Fortune.com reports that Facebook and Google are combining their efforts in order to make Facebook’s open source machine learning PyTorch framework integrate with Google’s Tensor Processing Unit (TPU), a custom computer chip used for machine learning.
The development of the TPU integration marks an interesting occasion in which two tech competitors have chosen to work with each other rather than against.
PyTorch is a deep learning framework designed for easy and flexible experimentation and Facebook on Tuesday announced the preview release of an updated version of the framework – PyTorch 1.0.
Google Cloud director of product management Rajen Sheth wrote in a blog post: “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,” Sheth said on the blog.
“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,” he added.
The new partnership is aimed at making PyTorch framework work with Google’s custom computer chips for ML, dubbed Tensor Processing Units, or TPU, Fortune.com reported.
“In conjunction with today’s release of PyTorch 1.0 Preview, we are broadening support for PyTorch throughout Google Cloud’s AI platforms and services,” Rajen Sheth, Director of Product Management, Google Cloud wrote in a blog post on Tuesday.
ML developers use many different tools, and Google has integrated several of the most popular open source frameworks into its products and services, including TensorFlow, PyTorch, scikit-learn, and XGBoost.
PyTorch 1.0 accelerates the workflow involved in taking breakthrough research in AI to production deployment, Facebook said.
Information Services Group principal analyst Blair Hanley Frank commented on the announcement that the two tech firms would be working on A.I. technology together stating: “Data scientists and machine learning engineers have a wide variety of open source tools to choose from today when it comes to developing intelligent systems. This announcement is a critical step to help ensure more people have access to the best hardware and software capabilities to create AI models.”
Frank stated that he expects to see “more collaboration like this to crop up in the AI market.” He continued to note that: “Expanding framework support can help cloud providers like AWS, Google and Microsoft drive additional usage of their platforms. That means it makes sense for them to support as broad a set of development tools as possible, to try and attract as many customers as they can.”
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