Chainer vs MLflow

MLflow vs Chainer: What are the differences?

What is MLflow? An open source machine learning platform. MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

What is Chainer? A Powerful, Flexible, and Intuitive Framework for Neural Networks. It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.

MLflow and Chainer can be categorized as "Machine Learning" tools.

Some of the features offered by MLflow are:

• Track experiments to record and compare parameters and results
• Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production
• Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms

On the other hand, Chainer provides the following key features:

• Supports CUDA computation
• Runs on multiple GPUs
• Supports various network architectures

MLflow and Chainer are both open source tools. Chainer with 4.98K GitHub stars and 1.32K forks on GitHub appears to be more popular than MLflow with 32 GitHub stars and 15 GitHub forks.