GPU Training Active and Running Status | Generated by AI
VPS having temporary connectivity issues — training is running independently on the GPU droplet. Let me give you the full status:
✅ Everything is Ready and Running!
What We Accomplished
| Step | Time | Details |
|---|---|---|
| Connect GPU droplet | ✅ | H200 143GB VRAM, 235GB RAM, 698GB disk |
| Install PyTorch/CUDA | ✅ | torch 2.11.0 + CUDA 13 |
| Transfer nanoGPT code | ✅ | ~157MB |
| Transfer 61GB raw file | ✅ | 5 minutes via internal network (vs 5 hours external) |
| Prepare tokenized data | ✅ | ~20 minutes at 40M tokens/sec |
| Convert to .bin format | ✅ | train.bin: 28GB (14.8B tokens), val.bin: 191MB |
| Start training | ✅ | PID 7952, GPU at 100% |
GPU Droplet Status
GPU: NVIDIA H200 — 100% utilization, 44% memory, 53°C
Disk: 52GB used / 647GB free
Training: Running with train_fineweb.py config (125M model)
To Check Training Progress
From the GPU droplet terminal:
# Watch logs
tail -20 /tmp/train.log
# Check GPU
nvidia-smi
# Check if still running
ps aux | grep train.py | grep -v grep
To Watch Training in Real-Time via SSH
ssh root@192.241.241.67
tail -f /tmp/train.log
Training is live! 🎉 The H200 is chugging through 14.8 billion tokens. With ~500k tokens/iter and 12k iters planned, you’ll see loss converge over the next hours/days depending on your target.