Cloud Pathways, a distributed ML runtime, is GA

Proven enterprise impact and customer momentum

Frontier AI labs, quantitative research firms, and global consumer platforms have adopted Cloud Pathways to run mission-critical production workloads on GKE, including:

• Hyperscale multi-host inference: One of the world’s largest consumer technology companies runs production batch and reinforcement learning (RL) inference on Cloud Pathways across tens of thousands of TPU v5p, Trillium (v6e), and Ironwood (TPU7x) chips, consuming over 100 million chip-hours per quarter, and co-designed elasticity primitives with Google to expand into pre-training.

• Elastic pre-training and multi-host RL for scientific foundation models: Google Cloud customer ZenteiQ uses Cloud Pathways on GKE with Cluster Toolkit (gcluster), colocated Python sidecars, and host snapshotting to scale elastic pre-training (MaxText) and single-controller reinforcement learning (MaxText + Tunix) across multi-slice Trillium (v6e) clusters for its scientific foundation models.

“Running multi-day pre-training and post-training jobs on high-cost accelerators requires reliable continuity through both unexpected hardware faults and planned maintenance windows. By running Cloud TPUs and MaxText with Cloud Pathways on the Google Cloud AI Hypercomputer stack, ZenteiQ relies on a proven controller that monitors our TPU topology and dynamically re-meshes and resizes the workload whenever a chip or slice goes offline. Combining Pathways with host snapshotting in production pushes recovery time down to a minimum when disruptions occur, keeping our available TPU capacity fully utilized with very little stall time. Our ML engineers no longer have to spend their hours watching job health or managing manual restarts, and can stay focused on their research workloads.” – Prof. Sashikumaar Ganesan, Founder, ZenteiQ

Getting started with Cloud Pathways

Cloud Pathways is generally available today in all Google Cloud regions supporting Cloud TPU v5e, TPU v5p, Trillium (TPU v6e), and Ironwood (TPU 7x) on GKE, with versioned container images (us-docker.pkg.dev/cloud-tpu-v2-images/cloud-pathways) supporting JAX 0.10.1+. Cloud Pathways is also available in Preview on Slurm TPU clusters in Cluster Director.

To start building with Cloud Pathways:

1. Read the overview: Explore the Introduction to Pathways on Cloud in the AI Hypercomputer documentation.

2. Deploy your cluster: 

1. GKE: Follow the step-by-step guide to Create a GKE cluster with Pathways using Cluster Toolkit (gcluster) or the gcloud CLI.

2. Slurm: Deploy Pathways via Cluster Director using the step-by-step guide.