Welcome to the AI Kit Optimization Tutorial! 🚀 Whether you're fine-tuning models or enhancing inference performance, this guide will help you maximize efficiency with our tools.

Key Optimization Techniques

  • Model Quantization 📊
    Reduce model size and improve inference speed using 8-bit or 4-bit quantization.
    Learn more about quantization

  • Pruning Strategies ✂️
    Remove redundant parameters to streamline model execution.

    Model Pruning

  • Distributed Training 🧩
    Scale training across multiple GPUs with our parallel computing framework.
    Explore distributed training options

Performance Tuning Tools

  • AutoTuner API ⚙️
    Leverage our auto-optimization engine to dynamically adjust hyperparameters.

    AutoTuner Interface

  • Profiling Dashboard 📈
    Monitor real-time performance metrics with interactive visualizations.
    Access the profiling dashboard

Best Practices

  1. Use mixed-precision training for faster convergence
  2. Regularly update model weights with gradient checkpointing
  3. Optimize data pipelines with our built-in caching system
  4. Check our documentation for advanced techniques

Optimization Workflow

Visualizing the optimization pipeline from training to deployment