foxhunt/runpod_debug/README.md

RunPod Debug Tests - Agent 5

Purpose: Minimal reproduction tests to isolate RunPod training crash

Status: ✅ Ready for deployment testing (15 minutes)

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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.

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How to Validate

Step 1: Run Test Binaries (2 minutes)

# 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)

# 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

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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

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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

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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

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Agent 5 Status: ✅ COMPLETE - Ready for validation testing