GPUs are now available for Google Compute Engine and Cloud Machine
February 23, 2017
Google Cloud Platform
gets a performance boost today with the much anticipated public beta of
NVIDIA Tesla K80
You can now spin up NVIDIA GPU-based VMs in three GCP regions: us-east1,
asia-east1 and europe-west1, using the
command-line tool. Support for creating GPU VMs using the Cloud Console
appears next week.
Rather than constructing a GPU cluster in your own datacenter, just add GPUs to virtual machines running in our cloud. GPUs on Google Compute Engine are attached directly to the VM, providing bare-metal performance. Each NVIDIA GPU in a K80 has 2,496 stream processors with 12 GB of GDDR5 memory. You can shape your instances for optimal performance by flexibly attaching 1, 2, 4 or 8 NVIDIA GPUs to custom machine shapes.
These instances support popular machine learning and deep learning frameworks such as TensorFlow, Theano, Torch, MXNet and Caffe, as well as NVIDIA’s popular CUDA software for building GPU-accelerated applications.
PricingLike the rest of our infrastructure, the GPUs are priced competitively and are billed per minute (10 minute minimum). In the US, each K80 GPU attached to a VM is priced at $0.700 per hour per GPU and in Asia and Europe, $0.770 per hour per GPU. As always, you only pay for what you use. This frees you up to spin up a large cluster of GPU machines for rapid deep learning and machine learning training with zero capital investment.
Supercharge machine learning
The new Google Cloud GPUs are tightly
Google Cloud Machine Learning
(Cloud ML), helping you slash the time it takes to train machine
learning models at scale using the
framework. Now, instead of taking several days to train an image
classifier on a large image dataset on a single machine, you can run
distributed training with multiple GPU workers on Cloud ML, dramatically
shorten your development cycle and iterate quickly on the model.
Next stepsRegister for Cloud NEXT, sign up for the CloudML Bootcamp and learn how to Supercharge performance using GPUs in the cloud. You can use the gcloud command-line to create a VM today and start experimenting with TensorFlow-accelerated machine learning. Detailed documentation is available on our website.