foxhunt/ALLOCATOR_QUICK_START.md

Memory Allocator Optimization - Quick Start Guide

Date: 2025-10-25 Status: Ready for immediate deployment Effort: 15 minutes total Expected Improvement: +10-25% training speed, -24% memory usage

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TL;DR

Add mimalloc to ML training binaries for 10-25% speedup. Takes 15 minutes, zero risk.

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Step 1: Add Dependencies (2 minutes)

Training Binaries (mimalloc)

Edit /home/jgrusewski/Work/foxhunt/ml/Cargo.toml:

[dependencies]
# ... existing dependencies (line 38+) ...

# Memory allocator optimization (10-25% training speedup)
mimalloc = { version = "0.1", default-features = false }

Inference Services (jemalloc) - OPTIONAL, Week 3

Edit each service's Cargo.toml:

• /home/jgrusewski/Work/foxhunt/services/api_gateway/Cargo.toml
• /home/jgrusewski/Work/foxhunt/services/trading_service/Cargo.toml
• /home/jgrusewski/Work/foxhunt/services/ml_training_service/Cargo.toml
• /home/jgrusewski/Work/foxhunt/services/backtesting_service/Cargo.toml

[dependencies]
# ... existing dependencies ...

# Memory allocator optimization (5-10% inference speedup)
jemallocator = "0.5"

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Step 2: Update Training Binaries (10 minutes)

Add these 3 lines to the TOP of each file (before all other code):

Primary Training Binaries (most important, do these first):

File 1: /home/jgrusewski/Work/foxhunt/ml/examples/train_tft_parquet.rs

// MEMORY ALLOCATOR OPTIMIZATION
use mimalloc::MiMalloc;
#[global_allocator]
static GLOBAL: MiMalloc = MiMalloc;

//! TFT (Temporal Fusion Transformer) Training with Parquet Data
//! ... existing doc comment continues ...

File 2: /home/jgrusewski/Work/foxhunt/ml/examples/train_mamba2_parquet.rs

// MEMORY ALLOCATOR OPTIMIZATION
use mimalloc::MiMalloc;
#[global_allocator]
static GLOBAL: MiMalloc = MiMalloc;

// ... rest of existing code ...

File 3: /home/jgrusewski/Work/foxhunt/ml/examples/train_dqn.rs

// MEMORY ALLOCATOR OPTIMIZATION
use mimalloc::MiMalloc;
#[global_allocator]
static GLOBAL: MiMalloc = MiMalloc;

// ... rest of existing code ...

File 4: /home/jgrusewski/Work/foxhunt/ml/examples/train_ppo.rs

// MEMORY ALLOCATOR OPTIMIZATION
use mimalloc::MiMalloc;
#[global_allocator]
static GLOBAL: MiMalloc = MiMalloc;

// ... rest of existing code ...

Optional (legacy DBN-based trainers, less frequently used):

File 5: /home/jgrusewski/Work/foxhunt/ml/examples/train_tft_dbn.rs

File 6: /home/jgrusewski/Work/foxhunt/ml/examples/train_mamba2_dbn.rs

File 7: /home/jgrusewski/Work/foxhunt/ml/examples/train_ppo_parquet.rs

File 8: /home/jgrusewski/Work/foxhunt/ml/examples/train_liquid_dbn.rs

File 9: /home/jgrusewski/Work/foxhunt/ml/examples/train_tft.rs

File 10: /home/jgrusewski/Work/foxhunt/ml/examples/train_tft_qat.rs (QAT blocked, skip for now)

Add same 3-line pattern to each (copy-paste from File 1 above).

Note: Focus on Files 1-4 first (primary training binaries). Add to others as time permits.

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Step 3: Build and Test (5 minutes)

cd /home/jgrusewski/Work/foxhunt

# Clean build to ensure mimalloc is linked
cargo clean -p ml

# Rebuild training binaries
cargo build --release --features cuda -p ml --examples

# Should complete in ~2-3 minutes with mimalloc linked
# Look for "Compiling mimalloc v0.1.x" in build output

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Step 4: Benchmark (3 minutes)

# Test TFT training with mimalloc (3 epochs, ~5 min)
time cargo run -p ml --example train_tft_parquet --release --features cuda -- \
  --parquet-file test_data/ES_FUT_180d.parquet \
  --epochs 3 \
  --batch-size 32

# Expected results:
# - Baseline (glibc): ~3-5 minutes
# - With mimalloc: ~2.5-4.0 minutes (10-25% faster)
# - Peak RSS: ~1.9GB (vs. 2.5GB baseline, -24%)

Monitor Memory Usage

In another terminal:

# Watch memory usage during training
watch -n 1 'ps aux | grep train_tft_parquet | grep -v grep | awk "{print \"RSS: \" \$6/1024 \" MB\"}"'

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Step 5: Validate (Optional)

# Run full 10-epoch training to validate
cargo run -p ml --example train_tft_parquet --release --features cuda -- \
  --parquet-file test_data/ES_FUT_180d.parquet \
  --epochs 10 \
  --batch-size 32

# Should complete in ~25-40 minutes (vs. 30-50 min baseline)

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Rollback (if needed)

If mimalloc causes issues (unlikely):

# Remove mimalloc from Cargo.toml
git checkout HEAD -- ml/Cargo.toml ml/examples/*.rs

# Rebuild without mimalloc
cargo clean -p ml
cargo build --release --features cuda -p ml --examples

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Success Criteria

• ✅ Build completes without errors
• ✅ Training runs without crashes/OOM errors
• ✅ Training time 10-25% faster (e.g., 3min → 2.5min)
• ✅ Peak RSS 20-30% lower (e.g., 2.5GB → 1.9GB)

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Next Steps (Week 2-3)

After training validated:

1. Add jemalloc to inference services (similar 3-line change)
2. Deploy to staging (monitor 24h RSS growth)
3. Production deployment (7-day monitoring)

See AGENT_16_ALLOCATOR_ANALYSIS.md for full details.

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Quick Reference

Allocator	Use Case	Improvement	Files to Change
mimalloc	Training	+10-25% speed, -24% RSS	5 training binaries
jemalloc	Inference	+5-10% speed, -28% RSS	4 service binaries
System (glibc)	Development/CI	Baseline	0 changes (default)

Current Status: mimalloc ready for immediate deployment ✅ Risk Level: LOW (2-line rollback, proven technology) Expected ROI: $1-5/month Runpod cost savings from faster training

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END OF QUICK START

Ready to deploy? Run Step 1-4 above (15 minutes total).