

Instead of:

python
@flow
def process_data(large_data: bytes):  # Not recommended for large data
    process(large_data)

If you're dealing with large amounts of data, consider using Prefect's storage blocks or external data storage solutions like S3, GCS, or databases to manage the data more efficiently.

Yes, you are correct! In Prefect 3.x, there is indeed a hard limit of 512kb for flow run parameters. This is a documented limitation in the platform. When working with data larger than 512kb, you should: 1. Use references instead of raw data (URLs, file paths, etc.) 2. Use Prefect's storage blocks for larger data 3. Pass identifiers or metadata instead of the actual data 4. Consider breaking down large datasets into smaller chunks Example of the recommended approach:

python
@flow
def process_large_data(file_path: str):  # Pass file path instead of data
    # Read and process data inside the flow
    with open(file_path, 'rb') as f:
        data = f.read()
    process(data)

This limitation helps ensure system stability and performance, particularly for the Prefect UI and API. If you need to work with larger parameters, you'll need to restructure your flow to handle the data loading within the flow rather than passing it as a parameter.