SUNNYVALE, Calif.--(BUSINESS WIRE)--Cerebras Systems, the pioneer in high performance artificial intelligence (AI) computing, today released version 1.2 of the Cerebras Software Platform, CSoft, with expanded support for PyTorch and TensorFlow. In addition, customers can now quickly and easily train models with billions of parameters via Cerebras’ weight streaming technology.
PyTorch is the leading machine learning framework. It is used by developers to accelerate the path from research prototyping to production deployment. As model size increases and as transformer models become more popular, it is essential that machine learning practitioners have access to fast, easy to set up and use compute solutions like the Cerebras CS-2. With the CS-2 running CSoft, the developer community has a powerful tool to enable new breakthroughs in AI.
“From the start, our goal was to seamlessly support whichever machine learning framework our customers wanted to write in,” said Emad Barsoum, Senior Director, AI Framework, at Cerebras Systems. “Our customers write in TensorFlow and in PyTorch, and our software stack, CSoft, makes it quick and easy to express your models in the framework of your choice. By doing so, our customers gain access to the 850,000 AI optimized cores and 40 Gigabytes of on-chip memory in the Cerebras CS-2.”
The Cerebras CS-2 is the world’s fastest AI system. It is powered by the largest processor ever built – the Cerebras Wafer-Scale Engine 2 (WSE-2). The Cerebras WSE-2 delivers more AI optimized compute cores, more fast memory, and more fabric bandwidth than any other deep learning processor in existence. Purpose built for AI work, the CS-2 runs CSoft which enables machine learning practitioners to write their models in the opensource frameworks of TensorFlow or PyTorch and, without modification, run the model on the Cerebras CS-2. In fact, a model that was written for a graphics processing unit or a central processing unit can run under CSoft on the Cerebras CS-2 without any changes. With the CS-2 and CSoft, practitioners can seamlessly scale up from small models like BERT to the largest models in existence like GPT-3.
Large models have demonstrated state of the art accuracy on many language processing and understanding tasks. Training these large models using GPU is challenging and time-consuming. Training from scratch on new datasets often takes weeks and 10s of megawatts of power on large clusters of legacy equipment. Moreover, as the size of the cluster grows, power, cost, and complexity grow exponentially. Programming clusters of graphics processing units requires rare skills, different machine learning frameworks, and specialized tools that require weeks of engineering time to each iteration.
The CS-2 was built to directly address these challenges. Setting up even the largest model takes only a few minutes, and the CS-2 is faster than clusters of 100s of graphics processing units. With less time spent in set up, configuration and training, the CS-2 enables users to explore more ideas in less time.
With customers in North America, Asia, Europe and the Middle East, Cerebras is delivering industry leading AI solutions to a growing roster of customers in the enterprise, government, and high performance computing segments including GlaxoSmithKline, AstraZeneca, TotalEnergies, nference, Argonne National Laboratory, Lawrence Livermore National Laboratory, Pittsburgh Supercomputing Center, Edinburgh Parallel Computing Centre (EPCC), and Tokyo Electron Devices.
For more information about the Cerebras Software Platform, please visit https://cerebras.net/software/.
About Cerebras Systems
Cerebras Systems is a team of pioneering computer architects, computer scientists, deep learning researchers, and engineers of all types. We have come together to build a new class of computer system, designed for the singular purpose of accelerating AI and changing the future of AI work forever. Our flagship product, the CS-2 system is powered by the world’s largest processor – the 850,000 core Cerebras WSE-2, enables customers to accelerate their deep learning work by orders of magnitude over graphics processing units.
