The 32-GPU Hang
We were trying to fine-tune a 30B parameter model across 4 nodes (32 GPUs total) using PyTorch's Fully Sharded Data Parallel (FSDP) wrapped via Hugging Face Accelerate.
For two agonizing days, the training loop would just hang at exactly step 150. There were no error logs. The GPUs just dropped to 0% utilization, and the SSH sessions sat in silence.
Debugging the NCCL Backend
When distributed training hangs without an error, it is almost always a networking issue blocking a collective operation (like an all_gather). We had to set NCCL_DEBUG=INFO and TORCH_DISTRIBUTED_DEBUG=DETAIL to finally track it down.
# Launching with deep NCCL debugging enabled
export NCCL_DEBUG=INFO
export NCCL_IB_DISABLE=0 # Ensure InfiniBand is actually being used
export TORCH_DISTRIBUTED_DEBUG=DETAIL
accelerate launch --num_machines 4 --num_processes 32 train.pyTurns out, it was a faulty InfiniBand configuration on Node 3 causing packets to drop, which eventually led to an NCCL timeout during gradient synchronization. Hugging Face makes multi-node code incredibly easy to write, but you still need to be a hardcore Linux sysadmin to debug the hardware layer.