Google Cloud wants to make it a lot easier to run massive ML workloads

Google Cloud has announced the general availability of its TPU virtual machines.

Tensor Processing Units (TPUs) are application-specific integrated circuits (ASICs) developed by Google that are used to accelerate machine learning workloads.

Cloud TPU lets you run your machine learning workloads on the cloud hosting giant’s TPU acceleration hardware using the open-source TensorFlow machine learning platform.

What can TPU VMs do for users?

Google says its user community has embraced virtual TPUs because they provide a better debugging experience and also enable certain training configurations, including distributed reinforcement learning, which it says were not feasible with Google. existing UPT (network access) node architecture.

Cloud TPUs are optimized for large-scale ranking and recommendation workloads according to Google, citing how Snap was an early adopter of the capability.

Additionally, with the GA release of TPU VMs, Google is introducing a new TPU integration API, which it claims can speed up ML-based ranking and recommendation workloads.

Google highlighted how many modern businesses rely on ranking and recommendation use cases, such as audio and video recommendations, product recommendations, and ad ranking.

The tech giant said TPUs can help companies implement a deep neural network-based approach to tackle the above use cases, which it says can be costly and resource-intensive. data to be formed.

Google also claims that its TPU VMs offer several additional features over the existing TPU node architecture due to their local runtime configuration, as the input data pipeline can run directly on TPU hosts, which allows organizations to save IT resources.

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TPU VM GA Release also supports other major ML frameworks such as PyTorch and JAX.

Interested in deploying a virtual TPU? You can follow one of Google’s quickstarts or tutorials.

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