Compile Failure in PyTorch Triton | Generated by AI
That’s torch.compile failing — Triton tried to invoke gcc to build a CUDA helper module and gcc returned non-zero. The smoke run never hit this because we forced --compile=False.
Fastest fix: just disable compile. You lose 20–30% throughput (so ~59h instead of ~42h for 19073 iters), but training is otherwise identical and the model quality is the same.
cd /mnt/data/nanoGPT
tmux new -s nano # or: tmux attach -t nano
source venv/bin/activate
PYTORCH_ALLOC_CONF=expandable_segments:True \
python -u train.py config/train_fineweb_gpt3.py --compile=False 2>&1 \
| tee -a out-fineweb-gpt3/train.log
One thing that looks off
The traceback paths are /home/lzw/.local/lib/python3.11/... — that’s system Python 3.11, not the nanoGPT venv (which I used earlier was Python 3.14 at /mnt/data/nanoGPT/venv/bin/python). Did you forget source venv/bin/activate in your tmux session? That’s not what’s causing this error (the gcc failure would happen in either env), but you’ll want consistency for the long run.
Check:
which python && python -c "import sys; print(sys.executable, sys.version)"
Optional: actually fix compile (if you want the speedup)
The gcc compile fails — to see why, run gcc with the same args manually from the traceback. Common causes on Ubuntu:
# Quick checks
gcc --version
ls /usr/local/cuda/lib64/libcuda.so* /usr/lib/x86_64-linux-gnu/libcuda.so*
ls /usr/local/cuda/include/cuda.h
echo $CUDA_HOME
Typical fixes:
sudo apt install nvidia-cuda-toolkit(gets the headers Triton needs)export CUDA_HOME=/usr/local/cudaandexport PATH=$CUDA_HOME/bin:$PATHbefore running- Or: re-run the smoke command — sometimes Triton’s stale
~/.triton/cachecauses phantom failures;rm -rf ~/.triton/cacheclears it
My recommendation: start the long run with --compile=False now (you already lost time on the failed start). Diagnose compile in parallel during the first day — if you get it working, kill, resume with --init_from=resume and compile=True. No data wasted.