Segment 50
This sub-session focused on preparing kpro6 for production DFlash training and iterating on the pipeline to balance throughput, power draw, and training correctness. A new LXC container (CT 200) was spun up with Ubuntu 24.04, 8× RTX PRO 6000 GPUs, and a complete Python environment. After fixing Triton compilation issues and OOM errors caused by logits computation on target and drafter GPUs (by skipping lm_head on targets and computing verifier logits only at anchor positions), the first stable training run was launched with a 7-1 GPU topology, achieving ~27 Ktok/s. To reduce rack power draw, the topology was switched to 6-1, saving ~1 kW while maintaining a balanced pipeline at ~30 Ktok/s and an ETA of ~4.2 days. The user identified a critical flaw in the training data pipeline: the build_batches function sorted all samples by length and created fixed batch assignments, meaning the optimizer always saw short samples together and long samples together, potentially causing gradient oscillation and poor convergence. A full random shuffle was tested but destroyed padding efficiency, dropping throughput to ~12 Ktok/s. The assistant and user then collaborated on a hybrid 'bucketed shuffle' strategy, running an analytical optimization against the actual sequence length distribution to determine six optimal bucket boundaries that minimized padding waste while ensuring diverse batch compositions each epoch. The training run was stopped to implement this new batching strategy. The assistant deployed the analytically optimized bucket boundaries, restarted the run from scratch, and achieved a steady-state throughput of 25.1 Ktok/s with a 5.1-day ETA, successfully recovering from the disastrous 12 Ktok/s of the full random shuffle. The pipeline is now perfectly balanced and producing diverse batches each epoch, with a jumpy loss curve diagnosed as a healthy indicator of the new strategy working correctly. The run is confirmed to be on a solid trajectory for robust convergence.
The Bucketed Shuffle: How Analytical Optimization Rescued a Multi-GPU DFlash Training Run