The two companies said Tuesday that an unspecified number of engineers are collaborating to make Facebook’s open source machine learning PyTorch framework work with Google’s custom computer chips for machine learning, dubbed Tensor Processing Units, or TPU. The collaboration marks one of the rare instances of the technology rivals working together on joint tech projects.
“Today, we’re pleased to announce that engineers on Google’s TPU team are actively collaborating with core PyTorch developers to connect PyTorch to Cloud TPUs,” Google Cloud director of product management Rajen Sheth wrote in a blog post. “The long-term goal is to enable everyone to enjoy the simplicity and flexibility of PyTorch while benefiting from the performance, scalability, and cost-efficiency of Cloud TPUs.”
Facebook product manager for artificial intelligence Joseph Spisak said in a separate blog post that “Engineers on Google’s Cloud TPU team are in active collaboration with our PyTorch team to enable support for PyTorch 1.0 models on this custom hardware.”
Google first debuted its TPUs in 2016 during its annual developer conference, and pitched them as a more efficient way for companies and researchers to power their machine-learning software projects. The search giant sells access to its TPUs via its cloud computing business instead of selling the chips individually to customers like Nvidia, whose graphics processing units, or GPUs, are popular with researchers working on deep learning projects.
Artificial intelligence technologies like deep learning have grown in popularity over the years with tech giants like Google and Facebook that use the technologies to create software applications that can automatically do tasks like recognize images in photos.
As more businesses explore machine learning technology, companies like Google, Facebook, and others have created their own AI software frameworks, essentially coding tools, intended to make it easier for developers to create their own machine-learning powered software. These companies have also offered these AI frameworks for free in an open source model in order to popularize them with coders.