# ray-distributed-trainer

Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management.

## Purpose

This skill provides distributed computing capabilities using Ray, enabling scalable model training, hyperparameter optimization, and resource management across compute clusters.

## Key Features

- **Ray Train**: Distributed training for PyTorch, TensorFlow, XGBoost
- **Ray Tune**: Scalable hyperparameter tuning with advanced schedulers
- **Resource Management**: Efficient CPU/GPU allocation
- **Fault Tolerance**: Automatic checkpointing and recovery
- **Elastic Training**: Dynamic scaling of training jobs

## When to Use

- Training models that don't fit on a single GPU
- Large-scale hyperparameter search
- Parallel model training experiments
- Resource-efficient cluster utilization
- Production-grade distributed ML pipelines

## Integration

Works seamlessly with:
- `pytorch-trainer` for PyTorch training
- `tensorflow-trainer` for TensorFlow training
- `optuna-hyperparameter-tuner` for hyperparameter optimization
- `mlflow-experiment-tracker` for experiment tracking
