#PyTorch integration

Hummingbird is a library that compiles traditional machine learning models into tensor computations for use with neural network frameworks, such as PyTorch, to boost performance. It leverages the optimizations inherent in these frameworks, offers native hardware acceleration, and provides a single platform for legacy and modern models, all without altering existing models. It supports conversion to PyTorch, TorchScript, ONNX, and TVM, and is compatible with models like scikit-learn Decision Trees, Random Forests, LightGBM, and XGBoost. Hummingbird offers an intuitive API compatible with the scikit-learn interface for easy model inference.

Lhotse, a Python library, enhances speech and audio data preparation by offering flexible and accessible solutions. It smoothly integrates with PyTorch and supports both novice and seasoned users with its command-line interface and standardized data preparation methods. Lhotse's features include dynamic audio cuts for real-time operations like mixing and truncation, optimizing storage and bandwidth usage. It allows for data augmentation and feature extraction in both pre-computed and real-time modes, supports feature-space cut mixing, and works with Kaldi and ESPnet frameworks, making it a valuable tool for researchers and developers in audio processing.

The StreamingDataset offers efficient large dataset training from cloud storage with minimal delay. Designed for distributed multi-node environments, it optimizes data delivery and accuracy while integrating with PyTorch workflows. Supporting diverse data types like images, text, and video, it works with major cloud storage providers for simplified dataset handling. This system features deterministic sampling, fast mid-epoch resumption, high throughput, and balanced convergence, enabling reproducible model training on setups spanning from single GPUs to large clusters, ensuring efficiency and scalability.