Fault Tolerance and GPU Failures at Scale

A distributed training job depends on more than GPUs. Each worker process, host, accelerator, network link, storage path, runtime, and scheduler must continue working. As a job uses more components or runs longer, it has more opportunities to encounter a transient or persistent fault. Failures can include process crashes, node loss, hardware errors, communication timeouts, filesystem interruptions, and software exceptions. Without a recovery plan, a failure may terminate a job and discard work since its last checkpoint. Checkpoints should capture model parameters, optimizer state, scheduler state, progress counters, and random-number state when resumption requires them. Distributed checkpoints need to represent a consistent training step and be written safely, often through a designated process or a coordinated sharded format. Save them to storage that survives node loss. A restart policy can relaunch failed jobs, but it should distinguish transient infrastructure faults from deterministic code or data errors. Blind retries may repeat a failing step, loop indefinitely, or hide corrupt inputs. Set retry limits, timeouts, and alerting. Record failure reason, restart count, checkpoint age, and lost work. Test recovery with deliberate process termination in a nonproduction environment. Elastic training can adjust worker count after failures when the training framework and algorithm support it. However, changing world size can affect effective batch, learning-rate schedule, data sharding, and reproducibility. Fixed-size restarts are simpler but still require coordinated recovery across ranks. Communication libraries need all workers to reach compatible collective operations or the job may hang. Health monitoring can detect GPU memory, thermal, power, driver, PCIe, or interconnect issues. Active diagnostics may interrupt workloads and should be scheduled appropriately. A healthy hardware reading does not prove training quality; compare metrics and validate checkpoints after restart. Fault tolerance is a system property that requires checkpoints, orchestration, storage, monitoring, and tested runbooks.