Borch

Getting Started | Documentation | Contributing

Borch is a universal probabilistic programming language (PPL) framework developed by Desupervised, that uses and integrates with PyTorch. Borch was designed with special attention to support Bayesian neural networks in a native fashion. Further, it's designed to

• Flexible and scalable framework
• Support neural networks out of the box.
• Have bells and whistles a universal PPL needs.

It can be installed with

pip install borch

Usage

See our full tutorials here.

As a quick example let's look into how the neural network interface looks. The module borch.nn provides implementations of neural network modules that are used for deep probabilistic programming and provides an interface almost identical to the torch.nn modules. In many cases it is possible to just switch

import torch.nn as nn

to

import borch.nn as nn

and a network defined in torch is now probabilistic, without any other changes in the model specification, one also need to change the loss function to infer.vi.vi_loss.

For example, a convolutional neural network can be written as

import torch
import torch.nn.functional as F
from borch import nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]
        num_features = 1
        for s in size:
            num_features *= s
        return num_features

Installation

Borch can be installed using

pip install borch

Docker

The Borch Docker images are available as both CPU and GPU versions at gitlab.com/desupervised/borch/container_registry. The latest CPU images can be used as

docker run registry.gitlab.com/desupervised/borch/cpu:master

Contributing

Please read the contribution guidelines in CONTRIBUTING.md.

Citation

If you use this software for your research or business please cite us and help the package grow!

@misc{belcher2022borch,
	title        = {Borch: A Deep Universal Probabilistic Programming Language},
	author       = {Belcher, Lewis and Gudmundsson, Johan and Green, Michael},
	year         = 2022,
	publisher    = {arXiv},
	doi          = {10.48550/ARXIV.2209.06168},
	url          = {https://arxiv.org/abs/2209.06168},
	copyright    = {Creative Commons Attribution 4.0 International},
	keywords     = {Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Programming Languages (cs.PL), FOS: Computer and information sciences, FOS: Computer and information sciences}
}