Learn about how Docker simplifies access to NVIDIA GPUs, AWS Inferentia and scaling ML containers on Kubernetes — If you told me a few years ago that data scientists would be using Docker containers in their day to day work, I wouldn’t have believed you. As a member of the broader machine learning (ML) community I always considered Docker, Kubernetes, Swarm (remember that?) exotic infrastructure tools for IT/Ops…

Efficient algorithms and methods in machine learning for AI accelerators — NVIDIA GPUs, Intel Habana Gaudi and AWS Trainium and Inferentia — Chances are you’re reading this blog post on a modern computer that’s either on your lap or on the palm of your hand. This modern computer has a central processing unit (CPU) and other dedicated chips for specialized tasks such as graphics, audio processing, networking and sensor fusion and others…

Learn about CPUs, GPUs, AWS Inferentia, and Amazon Elastic Inference and how to choose the right AI accelerator for inference deployment — Let’s start by answering the question “What is an AI accelerator?” An AI accelerator is a dedicated processor designed to accelerate machine learning computations. Machine learning, and particularly its subset, deep learning is primarily composed of a large number of linear algebra computations, (i.e. matrix-matrix, matrix-vector operations) and these operations can be easily parallelized. …

How to choose the right Amazon EC2 GPU instance for deep learning training and inference — from best performance to the most cost-effective and everything in-between — Just a decade ago, if you wanted access to a GPU to accelerate your data processing or scientific simulation code, you’d either have to get hold of a PC gamer or contact your friendly neighborhood supercomputing center. …

Talks by PyTorch and Meta researchers and contributors at NVIDIA GTC 2023 NVIDIA GPU Technology Conference (GTC) is one of those unique conferences that’s at the intersection of innovations in AI research and AI hardware & infrastructure. …

How to use free-to-use Amazon SageMaker Studio Lab and Amazon SageMaker together for your machine learning projects — As a machine learning practitioner, you likely spend most of your time in a Jupyter based IDE for development, you use CPU/GPU resources for training and testing, and need a descent sized storage for datasets and metadata. Those are your most basic necessities for an ML development environment, and SageMaker…

The only re:invent AI/ML launch summary you’ll need — It’s the most wonderful time of the year — for machine learning in the cloud that is. AWS announced several exciting services for AI and machine learning this week at re:invent 2021. In this post I’m going to break down all the ML deliciousness for you, service by service…

How to organize your machine learning experiments, trials, jobs and metadata with Amazon SageMaker and gain peace of mind — The word “experiment” means different things to different people. For scientists (and hopefully for rigorous data scientists), an experiment is an empirical procedure to determine if an outcome agrees or conflicts with some hypothesis. In a machine learning experiment, your hypothesis could be that a specific algorithm (say gradient boosted…

Lower your deep learning training costs with Managed Spot Training on Amazon SageMaker — One of the simplest ways to lower your machine learning training costs is to use Amazon EC2 Spot instances. Spot instances allow you to access spare Amazon EC2 compute capacity at a steep discount of up to 90% compared to on-demand rates. So why not always use Spot instances? Well…