– AI/ML models require accelerated compute such as NVIDIA GPUs for training and inference applications.
– Google Kubernetes Engine (GKE) simplifies container orchestration and is the platform of choice for deploying custom ML platforms.
– GKE can now automatically install NVIDIA GPU drivers, making it easier for customers to take advantage of GPUs.
As AI/ML models grow in size, demand for accelerated compute like NVIDIA GPUs for training and inference applications increases. Google Kubernetes Engine (GKE) is a fully managed Kubernetes service that simplifies container orchestration and has become the platform of choice for deploying, scaling, and managing custom ML platforms. GKE now offers automatic installation of NVIDIA GPU drivers, making it easier for customers to utilize GPUs without the need for manual installation.
Previously, using GPUs with GKE required manual installation of GPU drivers through a daemonset, which some customers found to be an unnecessary hassle that slowed down deployment workflows. With GKE now offering automated GPU driver installation, the process of using GPUs on the platform has become more streamlined and user-friendly.
Automated driver installation has been praised by customers for simplifying the process of adding GPUs to GKE node pools. By off-loading the installation management of GPU drivers to Google, the drivers can be precompiled for the GKE node, reducing the startup time of GPU nodes.
To take advantage of automated GPU driver installation on GKE, customers can specify the DRIVER_VERSION option when creating node pools, choosing between default, latest, or disabled options for driver installation. Enabling GPU driver installation via gcloud can help customers quickly and easily set up their GKE environment for GPU usage.