The iPhone was a game changer because it was not only a powerful computer in your pocket, but also a platform for countless applications at the touch of your fingers. This is the intuition behind Lightning Studio, a platform that brings together numerous new AI tools into his single interface.
Lightning AI, the company behind the widely used PyTorch Lightning framework, announced the availability of Lightning Studio to streamline the development and deployment of AI products. This release also includes Thunder, a powerful new open source source-to-source compiler for PyTorch designed to accelerate training and serving generative AI models across multiple GPUs.
Lightning Studio aims to address the challenges faced by developers in the AI industry by providing a unified interface for all your AI development needs.
“Software 1.0, such as web apps and servers, can be efficiently built on a laptop. Software 2.0, especially AI, requires thousands of GPUs, terabytes of storage, and collaboration capabilities,” says Lightning. said AI CEO Will Falcon. “Studio is a cloud-based virtual environment where AI researchers and developers can collaborate to develop and ship AI by coding in a browser from their laptop.”
The platform offers a variety of features, including pre-built templates, seamless scaling from CPU to GPU, and the ability to leverage natively integrated tools or build custom tools. Developers can deploy AI products anywhere: in their own cloud, in Lightning AI's cloud, or on a local GPU cluster.
One of Lightning Studio's key innovations is its app-based architecture. Falcon compared it to the iPhone, saying, “Apple didn't make the flashlight. They didn't make the calculator. They didn't make Spotify. They didn't make Uber. They didn't make Venmo. It's not like I did it,” he said. “We didn't build PayPal. These are all third-party products integrated through the iPhone. So we're doing something similar.”
Lightning Studio integrates a variety of third-party and in-house AI development tools into a single platform, giving developers multiple single-point solutions for tasks such as monitoring, training, delivery, and data preparation. No need to switch. , and hosting apps.
Thunder, a new open source compiler introduced as part of the release, is the culmination of two years of research into a next-generation deep learning compiler built with support from NVIDIA. Designed to efficiently train and serve modern generative AI models across multiple GPUs, achieving up to 40% speedup compared to unoptimized code in real-world scenarios. Masu. These speedups save weeks of training time and significantly reduce training costs.
Thunder leverages NVIDIA's best-in-class executors and software, including cuDNN, nvFuser, and torch.compile, as well as OpenAI's Triton. This allows developers to use all executors simultaneously, allowing each executor to handle the mathematical operations for which it is designed.
The Thunder team is led by Dr. Thomas Viehmann, who is best known for his early work on PyTorch, his important contributions to TorchScript, and for being the first to run PyTorch on a mobile device. His expertise is expected to drive future performance breakthroughs that will benefit the PyTorch and Lightning AI communities.
“Our collaboration with Lightning AI to integrate NVIDIA technology into Thunder will help the AI community improve training efficiency on NVIDIA GPUs and deliver larger, higher-performing AI models.” said Christian Sarofeen, director of deep learning frameworks at NVIDIA.
Lightning AI Studio pricing is based on a tiered model, with free tiers for individual developers, professional tiers for engineers, researchers, and scientists, team tiers for startups and teams, and large tiers for enterprise needs. A scale organization has an enterprise level. -Grade AI. Thunder itself is open source under the Apache 2.0 license.
With the release of Lightning Studio and Thunder, Lightning AI aims to revolutionize the way AI products are developed and deployed, making the process more efficient, collaborative, and available to a wider range of developers and organizations. Masu.
As the AI community begins to train increasingly sophisticated models across multiple modalities, these tools allow developers to use This allows you to get the most out of your processing resources.
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