Start by clarifying requirements and constraints, then outline a high-level architecture covering data/model parallelism, communication, and fault tolerance. Dive into trade-offs and optimizations, and conclude with monitoring and scaling considerations.
Pro tip: Emphasize the importance of overlapping communication with computation and using NVIDIA-specific technologies like NCCL and NVLink to achieve near-linear scaling.
Ask about model size, dataset size, hardware (e.g., GPUs, interconnects), and scaling goals to tailor the design.
Decide between data parallelism, model parallelism, or hybrid based on model architecture and memory constraints.
Select collective communication libraries (e.g., NCCL) and synchronization methods (e.g., all-reduce, parameter servers) to minimize overhead.
Implement mechanisms to handle node failures, such as periodic checkpointing and elastic training.
Profile performance, tune hyperparameters, and set up monitoring for throughput, latency, and resource utilization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second and just started listing checkpointing strategies.
Start by clarifying the system's scale, consistency requirements, and failure model, then describe a layered approach: detection, isolation, recovery, and prevention. Emphasize trade-offs between availability, consistency, and cost, and tie your answer to NVIDIA's context of GPU-accelerated distributed systems.
Pro tip: Mention that failures are inevitable and the goal is to minimize blast radius and recovery time, not to prevent all failures. Also, highlight the importance of observability and automated remediation to reduce human error.
Ask about scale, consistency needs, latency SLAs, and what constitutes a node failure (crash, slowdown, network partition). This shows you avoid assumptions and tailor solutions.
Explain how to detect failures quickly using heartbeats, health checks, and distributed tracing. Mention tools like Prometheus, Grafana, or custom telemetry.
Describe mechanisms to prevent cascading failures: circuit breakers, bulkheads, and graceful degradation. Emphasize limiting the blast radius.
Cover strategies like replication, checkpointing, and automatic failover. Discuss trade-offs between active-active vs. active-passive and the role of orchestration (e.g., Kubernetes).
Talk about chaos engineering, post-mortems, and capacity planning to reduce future failures. Highlight learning from incidents.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.