Backend Engine

Currently, the default engine (and the array type) is NumPy. Users can switch to various of supported backend engines for computational acceleration. Currently the following engines are supported in CPU and GPU runtime.

• NumPy (Default)

• JAX

• TensorFlow

• PyTorch

• Dask

The backend engine can be switched in the following ways.

Local

Users can use context manager use_backend in a with statement to temporarily switch the NumPy engine.

In the following example, to fit a PCA model, a NumPy array of daily returns daily_returns is passed into fit method. Use the function use_backend to switch the engine temporarily from NumPy to Tensorflow

from fpm_risk_model.engine import use_backend

with use_backend("tensorflow"):
    model = PCA(n_components=10, speedup=False)
    model.fit(daily_returns)

Looking into the type of the fitted factor returns, you will see Tensor type rather than NumPy array type.

print(model.factor_returns.__class__.__name__)    # Tensor type

Global

In the meantime, users can switch the backend engine in global with function set_backend.

For example, the following code switches the library backend to Tensorflow in the whole session.

from fpm_risk_model.engine import set_backend

set_backend("tensorflow")

Environment variable

Set the environment variable FPM_BACKEND_ENGINE before starting the process.

For example, to start the IPython with setting the default backend engine to torch, launch the terminal as below

FPM_BACKEND_ENGINE=torch ipython

Reference

For further details, please find the following notebook example.