Introduction
The Ray Distributed Debugger is a powerful observability tool purpose-built for tracing, inspecting, and debugging distributed applications run on the Ray ecosystem.
Unlike traditional debugging tools that focus on local, sequential code, the Ray Distributed Debugger provides deep visibility into remote tasks, actors, object lifecycles, and inter-process communication. It plays a critical role in troubleshooting distributed model training, dynamic pipelines, and multi-agent workloads powered by Ray Serve, Ray Train, Ray Tune, and Ray Data.
Key benefits of using the Ray Distributed Debugger include:
End-to-End Execution Tracing: Captures detailed task graphs, lineage, and event timelines to help developers understand control flow across remote functions, actors, and DAGs.
Live Inspection of Distributed Tasks: Enables runtime introspection of remote function arguments, state, return values, and exceptions—critical for debugging high-parallelism jobs.
Task and Actor State Monitoring: Tracks lifecycle, status, and dependencies of every Ray object, actor, and task to quickly isolate bottlenecks, resource contention, or failures.
Scalable to Thousands of Workers: Designed to operate at the scale of modern ML infrastructure, from laptop-based testing to multi-node GPU clusters orchestrated by Kubernetes.
Tight Integration with Ray Dashboard: Fully embedded in the Ray UI, providing a seamless debugging workflow alongside logs, metrics, resource usage, and execution timelines.
The Ray Distributed Debugger is used to support teams working on distributed model training, streaming inference pipelines, reinforcement learning simulations, and large-scale experimentation with Ray Tune. It complements other observability tools like Prometheus, Grafana, and OpenTelemetry, providing low-level, code-centric insight into task behavior across nodes. By adopting the Ray Distributed Debugger, you can ensure its distributed workloads are transparent, traceable, and developer-friendly at scale—empowering engineers to move fast without losing visibility into the complex world of parallel computing.