New SDK Functions for Model Optimization

🚀 New Features

AutoML Training with Bayesian & Hyperband

• Introduced automated machine learning training capabilities utilizing Bayesian optimization and Hyperband algorithms.
• Enables efficient hyperparameter tuning to achieve optimal model performance with minimal manual intervention.

New SDK Functions for Model Optimization

• Added get_prune_specs() and prune() functions to facilitate structured model pruning.
• Introduced get_retrain_specs() and retrain() functions to streamline the retraining process of pruned models.
• Implemented get_trt_engine_specs() and gen_trt_engine() functions for generating optimized TensorRT engines.
• Provided get_inference_specs() and inference() functions to simplify the inference process on optimized models.

Device Support Expansion

• Added support for the ARDUINO_NICLA_VISION device, enabling model deployment and benchmarking on this platform.

🐞 Bug Fixes

• Docker Installation Guide Update: Resolved issues in the INSTALLATION.md related to Docker usage:
  • Included the bash command in the Docker run instructions to ensure proper container initialization.
  • Added a step to install the package in editable mode (pip install -e .) to prevent ModuleNotFoundError for the netspresso module.

🧠 Why these matter

• The integration of AutoML capabilities significantly reduces the time and expertise required for model optimization, making advanced techniques more accessible.
• The new SDK functions provide a more modular and user-friendly approach to model pruning, retraining, engine generation, and inference, enhancing the overall developer experience.
• Expanding device support to include ARDUINO_NICLA_VISION broadens the deployment possibilities for users targeting edge devices.
• Updating the Docker installation guide ensures a smoother setup process, minimizing potential setup errors and improving usability.