Hardware analysis:
4x RTX 5090 32GB without NVLink is fully sufficient.
PCIe 5.0 all-reduce overhead <1% of step time for MacBERT-large (340M params).
BF16 mixed precision gives ~2x throughput vs FP32 on 5090.
Module B (Detector) — full 4-GPU DDP via Accelerate:
- DistributedSampler with per-epoch shuffling (correct DDP data split)
- BF16 autocast via accelerator.mixed_precision
- Gradient accumulation handled by accelerator.accumulate()
- Only rank-0 saves checkpoints and logs to wandb
- accelerator.gather_for_metrics() for correct multi-GPU validation
- per_gpu_batch_size=32, effective_batch = 32×4 = 128
Module C (Intervention) — hybrid parallel strategy:
- Stage 1 (BC warm-up): all 4 GPUs via Accelerate DDP
TensorDataset broadcast from rank-0 to all processes
- Stage 2 (PPO): GPU-0 only — env-agent loop is inherently sequential
- Detector preprocessing: distributed across all 4 GPUs via shard split
+ all_gather_object to collect results on rank-0
Configs updated:
detector_config.yaml: per_gpu_batch_size=32, gradient_accumulation_steps=1,
mixed_precision=bf16, num_workers=4
intervention_config.yaml: BC per_gpu_batch_size=256, PPO batch_size=256
Launch scripts added:
scripts/run_detector.sh — single command: 4-GPU detector training
scripts/run_intervention.sh — single command: hybrid BC+PPO training
scripts/run_full_pipeline.sh — end-to-end pipeline steps 1-5
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
