Running Locally

Make predictions on-device.

Function has experimental support for creating prediction functions that run entirely on the local device. These are called edge predictors, and enable building highly interactive, cross-platform functionality.

Creating the Predictor

Creating an edge predictor follows the same steps as cloud predictors. Create a predictor.ipynb notebook and add the following code cell:

from math import pi

def predict (radius: float) -> float:
    """
    Compute the area of a circle given its radius.
    """
    return pi * radius ** 2

Now, we can provision the predictor on Function in order to run it on any device, fully locally:

# Open a terminal and run the following command:
fxn create @username/area predictor.ipynb --edge --overwrite

Current Limitations

Edge predictors are currently in alpha. As such, there are major limitations compared to cloud predictors:

1. Only a very limited subset of Python is supported. We will be expanding support for more Python language features over time. See GitHub ↗
2. For security, all file I/O is restricted. Predictors must only operate on data explicitly passed in as an argument.

Technical Considerations

At its core, Function is a Python compiler and optimizer. Given a Python function, we transpile the function to C++ then cross-compile for the following platforms:

To transpile Python to C++, we map each Python operation to a set of equivalent implementations in C++. We then take the cartesian product of these mappings to generate a set of C++ functions for a single Python function.

The combinatorics involved require us to take a different approach to performance optimization. We compile all emitted C++ functions, serve them to users, and use telemetry data to choose the best C++ function for a unique device.