minipipe 0.1.2

A machine learning mini-batch pipeline for out-of-memory training

# MiniPipe: A mini-batch pipeline

MiniPipe is a mini-batch pipeline designed for training machine learning models on very large datasets in a streaming fashion, written in pure python. MiniPipe is designed for situations where the data are too large to fit into memory, or when doing so would discourage experiment iterations due to prohibitively long loading and/or processing times.

Instead of a distributed approach to model training MiniPipe is designed around a streaming paradigm that utilizes pipeline parallelism. In the intended use case data are loaded from a data lake one ‘chunk’ at a time. Such an approach requires a training method that allows for iterative training on small batches of data (mini-batches) such as stochastic gradient decent.

A MiniPipe pipeline is build up from pipe segments, which may be connected to form a DAG. In the general case, each pipe segments has two queues, one for accepting upstream data (inputs) and another for passing data downstream (outputs). All pipe segments run on their own process which allows for asynchronous pipeline parallelization (see figure below). Such parallelization can dramatically increase the throughput of a pipeline and reduce training/processing times.

The goal of MiniPipe is to encourage experimentation and prototyping at full data scale by making complex training pipelines simple, flexible and fast. The look and feel of the Minipipe API is based on the very successful Keras Model API, which strikes a good balance between simplicity and flexibility.

## Installation Installation is super easy with pip `sh pip install minipipe `