nauticml 0.1.0

A configuration-driven orchestration framework for neural-network design space exploration.

nauticML

An orchestration framework for co-optimizing neural network models on hardware platforms.

Installation

Prerequisites

• Docker
• Conda (or miniconda)
• GPU support (optional, but recommended)

Setup Steps

1. Clone the repository

   % git clone <repository-url>
   % cd nauticML
   

2. Create Conda Development Environment

   To install (just once):

    % conda env create -f environment.yml
    % bash conda/post-install.sh nauticML 
   

   Once it is created:

   conda activate nauticML
   

3. Start Prefect and PostgreSQL services

   Use Docker Compose to launch the Prefect server and PostgreSQL database:

   docker compose up -d
   

   This will start:

   • PostgreSQL Database (port not exposed, internal use only)
   • Prefect Server (accessible at http://localhost:4200)

4. Configure your experiment

   Edit config.yaml to set up your experiment parameters:

   • Dataset configuration
   • Model architecture and hyperparameters
   • Bayesian optimization search space
   • Training and evaluation settings

5. Run the optimization pipeline

   python run.py
   

   This will:

   • Initialize the experiment
   • Load the dataset
   • Run Bayesian optimization iterations
   • Build and train Bayesian models
   • Evaluate model performance
   • Track results in Prefect

Configuration

The config.yaml file controls all experiment parameters:

experiment:
  seed: 42                    # Random seed for reproducibility
  save_dir: ./results         # Directory for saving results
  gpus: [0]                   # GPU device IDs to use

dataset:
  name: mnist                 # Dataset name

model:
  name: lenet                 # Model architecture
  is_quant: false            # Enable quantization
  dropout_rate: 0.4          # Dropout rate
  num_bayes_layer: 3         # Number of Bayesian layers

bayes_opt:
  num_iter: 3                # Number of optimization iterations
  tunable:                   # Hyperparameters to optimize
    dropout_rate:
      space: [0.1, 0.2, 0.3, 0.4]
    p_rate:
      space: [0.0, 0.1, 0.2]
    num_bayes_layer:
      space: [1, 2, 3]

Running Experiments

Local Execution

python run.py

Monitor with Prefect UI

After starting Docker Compose, access the Prefect UI at:

http://localhost:4200

The UI provides:

• Real-time flow execution monitoring
• Task logs and performance metrics
• Experiment history and results

Results

Results are saved to the configured save_dir (default: ./results):

• Model checkpoints
• Training logs
• Evaluation metrics
• Optimization summaries

Project Structure

nauticML/
├── nautic/                 # Core framework
├── tasks/                  # Reusable built-in tasks
├── projects/
│   └── heart25/            # Student project and experiments
├── docker-compose.yaml     # Service definitions
└── requirements.txt        # Python dependencies

GPU Support

To use GPU acceleration:

1. Ensure CUDA-compatible GPU is available
2. Configure GPU devices in config.yaml:

   experiment:
     gpus: [0, 1]  # Use GPU 0 and 1