Metadata-Version: 2.1
Name: borch
Version: 0.0.2
Summary: Desupervised AI framework
Home-page: UNKNOWN
Author: Desupervised
License: Proprietary
Platform: UNKNOWN
Requires-Python: ~=3.7
Description-Content-Type: text/markdown
Requires-Dist: numpy (==1.17.5)
Requires-Dist: scipy (>=1.2.0)
Requires-Dist: torch (==1.6.0)
Provides-Extra: all-backends
Provides-Extra: docs
Requires-Dist: matplotlib (==2.2.3) ; extra == 'docs'
Requires-Dist: nbsphinx ; extra == 'docs'
Requires-Dist: sphinx (==2.2.2) ; extra == 'docs'
Requires-Dist: sphinx-gallery ; extra == 'docs'
Requires-Dist: sphinx-rtd-theme ; extra == 'docs'
Requires-Dist: sphinxcontrib-bibtex (<2.0.0) ; extra == 'docs'
Requires-Dist: sphinx-versions (==1.0.1) ; extra == 'docs'
Requires-Dist: recommonmark ; extra == 'docs'
Requires-Dist: pillow ; extra == 'docs'
Provides-Extra: examples
Requires-Dist: notebook ; extra == 'examples'
Provides-Extra: lint
Requires-Dist: black (==19.10b0) ; extra == 'lint'
Requires-Dist: isort (==4.3.21) ; extra == 'lint'
Requires-Dist: pylint (==2.4.4) ; extra == 'lint'
Provides-Extra: test
Requires-Dist: coverage (==5.2.1) ; extra == 'test'
Requires-Dist: pytest-cov (==2.8.1) ; extra == 'test'

borch
=====

[![pipeline status](https://gitlab.com/desupervised/borch/badges/master/pipeline.svg)](https://gitlab.com/desupervised/borch/-/commits/master)
[![coverage report](https://gitlab.com/desupervised/borch/badges/master/coverage.svg)](https://gitlab.com/desupervised/borch/-/commits/master)
![Lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)

borch is an artificial intelligence (AI) framework developed by Desupervised.
It's designed to be flexible and scalable framework that can solve problems
using artificial intelligence and machine learning. Doing so by utilizing a
wide ranging toolbox including Bayesian inference, ....

It consists of several sub packages:

- `ppl`: A universal probabilistic programming language
- `infer`: An inference package with support for Bayesian inference methods such
           as Variational Inference (VI), Markov Chain Monte Carlo (MCMC) as well
           as tools for semi-supervised training and many others.
- `utils`: various utility functions



Usage
-----

Run `make help` to see available make targets.



Installation
------------

### Virtual environment

When installing borch we normally use virtual environment to manage the Python version dependencies.
Two good ones are https://virtualenv.pypa.io/en/stable/ and https://docs.conda.io/en/latest/miniconda.html, 
look at them and pick one to use and follow their documentation to crate and activate an environment.

**NB** All installations of python packages should be placed in the correct
environment. Installing packages in the global python interpreter can
result in unexpected behavior, where global packages may be used in favor of
local packages.


### Install locally

Once an appropriate conda environment has been created, run
```
make install
```
to install a production version of borch with support for a GPU, or
```
ARCH=cpu make install
```
for a version that only supports a CPU.

To install in development mode on machine(with no gpu support) run, and all development dependencies.
```
ARCH=cpu make install-dev
```
and for GPU support use
```
make install-dev
```



Docker
------

Currently, all borch docker images are based on Ubuntu 16.04. The GPU image is
based on an Nvidia Cuda version. Both base images are specified as build
arguments which calling `docker build`.

The GPU image can be built using:
```
docker build --build-arg BASE="nvidia/cuda:9.1-cudnn7-runtime-ubuntu16.04" --build-arg ARCH=gpu --build-arg D2_AWS_REGION=$D2_AWS_REGION --build-arg D2_AWS_ACCESS_KEY_ID=$D2_AWS_ACCESS_KEY_ID --build-arg D2_AWS_SECRET_KEY_ID=$D2_AWS_SECRET_KEY_ID --build-arg D2_AWS_ENDPOINT=$D2_AWS_ENDPOINT --pull -t borch-gpu . 
```

And the CPU image using:
```
docker build --build-arg BASE="ubuntu:18.04" --build-arg ARCH=cpu --build-arg D2_AWS_REGION=$D2_AWS_REGION --build-arg D2_AWS_ACCESS_KEY_ID=$D2_AWS_ACCESS_KEY_ID --build-arg D2_AWS_SECRET_KEY_ID=$D2_AWS_SECRET_KEY_ID --build-arg D2_AWS_ENDPOINT=$D2_AWS_ENDPOINT --pull -t borch-cpu .
```

The `D2_AWS_...` envioromental variables are optional, but if not provided functionality that relies on s3 buckets will not work.


Contributing
------------

Please read the contribution guidelines in `CONTRIBUTING.md`.



