Lightning AI, the company behind PyTorch Lightning, with over 91 million downloads, announced the introduction of Lightning AI Studios, the culmination of 3 years of research into the next generation development paradigm for the age of AI.
Software 1.0 such as web apps, servers, etc, can be efficiently built on laptops. But for software 2.0, the age of AI, laptops are missing the 1000s of GPUs, Terabytes of storage and collaborative capabilities needed for this new paradigm. Lightning Studios are cloud-based virtual environments where AI researchers and developers can code on the browser or from their laptops to develop and ship AI together.
PyTorch Lightning has become the standard for training large-scale models, such as Stable Diffusion with over 10,000 companies using it to build AI at scale. Using this knowledge, the team at Lightning AI, rethought, from the ground up, what AI development on the cloud should feel like.
“What we did for AI frameworks with PyTorch Lightning, we’re doing for AI development with Lightning AI Studios,” said Will Falcon, founder and CEO of Lightning AI and creator of PyTorch Lightning. “ML tooling has not meaningfully evolved in decades – until now. Just like a smartphone brings together all the tools we previously carried separately – a phone, a camera, a calculator, you name it! – Lightning AI Studios brings together everything needed to build and deploy AI products at scale in one, easy-to-use platform.”
Developers today, string together 20 platforms to monitor, train, serve, prep data, host apps, etc. Lightning AI Studio unites all those tools into a single, cohesive experience that lets them stay focused on your work without context switching. A Lightning Studio has apps that do specialized work, such as code on the cloud, multi-node training, distributed data preparation, or hosting and sharing AI web apps. Developers can also write their own Studio apps to fully personalize the platform to their workflows.
“We have some developers who run 100s of variations of a model (sweep) at once, or train a single model on hundreds of GPUs without problems,” added Falcon. “They love the instant feedback they get when things fail. I wanted to introduce the iPhone of AI tooling, and I think this has the potential to be that.”
Lightning fundamentally re-architects how developers work by abstracting away every non-core activity and providing a single interface for all AI development needs. Users can:
- jumpstart with pre-built templates;
- scale from CPU to GPU to as many machines as they need at the click of a button;
- leverage natively integrated tools or build their own and deploy anywhere – their cloud, or Lightning AI’s, or on their local GPU cluster.
Lightning AI Studios is already in use by individual developers, start-ups, mid-sized businesses and Fortune 100 companies. It’s used in a wide range of applications, from drug discovery and clinical trials to fraud prevention and risk analysis.
Sam Wolk, a graduate student in MIT’s Sustainable Design Lab is using Lightning AI Studios in a program dedicated to reducing carbon emissions with buildings and development. They use AI and other tools to simulate different construction scenarios. Using Lightning AI Studios, the Lab has found it easier to collaborate and organize code, improve productivity and drastically reduce time to train models.
“When developers are in a state of flow, we want to keep working, and Lightning AI Studios keeps us there because we only need to switch the machine. I don’t need to be concerned about environment management, images or dependency management and the Lightning framework handles all the boilerplate work. This has resulted in a model that only took us six hours to develop with Lightning AI Studios that had previously taken us 36,” said Wolk.
Users can get started free without a credit card. They’ll get 15 free credits per month, and can buy more credits as they go. For enterprise users, Lightning AI can be deployed on their cloud accounts and private infrastructure so they can train and deploy models on their private data securely.
There are four pricing tiers: a free level for individual developers; a pro level for engineers, researchers and scientists; a teams level for start-ups and teams; and finally for larger organizations in need of enterprise-grade AI, the enterprise level.
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