By Chris Kleban, Product Manager
Not everyone needs the extra performance that GPUs bring to a compute workload, but those who do, really do. Earlier this year, we announced that you could attach GPUs to Preemptible VMs on Google Compute Engine and Google Kubernetes Engine, lowering the price of using GPUs by 50%. Today, Preemptible GPUs are generally available (GA) and we’ve lowered preemptible prices on our entire GPU portfolio to be 70% cheaper than GPUs attached to on-demand VMs.
Preemptible GPUs are ideal for customers with short-lived, fault-tolerant and batch workloads such as machine learning (ML) and high-performance computing (HPC). Customers get access to large-scale GPU infrastructure, predictable low pricing, without having to bid on capacity. GPUs attached to Preemptible VMs are the same as equivalent on-demand resources with two key differences: Compute Engine may shut them down after providing you a 30-second warning, and you can use them for a maximum of 24 hours. Any GPUs attached to a Preemptible VM instance will be considered Preemptible and will be billed at the lower rate.
We offer three different GPU platforms to choose from, making it easy to pick the right GPU for your workload.
Combined with custom machine types, Preemptible VMs with Preemptible GPUs let you build your compute stack with exactly the resources you need—and no more. Attaching Preemptible GPUs to custom Preemptible VMs allows you to reduce the amount of vCPU or host memory for your GPU VM, to save even further over pre-defined VM shapes. Additionally, customers can use Preemptible Local SSD for a low-cost, high-performance storage option with our Preemptible GPUs. Check out this pricing calculator to configure your own preemptible environment.
The use-case for Preemptible GPUs
Hardware-accelerated infrastructure is in high demand among innovators, researchers, and academics doing machine learning research, particularly when coupled with the low, predictable pricing of Preemptible GPUs.
“Preemptible GPUs have been instrumental in enabling our research group to process large video collections at scale using our Scanner open-source platform. The predictable low cost makes it feasible for a single grad student to repeatedly deploy hundreds of GPUs in ML-based analyses of 100,000 hours of TV news video. This price drop enables us to perform twice the amount of processing with the same budget.”
– Kayvon Fatahalian, Assistant Professor, Stanford University
Machine Learning Training and Preemptible GPUs
Training ML workloads is a great fit for Preemptible VMs with GPUs. Kubernetes Engine and Compute Engine’s managed instance groups allow you to create dynamically scalable clusters of Preemptible VMs with GPUs for your large compute jobs. To help deal with Preemptible VM terminations, Tensorflow’s checkpointing feature can be used to save and restore work progress. An example and walk-through is provided here.
To get started with Preemptible GPUs in Google Compute Engine, simply append –preemptible to your instance create command in gcloud, specify scheduling.preemptible to true in the REST API or set Preemptibility to “On” in the Google Cloud Platform Console, and then attach a GPU as usual. You can use your regular GPU quota to launch Preemptible GPUs or, alternatively, you can request a special Preemptible GPUs quota that only applies to GPUs attached to Preemptible VMs. Check out our documentation to learn more. To learn how to use Preemptible GPUs with Google Kubernetes Engine, head over to our Kubernetes Engine GPU documentation.
For a certain class of workloads, Google Cloud GPUs provide exceptional compute performance. Now, with new low Preemptible GPU pricing, we invite you to see for yourself how easy it is to get the performance you need, at the low, predictable price that you want.