Files
foxhunt/docs/troubleshooting/gpu-errors.md
jgrusewski 7458f1be01 feat(wave12): E2E validation complete - 225-feature pipeline ready
 Validation Results:
- PPO training: 24.2s (1 epoch, 950 samples, dim=225)
- Feature extraction: 105μs/bar (9.5x faster than target)
- Model checkpoint: 293KB (147KB actor + 146KB critic)
- GPU memory: 145MB used (96.4% headroom)
- Zero dimension mismatches

📊 Success Criteria (5/5):
 Feature dimension = 225 (Wave C 201 + Wave D 24)
 Model state_dim = 225
 Training completed without errors
 Checkpoint saved successfully
 No dimension mismatch errors

📁 Training Data Ready:
- ES.FUT: 2.9MB, 180 days
- NQ.FUT: 4.4MB, 180 days
- 6E.FUT: 2.8MB, 180 days
- ZN.FUT: 65KB, 90 days (clean)

🚀 Next: Full production model retraining (4 models, ~10min GPU time)

🤖 Generated with Claude Code (https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-22 22:48:04 +02:00

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1.2 KiB
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# GPU Errors Troubleshooting Guide
**Last Updated**: 2025-10-22
---
## GPU Not Detected
### Symptom: `nvidia-smi: command not found` in container
**Diagnosis**:
```bash
# Check GPU availability on node
kubectl get nodes -o json | jq '.items[].status.allocatable | select(.["nvidia.com/gpu"] != null)'
# Check NVIDIA device plugin
kubectl get pods -n kube-system | grep nvidia-device-plugin
```
**Resolution**:
```bash
# Install NVIDIA GPU Operator
helm install gpu-operator nvidia/gpu-operator \
--namespace gpu-operator-resources \
--create-namespace
# Verify GPU detected
kubectl describe node gpu-node-1 | grep nvidia.com/gpu
```
---
## CUDA Out of Memory
### Symptom: `CUDA error: out of memory`
**Diagnosis**:
```bash
# Check GPU memory usage
kubectl exec -n foxhunt foxhunt-ml-training-service-xxxxx -- nvidia-smi
# Output:
# GPU Memory-Usage
# 0 3800MiB / 4096MiB (93% - CRITICAL)
```
**Resolution**:
```python
# Reduce batch size
batch_size = 16 # Reduced from 32
# Enable gradient checkpointing
model.gradient_checkpointing_enable()
# Use mixed precision training
from torch.cuda.amp import autocast, GradScaler
scaler = GradScaler()
with autocast():
output = model(input)
```
---
**End of GPU Errors Troubleshooting Guide**