Distributed Training Optimization Scheme
Verifiers combinedvLLM+FSDPof a two-tier parallel strategy to maximize resource utilization:
- data parallelism::
GRPOTrainerMulti-GPU inference is supported by default through the--data-parallel-sizeParameter Configuration - model parallelism:: In conjunction with the
prime-rlIntegration enables FSDP full slice mode to support training with hundreds of billions of parameters - Flow line optimization: Use
flash-attnAccelerated Attention Calculator, recommended to add during installation--no-build-isolation
Recommended Configuration:
- 7 GPUs running
vf-vllmService handles inference requests - Running the training process on a separate GPU (Zero Stage 3 configuration)
- set up
NCCL_P2P_DISABLE=1Avoiding communication blocking - Monitoring tools show that each GPU utilization should remain above 85%
For nodes with more than 8 cards, it is recommended to usetorchrunInitiate multi-node training.
This answer comes from the articleVerifiers: a library of reinforcement learning environment tools for training large language modelsThe
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