ls-ml-toolkit 1.0.1

Label Studio ML Toolkit: Convert, Train, Optimize object detection models (CPU only)

LS-ML-Toolkit

A comprehensive machine learning toolkit for converting Label Studio annotations, training object detection models, and optimizing for deployment.

Features

• Label Studio to YOLO Conversion: Convert Label Studio JSON exports to YOLO format
• Image Downloading: Download images from S3/HTTP sources with progress tracking
• YOLO Model Training: Train YOLOv11 models with automatic device detection
• ONNX Export & Optimization: Export and optimize models for mobile deployment
• Cross-Platform GPU Support: MPS (macOS), CUDA (NVIDIA), ROCm (AMD)
• Centralized Configuration: YAML-based configuration with environment variable support
• Automatic .env Loading: Seamless integration with .env files for sensitive credentials
• Environment Variable Substitution: Support for ${VAR_NAME} and ${VAR_NAME:-default} syntax in YAML
• Flexible Import System: Works both as a Python module and as standalone scripts
• Secure Configuration: Sensitive data in .env, regular settings in YAML
• Modern CLI Interface: Beautiful terminal output with progress indicators and status displays
• Smart NMS Configuration: Optimized Non-Maximum Suppression settings to reduce warnings
• Automatic Training Directory Detection: Finds the latest YOLO training output automatically

Quick Start

Installation

# Install package (includes GPU support for all platforms)
pip install ls-ml-toolkit

# PyTorch automatically detects and uses:
# - macOS: Metal Performance Shaders (MPS)
# - Linux: CUDA/ROCm (if available)
# - Windows: CUDA (if available)

Basic Usage

# 1. Create .env file with your S3 credentials
cp env.example .env
# Edit .env with your AWS credentials

# 2. Train a model from Label Studio dataset
lsml-train dataset/v0.json --epochs 50 --batch 8 --device auto

# 3. Optimize an ONNX model
lsml-optimize model.onnx

# PyTorch automatically detects your platform and GPU
# All configuration is loaded automatically from .env and ls-ml-toolkit.yaml

Python API

from ls_ml_toolkit import LabelStudioToYOLOConverter, YOLOTrainer

# Convert dataset
converter = LabelStudioToYOLOConverter('dataset_name', 'path/to/labelstudio.json')
converter.process_dataset()

# Train model
trainer = YOLOTrainer('path/to/dataset')
trainer.train_model(epochs=50, device='auto')

Configuration

Environment Variables (.env)

Create a .env file with your sensitive credentials only:

# S3 Credentials (Sensitive Data)
LS_ML_S3_ACCESS_KEY_ID=your_access_key
LS_ML_S3_SECRET_ACCESS_KEY=your_secret_key

# Optional: Environment-specific settings
LS_ML_S3_DEFAULT_REGION=us-east-1
LS_ML_S3_ENDPOINT=https://custom-s3.example.com

Important:

• Only use .env for sensitive data (API keys, passwords, tokens)
• All other configuration should be in ls-ml-toolkit.yaml
• Copy env.example to .env and configure your credentials
• The toolkit automatically loads these variables and makes them available throughout the application

YAML Configuration (ls-ml-toolkit.yaml)

All regular settings are configured in ls-ml-toolkit.yaml. Environment variables are used only for sensitive data:

# Dataset Configuration
dataset:
  base_dir: "dataset"
  train_split: 0.8
  val_split: 0.2

# Training Configuration
training:
  epochs: 50
  batch_size: 8
  image_size: 640
  device: "auto"
  
  # NMS (Non-Maximum Suppression) settings
  nms:
    iou_threshold: 0.7  # IoU threshold for NMS (0.0-1.0) - higher = fewer detections
    conf_threshold: 0.25  # Confidence threshold for predictions (0.0-1.0) - higher = fewer detections
    max_det: 300  # Maximum number of detections per image - lower = faster processing

# Model Export Configuration
export:
  model_path: "shared/models/layout_yolo_universal.onnx"
  optimized_model_path: "shared/models/layout_yolo_universal_optimized.onnx"  # Optional
  optimize: true
  optimization_level: "all"

# S3 Configuration (uses .env for sensitive data)
s3:
  access_key_id: "${LS_ML_S3_ACCESS_KEY_ID}"  # From .env file
  secret_access_key: "${LS_ML_S3_SECRET_ACCESS_KEY}"  # From .env file
  region: "${LS_ML_S3_DEFAULT_REGION:-us-east-1}"  # From .env file with default
  endpoint: "${LS_ML_S3_ENDPOINT:-}"  # From .env file (optional)

# Platform-specific settings
platform:
  auto_detect_gpu: true
  force_device: null
  macos:
    device: "mps"
    batch_size: 16
  linux:
    device: "auto"  # PyTorch will auto-detect GPU
    batch_size: 16

Platform Support

macOS

• MPS Support: Automatic Metal Performance Shaders detection
• Installation: pip install ls-ml-toolkit

Linux

• CUDA Support: Automatic NVIDIA GPU detection and configuration
• ROCm Support: Automatic AMD GPU detection
• Installation: pip install ls-ml-toolkit
• Requirements: NVIDIA drivers + CUDA toolkit OR ROCm drivers

Windows

• CUDA Support: Automatic NVIDIA GPU detection
• Installation: pip install ls-ml-toolkit
• Requirements: NVIDIA drivers + CUDA toolkit

Development

Setup Development Environment

git clone https://github.com/bavix/ls-ml-toolkit.git
cd ls-ml-toolkit
pip install -e .
pip install -r requirements-dev.txt

Running Tests

pytest tests/

Building Packages

# Build package
python -m build

# Install in development mode
pip install -e .

Command Line Tools

• lsml-train: Train YOLO models from Label Studio datasets
• lsml-optimize: Optimize ONNX models for deployment

CLI Features

• Beautiful Interface: Modern terminal UI with colors, icons, and progress indicators
• Status Tracking: Real-time progress updates during training and optimization
• Configuration Display: Shows current settings in a formatted table
• File Tree Display: Visual representation of training results and file structure
• Error Handling: Clear error messages and troubleshooting guidance

Examples

Training with Custom Configuration

# Method 1: Use .env file (recommended for secrets)
echo "LS_ML_S3_ACCESS_KEY_ID=your_key" >> .env
echo "LS_ML_S3_SECRET_ACCESS_KEY=your_secret" >> .env

# Method 2: Use environment variables
export LS_ML_S3_ACCESS_KEY_ID="your_key"
export LS_ML_S3_SECRET_ACCESS_KEY="your_secret"

# Train with custom settings
lsml-train dataset/v0.json \
  --epochs 100 \
  --batch 16 \
  --device mps \
  --imgsz 640 \
  --optimize \
  --force-download

Using Configuration File

# Use custom YAML configuration
lsml-train dataset/v0.json --config custom-config.yaml

# Override specific settings via command line
lsml-train dataset/v0.json --epochs 100 --batch 16 --device mps

Advanced Usage Examples

# Force re-download of existing images
lsml-train dataset/v0.json --force-download

# Train with custom NMS settings (via YAML config)
# Edit ls-ml-toolkit.yaml:
# training:
#   nms:
#     iou_threshold: 0.8
#     conf_threshold: 0.3
#     max_det: 200

# Optimize existing ONNX model
lsml-optimize model.onnx --level extended

# Use custom output path for optimization
lsml-optimize model.onnx --output optimized_model.onnx

Quick Setup Guide

# 1. Clone and install
git clone https://github.com/bavix/ls-ml-toolkit.git
cd ls-ml-toolkit
pip install -e .

# 2. Setup credentials
cp env.example .env
# Edit .env with your AWS credentials

# 3. Train your model
lsml-train your_dataset.json --epochs 50 --batch 8

Environment Variable Substitution

The YAML configuration supports environment variable substitution only for sensitive data:

# S3 Configuration (uses .env variables)
s3:
  access_key_id: "${LS_ML_S3_ACCESS_KEY_ID}"  # From .env file
  secret_access_key: "${LS_ML_S3_SECRET_ACCESS_KEY}"  # From .env file
  region: "${LS_ML_S3_DEFAULT_REGION:-us-east-1}"  # From .env with default
  endpoint: "${LS_ML_S3_ENDPOINT:-}"  # From .env (optional)

# Regular configuration (no env vars needed)
training:
  epochs: 50
  batch_size: 8
  image_size: 640

Naming Convention: LS_ML_<CATEGORY>_<SETTING>

• LS_ML_S3_ACCESS_KEY_ID - S3 credentials
• LS_ML_S3_SECRET_ACCESS_KEY - S3 credentials
• LS_ML_S3_DEFAULT_REGION - S3 configuration
• LS_ML_S3_ENDPOINT - S3 endpoint

Configuration Best Practices

✅ Use .env for:

• API Keys & Secrets: LS_ML_S3_ACCESS_KEY_ID, LS_ML_S3_SECRET_ACCESS_KEY
• Environment-specific settings: LS_ML_S3_DEFAULT_REGION, LS_ML_S3_ENDPOINT
• Values that change between deployments

✅ Use YAML for:

• Regular configuration: epochs, batch_size, image_size
• Default values: model paths, directory structures
• Platform settings: device detection, optimization levels

Model Export Configuration

Model Paths

• model_path: Path for the regular ONNX export (required)
• optimized_model_path: Path for the optimized ONNX model (optional)

Fallback Behavior

If optimized_model_path is not specified in the configuration:

• Training script: Uses {model_path}_optimized.onnx as fallback
• Optimization script: Uses {input_model}_optimized.onnx as fallback

Examples

export:
  model_path: "models/my_model.onnx"
  optimized_model_path: "models/my_model_optimized.onnx"  # Optional
  optimize: true
  optimization_level: "all"

• All non-sensitive settings

🔒 Security:

• Never commit .env files to version control
• Use .env.example as a template
• Keep sensitive data separate from code

File Structure

ls-ml-toolkit/
├── src/
│   └── ls_ml_toolkit/         # Main package source
│       ├── __init__.py
│       ├── train.py            # Main training script
│       ├── config_loader.py    # Configuration management with .env support
│       ├── env_loader.py       # Environment variable loader
│       ├── optimize_onnx.py    # ONNX optimization
│       └── ui.py               # CLI UI components
├── tests/                      # Test files
├── requirements.txt            # Dependencies
├── pyproject.toml             # Package configuration
├── setup.py                   # Setup script
├── ls-ml-toolkit.yaml         # Main configuration with env var substitution
├── env.example                # Environment template
├── .env                       # Your environment variables (create from .env.example)
└── README.md                  # This file

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

Troubleshooting

NMS Time Limit Warnings

If you see WARNING ⚠️ NMS time limit 2.800s exceeded:

What it means:

• NMS (Non-Maximum Suppression) operation is taking too long
• This can slow down validation and inference
• Usually happens with many objects or suboptimal settings

How to fix:

1. Optimize NMS settings in ls-ml-toolkit.yaml:

   training:
     nms:
       iou_threshold: 0.8    # Higher = fewer detections (0.7-0.9)
       conf_threshold: 0.3   # Higher = fewer detections (0.25-0.5)
       max_det: 200          # Lower = fewer detections (100-300)
   

2. Reduce batch size if memory allows:

   training:
     batch_size: 4  # Reduce from 8 to 4
   

3. Optimize other parameters: Focus on iou_threshold, conf_threshold, and max_det for better performance

Environment Variables Not Loading

If your .env file is not being loaded:

1. Check file location: Ensure .env is in the project root directory
2. Verify file format: Use KEY=value format (no spaces around =)
3. Check permissions: Ensure the file is readable
4. Copy from template: Use cp env.example .env as a starting point
5. Check naming: Use exact variable names like LS_ML_S3_ACCESS_KEY_ID

YAML Variable Substitution Issues

If environment variables are not substituted in YAML:

1. Check variable names: Use exact names like LS_ML_S3_ACCESS_KEY_ID
2. Verify syntax: Use ${VAR_NAME} or ${VAR_NAME:-default} format
3. Test loading: Run python -c "from ls_ml_toolkit.config_loader import ConfigLoader; print(ConfigLoader().get_s3_config())"
4. Remember: Only use env vars for sensitive data, not regular config

Import Errors

If you get import errors when running scripts:

1. Install in development mode: pip install -e .
2. Check Python path: Ensure the package is in your Python path
3. Use absolute imports: The toolkit supports both relative and absolute imports

Training Directory Issues

If the script can't find the latest training directory:

1. Check YOLO output: Ensure runs/detect/ directory exists
2. Verify permissions: Make sure the script can read the directory
3. Manual path: The script automatically finds the latest train* directory

ONNX Optimization Issues

If ONNX optimization fails:

1. Install dependencies: pip install onnx onnxruntime
2. Check model format: Ensure input is a valid ONNX model
3. Use fallback: The script will use default naming if config path is missing

License

This project is licensed under the MIT License - see the LICENSE file for details.