7458f1be01
✅ 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>
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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:
# 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:
# 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:
# 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:
# 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