OpenAI has introduced Triton, an open-source Python-like GPU programming language, needed to create neural networks.
The company claims Triton will help to achieve maximum equipment performance without much effort. Developers who have no experience with CUDA will get an opportunity to produce highly efficient code on GPU.
The novel programming language is compatible with Python and allows researchers to perform a task within 25 lines, although it typically requires way more effort.
Triton facilitates development of custom kernels, which have the potential to operate much faster than their counterparts in shared libraries. The compiler automatically optimizes and parallelizes the code, making it suitable to run on the latest Nvidia GPUs.
“Our researchers have already used [Triton] to produce kernels that are up to 2x more efficient than equivalent Torch implementations, and we’re excited to work with the community to make GPU programming more accessible to everyone,” stated Triton developer Philippe Tillet.
The first version of the programming language is available on GitHub. Triton currently runs on Nvidia hardware solely.
Triton was first presented in 2019 during the International Workshop on Machine Learning and Programming Languages. Soon its creator, Philippe Tillet, moved to work at OpenAI.
In July, OpenAI disbanded the robotics team and ceased trying to apply AI to physical machines.
In June, the company experts found a way to improve AI language model behaviour and reduce harmful content.
In March, OpenAI representatives said GPT-3 spewed out 4.5 billion words daily.
The future is now. That is definitely proven by OpenAI, an AI research and deployment company, which has succeeded in developing its own programming language named Triton. It appears far more appealing for coders and specialists, marking a whole new era in a long path towards getting along with AI.
The views, thoughts, and opinions expressed in this article belong solely to the author, and should not be taken as investment advice. Do your own research before taking any investment decisions.
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