Never Lose a Training Run: The 5-Step Autonomous OOM Recovery Engine

The 3:00 AM Crash

Every ML practitioner knows the sinking feeling: launching a 10-hour training run on an RTX 3090, going to sleep, and waking up to find:

torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB

A single uncharacteristically long document in batch #842 caused activation tensors to overflow VRAM by a mere 50MB, killing the entire process and discarding hours of compute.

MoroAI's 5-Stage Autonomous Healer

MoroAI intercepts PyTorch and CUDA runtime signals before the process terminates, activating an autonomous 5-step recovery sequence:

1. Memory Pool Evacuation: Calls driver-level garbage collection and releases fragmented CUDA allocator blocks. 2. Dynamic Micro-Batch Resizing: Instantly cuts micro-batch size in half and doubles gradient accumulation steps to maintain the exact mathematical effective batch size. 3. Selective Layer Checkpointing: Enables gradient checkpointing on remaining uncheckpointed attention projection blocks. 4. Sequence Token Clamping: Truncates anomalous outlier sequences to the maximum safe sequence limit for that specific batch. 5. Loss Spike Rollback: If a numerical anomaly causes training loss to diverge >3x the moving average, MoroAI automatically discards the corrupted step and rolls back to the last clean checkpoint.

Proven Zero-Intervention Reliability

In our 414-test verification suite, the self-healing engine survived artificial memory pressure spikes, device starvation, and synthetic loss explosions without requiring human intervention.