PyCave Documentation

PyCave allows you to run traditional machine learning models on CPU, GPU, and even on multiple nodes. All models are implemented in PyTorch and provide an Estimator API that is fully compatible with scikit-learn.

https://img.shields.io/pypi/v/pycave?label=version https://img.shields.io/pypi/l/pycave

Features

  • Support for GPU and multi-node training by implementing models in PyTorch and relying on PyTorch Lightning

  • Mini-batch training for all models such that they can be used on huge datasets

  • Well-structured implementation of models

    • High-level Estimator API allows for easy usage such that models feel and behave like in scikit-learn

    • Medium-level LightingModule implements the training algorithm

    • Low-level PyTorch Module manages the model parameters

Installation

PyCave is available via pip:

pip install pycave

If you are using Poetry:

poetry add pycave

Usage

If you've ever used scikit-learn, you'll feel right at home when using PyCave. First, let's create some artificial data to work with:

import torch

X = torch.cat([
    torch.randn(10000, 8) - 5,
    torch.randn(10000, 8),
    torch.randn(10000, 8) + 5,
])

This dataset consists of three clusters with 8-dimensional datapoints. If you want to fit a K-Means model, to find the clusters' centroids, it's as easy as:

from pycave.clustering import KMeans

estimator = KMeans(3)
estimator.fit(X)

# Once the estimator is fitted, it provides various properties. One of them is
# the `model_` property which yields the PyTorch module with the fitted parameters.
print("Centroids are:")
print(estimator.model_.centroids)

Due to the high-level estimator API, the usage for all machine learning models is similar. The API documentation provides more detailed information about parameters that can be passed to estimators and which methods are available.

GPU and Multi-Node training

For GPU- and multi-node training, PyCave leverages PyTorch Lightning. The hardware that training runs on is determined by the pytorch_lightning.trainer.Trainer class. It's __init__() method provides various configuration options.

If you want to run K-Means with a GPU, you can pass the option gpus=1 to the estimator's initializer:

estimator = KMeans(3, trainer_params=dict(gpus=1))

Similarly, if you want to train on 4 nodes simultaneously where each node has one GPU available, you can specify this as follows:

estimator = KMeans(3, trainer_params=dict(num_nodes=4, gpus=1))

In fact, you do not need to change anything else in your code.

Implemented Models

Currently, PyCave implements three different models. Some of these models are also available in scikit-learn. In this case, we benchmark our implementation against their (see here).

GaussianMixture

Probabilistic model assuming that data is generated from a mixture of Gaussians.

MarkovChain

Probabilistic model for observed state transitions.

KMeans

Model for clustering data into a predefined number of clusters.

Reference

Index