Google announces Cloud TPU virtual machines for AI workloads

Google Cloud has introduced the final availability of TPU digital machines (VMs) for synthetic intelligence workloads.

The overall availability launch features a new TPU embedding API, which Google Cloud claims can speed up large-scale, ML-based rating and advice workloads

Google Cloud mentioned embedding acceleration with Cloud TPU might help companies decrease value related to rating and advice use-cases which generally depend on deeply neural network-based algorithms that may be pricey to run.

“They have a tendency to make use of massive quantities of knowledge and may be tough and costly to coach and deploy with conventional ML infrastructure,” Google Cloud mentioned in a blog post.

“Embedding acceleration with Cloud TPU can resolve this drawback at a decrease value. Embedding APIs can effectively deal with massive quantities of knowledge, reminiscent of embedding tables, by routinely sharding throughout lots of of Cloud TPU chips in a pod, all related to at least one one other by way of the custom-built interconnect.”

On the identical time, the TPU VMs have been designed to help three main frameworks — TensorFlow, PyTorch, and JAX — which are supplied by means of three environments for ease of setup with the respective framework.

Google Cloud added that the TPU VMs allow enter knowledge pipelines to be executed instantly on the TPU hosts. By way of this functionality, customers can construct their very own buyer ops, reminiscent of TensorFlow Textual content so they’re now not certain to TensorFlow runtime launch model.

Native execution on the host with the accelerator additionally permits use circumstances reminiscent of distributed reinforcement studying.

“With Cloud TPU VMs you possibly can work interactively on the identical hosts the place the bodily TPU {hardware} is connected,” Google Cloud mentioned.

“Our quickly rising TPU consumer neighborhood has enthusiastically adopted this entry mechanism, as a result of it not solely makes it doable to have a greater debugging expertise, but it surely additionally permits sure coaching setups reminiscent of distributed reinforcement studying which weren’t possible with TPU Node (networks accessed) structure.”

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