MLCube examples

The machine learning (ML) community has seen an explosive growth and innovation in the last decade. New models emerge on a daily basis, but sharing those models remains an ad-hoc process. Often, when a researcher wants to use a model produced elsewhere, they must waste hours or days on a frustrating attempt to get the model to work. Similarly, a ML engineer may struggle to port and tune models between development and production environments which can be significantly different from each other. This challenge is magnified when working with a set of models, such as reproducing related work, employing a performance benchmark suite like MLPerf, or developing model management infrastructures. Reproducibility, transparency and consistent performance measurement are cornerstones of good science and engineering.

The field needs to make sharing models simple for model creators, model users, developers and operators for both experimental and production purpose while following responsible practices. Prior works in the MLOps space have provided a variety of tools and processes that simplify user journey of deploying and managing ML in various environments, which include management of models, datasets, and dependencies, tracking of metadata and experiments, deployment and management of ML lifecycles, automation of performance evaluations and analysis, etc.

We propose an MLCube, a contract for packaging ML tasks and models that enables easy sharing and consistent reproduction of models, experiments and benchmarks amidst these existing MLOps processes. MLCube differs from an operation tool by acting as a contract and specification as opposed to a product or implementation.

This repository contains a number of MLCube examples that can run in different environments using MLCube runners.

1. MNIST MLCube downloads data and trains a simple neural network. This MLCube can run with Docker or Singularity locally and on remote hosts. The README file provides instructions on how to run it. MLCube documentation provides additional details.
2. Hello World MLCube is a simple exampled described in this tutorial.
3. EMDenoise MLCube downloads data and trains a deep convolutional neural network for Electron Microscopy Benchmark. This MLCube can only run the Docker container. The README file provides instructions on how to run it.
4. Matmul Matmul performs a matrix multiply.