From the Creators of PyTorch Lighting. Democratizing State of the Art AI Research by Focusing on Machine Learning not Infrastructure.

Grid in 3 minutes Introduction Grid is designed for developing and training deep learning models at scale. The TL;DR of using Grid is this: Create a DATASTORE with your dataset. Spin up an interactive SESSION to develop, analyze and prototype models/ideas. When you have something that works, train it at scale via RUN.

With mobile web support, the Grid platform makes it easy to track experiments and manage to compute on the go. Managed service running in the Grid.ai cloud or your own VPC.

Learn more about Grid.ai and how we make it easy to manage cloud infrastructure to quickly iterate through the model development life cycle.

What is Lightning AI (and Where Does Grid.ai Fit With It)? By Femi Ojo, Senior Support Engineer Lightning AI. Lightning is a free, modular, distributed, and open-source framework for building Lightning Apps where the components you want to use interact together. How is Lightning AI Different from Grid.ai? Lighting AI is the evolution of Grid.ai.

We’ve got some pretty big news today – Grid.ai is now Lightning AI! This evolution of our company’s identity echoes our expanding focus on unifying the entire artificial intelligence development lifecycle.

Grid.ai was founded in 2019 by the original creators of PyTorch Lightning, with origins in the academic and open source community. Headquartered in New York City, the team is a remote-first workforce distributed around the globe.

Explore Grid's Resources. Here you'll find the latest news, media, tutorials, user stories, events and more!

Billing for the Community plan is invoiced as services are used. Teams and Enterprise plans are invoiced annually with flexible deployment and usage options. Grid.ai invoices monthly for your cloud usage compute fees.

Grid.ai’s SaaS platform, Grid, enables researchers to quickly train advanced ML models on-cloud without having to invest in their own computing capabilities. With Grid, researchers can focus on preparing the training datasets and experimenting with different learning algorithms, leaving it up to Grid’s infrastructure to crunch the data.