Designing open-source libraries for modern machine learning “Don’t repeat yourself”, or DRY, is a well-known principle of software development. The principle originates from “The pragmatic programmer”, one of the most read books on code design. The principle’s simple message    

Santa Clara and San Francisco, CA, April 12th, 2022 Powered by deep learning, transformer models deliver state-of-the-art performance on a wide range of machine learning tasks, such as natural language processing, computer vision, speech, and more. However, training them at scale often requires a large amount of computing power, making the whole process unnecessarily long, complex, and costly. Today,

Hey friends! Welcome to Machine Learning Experts. I’m your host, Britney Muller and today’s guest is Lewis Tunstall. Lewis is a Machine Learning Engineer at Hugging Face where he works on applying Transformers to automate business processes and solve MLOps challenges. Lewis has built ML applications for startups and enterprises in the domains of NLP, topological data analysis, and time    

Climate change is one of the greatest challenges that we are facing and reducing emissions of greenhouse gases such as carbon dioxide (CO2) is an important part of tackling this problem. Training and deploying machine learning models will emit CO2 due to the energy usage of the computing infrastructures that are used: from GPUs to storage, it all needs energy to function and emits CO2 in the process. Pictured: Recent Transformer models and their carbon footprints The amount of CO2 […]

Given that machine learning will make up the overwhelming majority of software development and that non-technical people will be exposed to AI systems more and more, one of the main challenges of AI is adapting and enhancing employee skills. It is also becoming necessary to support teaching staff in proactively taking AI’s ethical and critical issues into account. As an open-source company democratizing machine    

Understanding your users’ needs is crucial in any user-related business. But it also requires a lot of hard work and analysis, which is quite expensive. Why not leverage Machine Learning then? With much less coding by using Auto ML. In this article, we will leverage HuggingFace AutoTrain and Kili to build an active learning pipeline for text classification. Kili    

In this post we will look at how we can leverage Accelerate Library for training large models which enables users to leverage the latest features of PyTorch FullyShardedDataParallel (FSDP).