NanoGPT Training Command Explanation | Generated by AI
Yes — that command pattern is exactly right, and compile=False is the correct call here. Use this:
PYTORCH_ALLOC_CONF=expandable_segments:True python3.11 -u train.py config/train_gpt2_200m.py --compile=False 2>&1 | tee -a out-gpt2-200m/train.log
Why each piece:
PYTORCH_ALLOC_CONF=expandable_segments:True— reduces VRAM fragmentation. Useful here because we have many similar-sized allocations across 128 grad-accum micro-steps; can recover ~5–10% effective headroom.python3.11 -u— unbuffered stdout so theteelog is live, not chunked.--compile=False— nanoGPT’sconfigurator.pyaccepts CLI overrides, so this flips the config flag without editing the file. Triton/inductor can’t link against-lcudain your env; not worth debugging mid-run.tee -a out-gpt2-200m/train.log—train.pycreatesout_diron startup, so this works withoutmkdir.-aappends so resumes don’t clobber prior log.
Time impact of dropping compile: the no-compile estimate from the VRAM probe was ~21.4 s/iter → ~48 hours (~2 days) for the full 8,000-iter / 4.19B-token run. So the cost of compile=False versus a working compile is roughly +12 hours of wall-clock. Worth it — a working run beats a 25% speedup you can’t reach.
Verification before the long run: I’d recommend a quick 25-iter dry-run with the production config to confirm timing matches the projection:
PYTORCH_ALLOC_CONF=expandable_segments:True python3.11 -u train.py config/train_gpt2_200m.py --compile=False --max_iters=25 --eval_interval=1000 --log_interval=1 --out_dir=out-gpt2-200m-dryrun
That’ll show you actual ms/iter at bs=4, grad_accum=128 (should be ~20–22 s/iter). If it lands in that range, fire the real run.
If you want me to chase the gcc/Triton issue later for the 25% speedup, I can — but it can wait until after you have a trained model.