@Glime Why are

ints

in python

dicts

converted to

float

when I pass them from one flyte task to another?

The conversion of

ints

to

floats

in Python dictionaries when passed between Flyte tasks is not directly addressed as a feature or behavior in the provided context. However, this behavior can be inferred as a consequence of how Flyte handles data serialization and deserialization, particularly due to the limitations of the underlying data interchange formats and type systems. Flyte uses Google's Protocol Buffers (protobuf) for serializing and deserializing task inputs and outputs. As mentioned in one of the context sources, there is a limitation in Google's protobuf library that doesn't distinguish between

int

and

float

types. This limitation likely leads to the observed behavior where integers are converted to floats, as protobuf does not preserve the exact Python type but instead focuses on a compatible type that can represent the data across different languages and systems. Moreover, the issue of

ints

being recast as

floats

is acknowledged in a discussion where it's mentioned that this behavior is "kind of hacky...but works." This suggests that the behavior is a known limitation rather than a bug, and workarounds or specific handling might be necessary to preserve integer types when passing data between tasks. Additionally, Flyte's strong typing system, which is designed to ensure data compatibility and integrity across tasks and workflows, relies on Python type hints and its own type system to map Python types to Flyte types. While this system is effective for catching bugs and ensuring data integrity, the nuances of how data types are represented in the serialization format (protobuf) can lead to the observed conversion behavior. In summary, the conversion of

ints

to

floats

when passing Python dictionaries between Flyte tasks is likely a consequence of the limitations in the serialization/deserialization process, specifically related to the protobuf library's handling of numeric types. This behavior underscores the importance of being aware of the underlying data representation and type system interactions when designing workflows in Flyte. --- This is an AI-generated response, take its recommendations with caution. Please help us improve the model accuracy by giving this response a thumbs up/thumbs down reaction. Sources https://discuss.flyte.org/t/12041237/hello-all-i-m-trying-to-return-machine-learning-models-to-fi/ https://docs.flyte.org/projects/cookbook/en/stable/auto_examples/data_types_and_io/index.html/ 👍 button 👎 button