Running Examples

Overview

The repository includes a complete training workflow for multisite λ-dynamics bias coefficient optimization. The main example demonstrates the end-to-end pipeline from combination generation through training and simulation execution.

For architectural details, see Contextual Bandit Setup. For workflow configuration options, see Workflow System.

Main Training Workflow

The primary example is examples/run_workflow_deepset.py, which implements the complete training pipeline using pretrained DeepSet embeddings.

Quick Start

cd examples
python run_workflow_deepset.py workflow_14benz.yaml

  • Generate combinations from the 14benz system

  • Split into train/val/test sets (70/15/15)

  • Train for 50 epochs with SLURM job submission

  • Save checkpoints every 5 epochs

  • Write outputs to training_output/

Configuration

Edit examples/workflow_14benz.yaml to customize paths and basic settings:

system:
  solvent_state: solv

create_combos:
  input_dir: /path/to/site_sub_fragments
  out_dir: /path/to/generated_combos

split:
  train_frac: 0.70
  val_frac: 0.15

training:
  num_epochs: 50

output:
  base_dir: /path/to/training_output
  save_checkpoints: true
  checkpoint_freq: 5

See Workflow System for complete configuration options including curriculum learning, pretraining, archiving, and reward function tuning.

Pretraining Example

To pretrain from existing simulations:

# Organize existing simulation data
mkdir -p pretraining
cp -r previous_runs/good_combos/* pretraining/

# Run pretraining
python run_deepset_pretraining.py --pretraining-dir pretraining --output-dir models --steps all

# Use pretrained model in main training
python run_workflow_deepset.py workflow_14benz.yaml

See Workflow System for detailed pretraining configuration, data organization, and benefits.

Example System: 14benz

The examples/14benz/ directory contains:

  • site1_sub1_pres.rtf through site1_sub5_pres.rtf: Site 1 substituents

  • site2_sub1_pres.rtf through site2_sub6_pres.rtf: Site 2 substituents

  • msld_flat.py: CHARMM/pyCHARMM simulation script

  • prep/: Pre-equilibrated structures

With 5 substituents at site 1 and 6 at site 2, the rotating anchor strategy generates:

  • 75 within-site combinations for site 1 (5 anchors × 15 each)

  • 186 within-site combinations for site 2 (6 anchors × 31 each)

  • 13,950 cross-site combinations (75 × 186)

  • Total: 14,211 unique combinations

SLURM Job Submission

For production training on a cluster:

cd examples/
sbatch training_test.sh

The training script:

  • Activates the conda environment

  • Runs python -u run_workflow_deepset.py workflow_14benz.yaml

  • Submits MSLD simulations as separate SLURM jobs

  • Manages up to 30 concurrent simulation jobs

  • Writes progress to training_status.out

The -u flag enables unbuffered output for real-time monitoring:

tail -f training_status.out

Resume Capability

Training automatically resumes from the latest checkpoint if interrupted:

sbatch training_test.sh  # Automatically detects and resumes

See Workflow System for checkpoint configuration and resume details.

Customizing the Workflow

To adapt for your system:

  1. Prepare fragments: Create siteN_subM_pres.rtf files for your sites/substituents

  2. Update config: Edit workflow_14benz.yaml with your paths and system.solvent_state (see workflow_deepset.yaml for an alternate template)

  3. Modify simulation script: Adapt msld_flat.py for your force field and protocol

  4. Run training: Execute python run_workflow_deepset.py your_config.yaml

See Workflow System for configuration options and Contextual Bandit Setup for architecture details.

Tips

  • Start with fewer epochs (e.g., 5) to test the full pipeline

  • Use max_concurrent_jobs to control cluster load

  • Monitor training_status.out for real-time progress

  • Check training_output/epoch_NNN/ for per-epoch results

  • Verify checkpoint files are saved at checkpoint_freq intervals

Reward Function Tuning

Epoch checkpoints enable testing different reward configurations without re-running simulations:

python examples/test_reward_configs.py training_output/epoch_005 \
    --configs reward_configs_example.yaml

See Workflow System for reward function details and experimentation workflow.

See Also