@Marvin, if I'm creating a task that retrieve a large amount of data and returns a python dict of simple objects (basic types), does passing the return to another task value creates a copy?

When a task in Prefect returns a large amount of data, such as a Python dictionary, and this data is passed to another task, Prefect handles the data through serialization and deserialization. This process does not inherently create a deep copy of the data. Instead, Prefect uses serialization to transfer data between tasks, which can result in a new instance of the data being created on the receiving end. Here's a summary of how it works: 1. Serialization: When a task completes and its result is passed to another task, Prefect serializes the result. This involves converting the data into a format suitable for storage and transmission. 2. Deserialization: When the receiving task starts, Prefect deserializes the data back into a Python object. 3. Data Handling: This serialization process can be customized using Pydantic models or custom cache key functions, as mentioned in the task caching documentation. Thus, while it might seem like a copy due to the serialization and deserialization steps, it's not a direct in-memory copy operation. For more information, you can review the task caching documentation. If you need to ensure specific data handling behavior, consider utilizing Pydantic models or custom serialization logic. If you have any preferences or specific requirements, feel free to share them!

I am getting confused while deploying prefect in ec2. Can I get sample instructions on how to deploy prefect core on ec2