foxhunt/HYPEROPT_LOG_IMPLEMENTATION_COMPLETE.md

Hyperopt Log File Implementation - COMPLETE

Status: ✅ IMPLEMENTED

All four ML model adapters now write proper log files during hyperparameter optimization training.

Implementation Summary

Files Modified

1. ml/src/hyperopt/adapters/mamba2.rs

   • Added imports: std::fs::OpenOptions, std::io::Write as IoWrite
   • Added helper functions: write_training_log_mamba2(), write_trial_result_mamba2()
   • Modified train_with_params(): trial timing, log writes at start/end
   • Lines modified: 37-38 (imports), 703-742 (helpers), 750, 783-786, 942-959 (logging)

2. ml/src/hyperopt/adapters/dqn.rs

   • Added imports: std::fs::OpenOptions, std::io::Write as IoWrite
   • Added helper functions: write_training_log_dqn(), write_trial_result_dqn()
   • Modified train_with_params(): trial timing, log writes, directory logging
   • Lines modified: 35-37 (imports), 289-328 (helpers), 336, 348-352, 363-364, 474-491 (logging)

3. ml/src/hyperopt/adapters/ppo.rs

   • Added imports: std::fs::OpenOptions, std::io::Write as IoWrite
   • Added helper functions: write_training_log_ppo(), write_trial_result_ppo()
   • Modified train_with_params(): trial timing, log writes, directory creation
   • Lines modified: 35-37 (imports), 248-287 (helpers), 295, 304-316, 446-463 (logging)

4. ml/src/hyperopt/adapters/tft.rs

   • Added imports: std::fs::OpenOptions, std::io::Write as IoWrite
   • Added helper functions: write_training_log_tft(), write_trial_result_tft()
   • Modified train_with_params(): trial timing, log writes, directory creation
   • Lines modified: 37-39 (imports), 279-318 (helpers), 326, 335-339, 356-363, 435-452 (logging)

Features Implemented

1. Training Log File ({logs_dir}/training.log)

Format:

[2025-10-29 14:32:15] === Starting MAMBA-2 Trial ===
Params: Mamba2Params {
    learning_rate: 0.0001,
    batch_size: 32,
    dropout: 0.1,
    ...
}
[2025-10-29 14:34:23] Training completed in 128.45s: val_loss=0.234567, train_loss=0.198765, accuracy=67.89%

Features:

• UTC timestamps for consistency across deployments
• Append mode (accumulates across trials)
• Params logged at trial start (full Debug format)
• Metrics logged at trial end (key metrics + duration)
• Model-specific metric formatting:
  • MAMBA-2: val_loss, train_loss, accuracy
  • DQN: loss, q_value
  • PPO: val_policy_loss, val_value_loss
  • TFT: val_loss, train_loss, rmse

2. Trial Results JSON ({hyperopt_dir}/trials.json)

Format:

[
  {
    "trial_num": 0,
    "params": {
      "learning_rate": 0.0001,
      "batch_size": 32,
      "dropout": 0.1,
      ...
    },
    "objective": 0.234567,
    "duration_secs": 128.45
  },
  {
    "trial_num": 0,
    "params": { ... },
    "objective": 0.198765,
    "duration_secs": 115.23
  }
]

Features:

• JSON array format (pretty-printed)
• Accumulates trials across multiple runs
• Compatible with TrialResult<P> generic type
• Trial numbers set to 0 (optimizer overwrites with actual trial number)
• Includes full parameter set for reproducibility
• Objective value (lower is better)
• Duration in seconds

3. Directory Structure

/runpod-volume/training_runs/{model_name}/run_{run_id}/
├── checkpoints/
│   └── best_model.safetensors
├── logs/
│   └── training.log          # ← NEW: Training progress log
├── hyperopt/
│   └── trials.json           # ← NEW: Trial results JSON
└── metrics/

Implementation Pattern

Helper Functions

Each adapter has two helper functions with model-specific names to avoid conflicts:

1. write_training_log_{model}(logs_dir, message)

   • Creates/appends to training.log
   • Adds UTC timestamp prefix
   • Non-fatal errors (.ok() to ignore I/O failures)

2. write_trial_result_{model}(hyperopt_dir, trial_result)

   • Creates/updates trials.json
   • Reads existing trials, appends new one
   • Pretty-prints JSON for human readability
   • Non-fatal errors (.ok() to ignore I/O failures)

Integration Points in train_with_params()

fn train_with_params(&mut self, params: Self::Params) -> Result<Self::Metrics, MLError> {
    // 1. START: Trial timing
    let trial_start = std::time::Instant::now();

    // 2. Log trial start (after parameter logging)
    write_training_log_{model}(
        &self.training_paths.logs_dir(),
        &format!("=== Starting {MODEL} Trial ===\nParams: {:#?}", params)
    ).ok();

    // 3. Create all directories (checkpoints, logs, hyperopt, metrics)
    self.training_paths.create_all()
        .map_err(|e| MLError::ModelError(...))?;

    // ... existing training code ...

    // 4. END: Log completion + write trial result
    let duration_secs = trial_start.elapsed().as_secs_f64();
    write_training_log_{model}(
        &self.training_paths.logs_dir(),
        &format!("Training completed in {:.2}s: metrics={:#?}", duration_secs, metrics)
    ).ok();

    let trial_result = crate::hyperopt::traits::TrialResult {
        trial_num: 0, // Overwritten by optimizer
        params,
        objective: Self::extract_objective(&metrics),
        duration_secs,
    };

    write_trial_result_{model}(&self.training_paths.hyperopt_dir(), &trial_result).ok();

    Ok(metrics)
}

Error Handling

• Non-fatal I/O errors: .ok() used on all log writes

  • Training continues even if log write fails
  • Prevents trial failure due to disk issues
  • Console logging (via info!()) remains as backup

• Directory creation: Fails fast with proper error

  • Critical path (needed for checkpoints)
  • MLError::ModelError with context

Verification

Quick Test

# Run hyperopt examples (any model)
cargo run -p ml --example hyperopt_mamba2_demo --release --features cuda

# Check logs were created
ls -lh /tmp/ml_training/training_runs/mamba2/run_*/logs/training.log
cat /tmp/ml_training/training_runs/mamba2/run_*/hyperopt/trials.json

Expected Output Structure

training.log:

• Multiple timestamped entries per trial
• Start marker with full params (Debug format)
• End marker with key metrics + duration
• Chronological order (append mode)

trials.json:

• Valid JSON array
• One object per trial
• All fields present (trial_num, params, objective, duration_secs)
• Pretty-printed (2-space indentation)

Dependencies

No new dependencies added. Uses existing:

• std::fs::OpenOptions - file I/O
• std::io::Write - write operations
• chrono::Utc - timestamps (already in ml/Cargo.toml)
• serde_json - JSON serialization (already in ml/Cargo.toml)

Production Deployment

Runpod Volume Mount

Logs will be written to:

/runpod-volume/training_runs/{model}/run_{run_id}/
├── logs/training.log
└── hyperopt/trials.json

S3 Sync

These files will be automatically synced to S3 via existing upload logic:

aws s3 sync /runpod-volume/training_runs/ s3://se3zdnb5o4/training_runs/ \
  --endpoint-url https://s3api-eur-is-1.runpod.io \
  --profile runpod

Monitoring

Check hyperopt progress:

# View recent logs
tail -f /runpod-volume/training_runs/mamba2/run_*/logs/training.log

# Check trial count
jq 'length' /runpod-volume/training_runs/mamba2/run_*/hyperopt/trials.json

# View best trial
jq '[.[] | {trial: .trial_num, obj: .objective, dur: .duration_secs}] | sort_by(.obj) | .[0]' \
  /runpod-volume/training_runs/mamba2/run_*/hyperopt/trials.json

Testing Checklist

• MAMBA-2 adapter: Log functions added
• DQN adapter: Log functions added
• PPO adapter: Log functions added
• TFT adapter: Log functions added
• All adapters: Imports added
• All adapters: Trial timing added
• All adapters: Directory creation verified
• All adapters: Start/end logging added
• All adapters: Trial result JSON write added
• Compilation check (blocked by sqlx database connection)
• Integration test (run hyperopt examples)
• Runpod deployment test

Notes

1. Trial Numbers: Set to 0 in adapters, optimizer overwrites with actual trial number
2. Timestamp Format: UTC ISO 8601 (%Y-%m-%d %H:%M:%S)
3. Append vs Overwrite: Logs append, JSON array accumulates
4. Metrics Logging: Model-specific format (different metrics per model type)
5. Directory Creation: Uses TrainingPaths.create_all() pattern from checkpoints

Related Files

• Implementation guide: /home/jgrusewski/Work/foxhunt/HYPEROPT_LOG_IMPLEMENTATION.md
• TrainingPaths: /home/jgrusewski/Work/foxhunt/ml/src/hyperopt/paths.rs
• TrialResult: /home/jgrusewski/Work/foxhunt/ml/src/hyperopt/traits.rs:364-373
• Examples:
  • /home/jgrusewski/Work/foxhunt/ml/examples/hyperopt_mamba2_demo.rs
  • /home/jgrusewski/Work/foxhunt/ml/examples/hyperopt_dqn_demo.rs
  • /home/jgrusewski/Work/foxhunt/ml/examples/hyperopt_ppo_demo.rs
  • /home/jgrusewski/Work/foxhunt/ml/examples/hyperopt_tft_demo.rs

Next Steps

1. Fix SQLx Database Connection (blocking compilation)

   • Start PostgreSQL: docker-compose up -d postgres
   • Or disable regime module temporarily

2. Integration Testing

   • Run hyperopt examples with 2-3 trials
   • Verify log files created
   • Verify JSON format correct
   • Check S3 sync works

3. Runpod Deployment

   • Deploy with new code
   • Monitor log file creation
   • Verify S3 upload includes logs
   • Check trial progress via JSON

4. Documentation Update

   • Update CLAUDE.md with log file locations
   • Add monitoring commands to deployment guide
   • Document log format in ML_TRAINING_PARQUET_GUIDE.md