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OpenAI proposes Triton language as an alternative to Nvidia's CUDA

ZDNet

Graphics processing units from Nvidia are too hard to program, including with Nvidia's own programming tool, CUDA, according to artificial intelligence research firm OpenAI. The San Francisco-based AI startup, which is backed by Microsoft and VC firm Khosla ventures, on Wednesday introduced the 1.0 version a new programming language specially crafted to ease that burden, called Triton, detailed in a blog post, with the link to GitHub source code. OpenAI claims Triton can deliver substantial ease-of-use benefits over coding in CUDA for some neural network tasks at the heart of machine learning forms of AI such as matrix multiplies. "Our goal is for it to become a viable alternative to CUDA for Deep Learning," the leader of the effort, OpenAI scientist Philippe Tillet, told ZDNet via email. Triton "is for machine learning researchers and engineers who are unfamiliar with GPU programming despite having good software engineering skills," said Tillet.


OpenAI releases Triton, a programming language for AI workload optimization

#artificialintelligence

All the sessions from Transform 2021 are available on-demand now. OpenAI today released Triton, an open source, Python-like programming language that enables researchers to write highly efficient GPU code for AI workloads. Triton makes it possible to reach peak hardware performance with relatively little effort, OpenAI claims, producing code on par with what an expert could achieve in as few as 25 lines. Deep neural networks have emerged as an important type of AI model, capable of achieving state-of-the-art performance across natural language processing, computer vision, and other domains. The strength of these models lies in their hierarchical structure, which generates a large amount of highly parallelizable work well-suited for multicore hardware like GPUs.


Deploy fast and scalable AI with NVIDIA Triton Inference Server in Amazon SageMaker

#artificialintelligence

Machine learning (ML) and deep learning (DL) are becoming effective tools for solving diverse computing problems, from image classification in medical diagnosis, conversational AI in chatbots, to recommender systems in ecommerce. However, ML models that have specific latency or high throughput requirements can become prohibitively expensive to run at scale on generic computing infrastructure. To achieve performance and deliver inference at the lowest cost, ML models require inference accelerators such as GPUs to meet the stringent throughput, scale, and latency requirements businesses and customers expect. The deployment of trained models and accompanying code in the data center, public cloud, or at the edge is called inference serving. We are proud to announce the integration of NVIDIA Triton Inference Server in Amazon SageMaker.


NVIDIA doubles down on AI language models and inference as a substrate for the Metaverse, in data centers, the cloud and at the edge

ZDNet

GTC, NVIDIA's flagship event, is always a source of announcements around all things AI. The fall 2021 edition is no exception. Omniverse is NVIDIA's virtual world simulation and collaboration platform for 3D workflows, bringing its technologies together. Based on what we've seen, we would describe the Omniverse as NVIDIA's take on Metaverse. You will be able to read more about the Omniverse in Stephanie Condon and Larry Dignan's coverage here on ZDNet.


Nvidia doubles down on AI language models and inference as a substrate for the Metaverse, in data centers, the cloud and at the edge

#artificialintelligence

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them. GTC, Nvidia's flagship event, is always a source of announcements around all things AI. The fall 2021 edition is no exception. Omniverse is Nvidia's virtual world simulation and collaboration platform for 3D workflows, bringing its technologies together.