Crash-Consistent Checkpointing for AI Training on macOS/APFS

Deep learning training relies on periodic checkpoints to recover from failures, but unsafe checkpoint installation can leave corrupted files on disk. This paper presents an experimental study of checkpoint installation protocols and integrity validation for AI training on macOS/APFS. We implement three write modes with increasing durability guarantees: unsafe (baseline, no fsync), atomic_nodirsync (file-level durability via fsync()), and atomic_dirsync (file + directory durability). We design a format-agnostic integrity guard using SHA-256 checksums with automatic rollback. Through controlled experiments including crash injection (430 unsafe-mode trials) and corruption injection (1,600 atomic-mode trials), we demonstrate that the integrity guard detects 99.8-100% of corruptions with zero false positives. Performance overhead is 56.5-108.4% for atomic_nodirsync and 84.2-570.6% for atomic_dirsync relative to the unsafe baseline. Our findings quantify the reliability-performance trade-offs and provide deployment guidance for production AI infrastructure.