Error prevention programs
Preventive measures for typical problems:
- Gradient anomaly detection::
- exist
trainer.pyset up ingradient_norm_threshold: 1.0 - Enable autoscaling:
--auto-scale-lr - control
gradient_health_check.loglog file
- exist
- hardware compatibility::
- (of a computer) run
./scripts/hardware_check.shVerification Environment - Avoid mixing GPUs of different architectures
- NVLink connectivity prioritized over PCIe
- (of a computer) run
- Hyperparameter validation::
- utilization
validate_config.pyChecking the rationality of parameters - Key parameter alert values:
- Learning rate > 0.001 triggers a warning
- batch_size exceeds VRAM80% auto-adjustment
- utilization
Failure recovery mechanisms
Built-in protection:
- Auto-save checkpoints every 1000steps
- Abnormal interruptions can be followed by
--resume-fromresumption - Automatic activation of gradient checkpointing on memory overflow
This answer comes from the articleOpen-Reasoner-Zero: Open Source Large-Scale Reasoning Reinforcement Learning Training PlatformThe
May not be reproduced without permission:AI productivity tools " How to avoid common misconfigurations in reinforcement learning training?
