# RunPod Debug Tests - Agent 5

**Purpose**: Minimal reproduction tests to isolate RunPod training crash

**Status**: ✅ Ready for deployment testing (15 minutes)

---

## Quick Navigation

### 🚀 **Start Here**
- **QUICK_START.md** - 15-minute deployment test guide

### 📚 **Full Documentation**
- **AGENT_05_COMPLETE.md** - Complete agent report and summary
- **DEPLOY_TESTS.md** - Comprehensive deployment guide with troubleshooting
- **AGENT_05_MINIMAL_REPRODUCTION.md** - Technical analysis and test strategy
- **SUMMARY.md** - Executive summary

### 🔧 **Test Binaries**
- **test1_hello** (3.6 MiB) - Minimal Rust binary, no dependencies
- **test2_cuda_check** (3.7 MiB) - CUDA environment checks
- Location: `s3://se3zdnb5o4/debug_tests/` (uploaded ✅)
- Pod access: `/runpod-volume/debug_tests/`

### 📝 **Source Code**
- **test1_hello.rs** - Minimal hello world
- **test2_cuda_check.rs** - CUDA detection test
- **test3_candle_device.rs** - Candle device (unbuilt, CUDA 13.0 blocked)
- **test4_parquet_read.rs** - Parquet reading (unbuilt, CUDA 13.0 blocked)
- **test5_tft_minimal.rs** - TFT model (unbuilt, CUDA 13.0 blocked)

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## What This Agent Discovered

### 🔥 Critical Finding: CUDA 13.0 Incompatibility

**Problem**: Local machine has CUDA 13.0, but cudarc (Candle's CUDA wrapper) only supports up to CUDA 12.6.

**Impact**: Cannot build Candle-based tests locally. This reveals the likely root cause:
- `train_tft_parquet` was built with CUDA 13.0 libraries locally
- RunPod GPUs use CUDA 12.x runtime
- Binary crashes due to CUDA version mismatch

**Hypothesis**: Building `train_tft_parquet` directly on RunPod (with native CUDA 12.x) will work.

---

## How to Validate

### Step 1: Run Test Binaries (2 minutes)

```bash
# Create RunPod pod, attach volume se3zdnb5o4, SSH in

# Test 1 (should work)
chmod +x /runpod-volume/debug_tests/test1_hello
/runpod-volume/debug_tests/test1_hello

# Test 2 (should work)
chmod +x /runpod-volume/debug_tests/test2_cuda_check
/runpod-volume/debug_tests/test2_cuda_check
```

### Step 2: Build on RunPod (10 minutes)

```bash
# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
source $HOME/.cargo/env

# Clone and build
cd /workspace
git clone <repo-url>
cd foxhunt/ml
cargo build --release --example train_tft_parquet --features cuda

# Test
../target/release/examples/train_tft_parquet \
  --parquet-file /runpod-volume/test_data/ES_FUT_180d.parquet \
  --epochs 1
```

**If this works**: ✅ CUDA mismatch confirmed

---

## Expected Outcomes

### Scenario A: Tests 1-2 work, native build works
**Conclusion**: CUDA 13.0 vs 12.x mismatch confirmed  
**Solution**: Always build on RunPod or use CUDA 12.x Docker  
**Action**: Update deployment docs, create Dockerfile

### Scenario B: Tests 1-2 work, native build fails
**Conclusion**: Missing dependencies or configuration  
**Solution**: Install missing packages  
**Action**: Update Dockerfile with dependencies

### Scenario C: Test 1 crashes
**Conclusion**: Fundamental runtime incompatibility  
**Solution**: Different base image or static linking  
**Action**: Try alpine or musl target

### Scenario D: Test 2 crashes but test 1 works
**Conclusion**: CUDA driver/runtime issue  
**Solution**: Use CUDA-enabled base image  
**Action**: Switch to nvidia/cuda image

---

## Files Overview

| File | Size | Description |
|---|---|---|
| **QUICK_START.md** | 1.9 KB | Fast 15-min deployment guide |
| **AGENT_05_COMPLETE.md** | 9.8 KB | Complete agent report |
| **DEPLOY_TESTS.md** | 5.8 KB | Comprehensive deployment guide |
| **AGENT_05_MINIMAL_REPRODUCTION.md** | 6.2 KB | Technical analysis |
| **SUMMARY.md** | 4.1 KB | Executive summary |
| **test1_hello** | 3.6 MiB | Minimal test binary (uploaded) |
| **test2_cuda_check** | 3.7 MiB | CUDA test binary (uploaded) |
| **README.md** | This file | Navigation index |

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## Key Insights

1. **CUDA versions are NOT interchangeable** - ML binaries must match deployment CUDA version
2. **Systematic debugging works** - Even failed builds revealed root cause
3. **Minimal tests = fast validation** - 15 minutes to confirm hypothesis
4. **Build environment matters** - Local CUDA 13.0 ≠ RunPod CUDA 12.x

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## Next Steps

1. **User**: Run `QUICK_START.md` deployment tests (15 min)
2. **Agent 6**: Analyze results and implement solution based on findings
3. **Production**: Update deployment process with CUDA version requirements

---

## Time Investment

- Binary creation: 15 min
- Build investigation: 20 min
- S3 upload: 5 min
- Documentation: 20 min
- **Total**: 60 min

**Deliverables**: 2 test binaries (uploaded), 8 documentation files (28 KB), actionable hypothesis

---

**Agent 5 Status**: ✅ COMPLETE - Ready for validation testing