foxhunt/AGENT_143_SUMMARY.md

Agent 143: CUDA Mandatory - Quick Summary

Mission: Stop wasting time on CPU fallback, make CUDA mandatory for ALL ML training

Status: ✅ COMPLETE

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What Changed

1. CUDA Now Default Feature

# ml/Cargo.toml
default = ["minimal-inference", "cuda"]

Impact: cargo build -p ml automatically enables CUDA

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2. New Helper Functions

// ml/src/lib.rs
use ml::get_training_device;

let device = get_training_device();  // Panics if no GPU with helpful error

Features:

• Fail-fast with 5-step troubleshooting guide
• No silent CPU fallback
• Clear CUDA requirements

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3. Removed --use-gpu Flags

Files Modified:

• ✅ train_tft_dbn.rs - Removed --use-gpu flag, force GPU
• ✅ train_ppo.rs - Removed --use-gpu flag, force GPU
• ✅ train_mamba2_dbn.rs - Already correct (mandatory CUDA)

Before:

cargo run -p ml --example train_tft_dbn --release --features cuda --use-gpu

After:

cargo run -p ml --example train_tft_dbn --release

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Error Message (When GPU Missing)

╔═══════════════════════════════════════════════════════════════════╗
║  CUDA GPU REQUIRED FOR TRAINING                                   ║
╚═══════════════════════════════════════════════════════════════════╝

Training requires CUDA GPU acceleration. CPU fallback is disabled.

Error: CUDA not available

Troubleshooting:

1. Check GPU availability:
   nvidia-smi

2. Verify CUDA toolkit installation:
   nvcc --version

3. Check CUDA libraries are in LD_LIBRARY_PATH:
   echo $LD_LIBRARY_PATH | grep cuda

4. Ensure project built with CUDA feature:
   cargo build --release --features cuda

5. Check CUDA environment variables:
   echo $CUDA_HOME
   ls $CUDA_HOME/lib64/

If GPU is unavailable, training cannot proceed.

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Files Changed

1. ✅ ml/Cargo.toml - CUDA default feature
2. ✅ ml/src/lib.rs - Helper functions (+109 lines)
3. ✅ ml/examples/train_tft_dbn.rs - Remove use_gpu flag
4. ✅ ml/examples/train_ppo.rs - Remove use_gpu flag
5. ✅ ml/examples/train_mamba2_dbn.rs - Already correct

Total: ~150 lines changed

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Training Commands (Simplified)

# TFT
cargo run --release -p ml --example train_tft_dbn -- --epochs 50

# PPO
cargo run --release -p ml --example train_ppo -- --epochs 50

# MAMBA-2
cargo run --release -p ml --example train_mamba2_dbn -- --epochs 200

# DQN
cargo run --release -p ml --example train_dqn -- --epochs 500

Note: No more --features cuda or --use-gpu needed!

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Impact

Before

• ❌ Silent CPU fallback (100x slower)
• ❌ Users confused about slow training
• ❌ Hours wasted on accidental CPU training
• ❌ --use-gpu flag easy to forget

After

• ✅ Fails immediately if GPU unavailable
• ✅ Clear troubleshooting guide
• ✅ CUDA automatic (no flags)
• ✅ Zero time wasted

---

Validation

Test CUDA Requirement

# This MUST fail with helpful error:
CUDA_VISIBLE_DEVICES="" cargo run --release -p ml --example train_tft_dbn

Verify Build

# Compiles successfully:
cargo check -p ml
cargo check -p ml --example train_tft_dbn
cargo check -p ml --example train_ppo
cargo check -p ml --example train_mamba2_dbn

Status: ✅ All checks pass (warnings only, no errors)

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Quick Reference

Old Pattern (WRONG)

let device = Device::cuda_if_available(0)?;  // Silent CPU fallback

New Pattern (CORRECT)

use ml::get_training_device;
let device = get_training_device();  // Fails if no GPU

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Next: User Experience

Before: Silent failure, hours wasted on CPU

After: Immediate failure, clear error, no time wasted

Mission accomplished: CUDA is now mandatory. No more CPU fallback. No more wasting time.