#sparsification

SparseML is an open-source toolkit that optimizes neural networks using sparsification techniques, including pruning, quantization, and distillation. These methods create faster, smaller models while maintaining performance. SparseML integrates with PyTorch and Hugging Face and supports Sparse Transfer Learning through SparseZoo pre-trained models. Additionally, it converts optimized models to ONNX for deployment with DeepSparse, achieving GPU-level performance on CPUs. The toolkit provides a flexible recipe-based approach to model optimization with comprehensive tutorials and popular ML framework integrations.

Explore a rich repository of sparsified neural network models with the SparseZoo platform. Simplify and accelerate your deep learning projects by leveraging pre-built, inference-optimized models and customizable sparsification recipes. With SparseZoo's API-driven approach, you can easily integrate with existing networks or start from scratch, providing flexible solutions suitable for various applications. Enjoy the benefits of open-source community support and continuous updates that enhance model efficiency and performance. The collection of sparsified models and recipes effectively reduces time-to-value in deep learning projects.