Orchestrating GPU workloads on Runpod with dstack

Orchestration in machine learning (ML) teams involves automating the provisioning and management of computing resources to reduce costs and improve efficiency. dstack is a lightweight, open-source alternative to traditional orchestration tools like Kubernetes and Slurm, designed with a GPU-native focus and integration with modern cloud providers, including Runpod. It simplifies day-to-day operations by providing interactive development environments, task scheduling, and persistent service endpoints, all controlled through a declarative YAML configuration. By optimizing resource utilization and implementing policies like auto-shutdown and utilization-based termination, dstack helps ML teams avoid overpaying for GPU usage, as demonstrated by Electronic Arts, which reported significant cost savings. The platform's support for multi-cloud and hybrid environments allows for flexible job routing to cost-effective backends, making it a comprehensive solution for managing the entire ML lifecycle from development through training to inference.