What customers are sayingĮarly access customers have been using Cloud TPU VMs since last October, and several teams of researchers and engineers have used them intensively since then. Furthermore, you may also see significant cost savings: If you previously needed a fleet of powerful Compute Engine VMs to feed data to remote hosts in a Cloud TPU Pod slice, you can now run that data processing directly on the Cloud TPU hosts and eliminate the need for the additional Compute Engine VMs.
Run 8 train simulator v3 teaser code#
In addition to major usability benefits, you may also achieve performance gains because your code no longer needs to make round trips across the datacenter network to reach the TPUs.
This new Cloud TPU system architecture is simpler and more flexible. You would typically create one or more VMs that would then communicate with Cloud TPU host machines over the network using gRPC: Until now, you could only access Cloud TPUs remotely. “Direct access to TPU VMs has completely changed what we're capable of building on TPUs and has dramatically improved the developer experience and model performance."- Aidan Gomez, co-founder and CEO, Cohere A closer look at the new Cloud TPU architecture In addition to Cloud TPU integrations with TensorFlow, PyTorch, and JAX, and you can even write your own integrations via a new libtpu shared library on the VM. You can use local storage, execute custom code in your input pipelines, and more easily integrate Cloud TPUs into your research and production workflows.
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You have root access to every TPU VM you create, so you can install and run any code you wish in a tight loop with your TPU accelerators. Now you can write and debug an ML model line-by-line using a single TPU VM, then scale it up on a Cloud TPU Pod slice to take advantage of the super-fast TPU interconnect.
Instead of accessing Cloud TPUs remotely over the network, Cloud TPU VMs let you set up your own interactive development environment on each TPU host machine. Today, we’re excited to announce new Cloud TPU VMs, which make it easier than ever before to use our industry-leading TPU hardware by providing direct access to TPU host machines, offering a new and improved user experience to develop and deploy TensorFlow, PyTorch, and JAX on Cloud TPUs.
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