Tutorial: Using the MLBench Commandline Interface

We recently released MLBench version 2.1.0, which contains a new commandline interface, making it even easier to run our benchmarks.

In this post we’ll introduce the CLI and show you how easy it is to get it up and running.

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Tutorial: Adding an existing PyTorch model to an MLBench task

In this tutorial, we will go through the process of adapting existing distributed PyTorch code to work with the MLBench framework. This allows you to run your models in the MLBench environment and easily compare them with our reference implementations as baselines to see how well your code performs.

MLBench is designed to easily be used with third-party models, allowing for quick and fair comparisons and saving all of the hassle that’s needed to implement your own baselines for comparison.

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Google Cloud Tutorial

This tutorial guides you through setting up MLBench in a Google Cloud Kubernetes Engine cluster and explains basic MLBench functionality. For setup in other environments, please refer to our installation documentation. We use Google Cloud as an example, but MLBench runs in any Kubernetes cluster.

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Introducing MLBench

MLBench is a framework for distributed machine learning. Its purpose is to improve transparency, reproducibility, robustness, and to provide fair performance measures as well as reference implementations, helping adoption of distributed machine learning methods both in industry and in the academic community.

The MLBench Dashboard

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