#SparseML

Lightning-Bolts extends PyTorch Lightning with additional components such as callbacks and datasets, facilitating applied research and production. It includes features like the Torch ORT Callback for enhanced training and inference speeds on NVIDIA or AMD GPUs. The SparseMLCallback further introduces sparsity in model fine-tuning using the DeepSparse engine. Encouraging community contributions, Lightning-Bolts evolves to support diverse machine learning needs in different domains without exaggeration.

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.

Neural Magic provides tools for enhancing deep learning models on CPUs, eliminating the need for GPUs. Explore solutions like SparseML, Sparsify, and SparseZoo for model optimization and deployment. Access detailed documentation for effective use in data science and AI projects. Participate in the Neural Magic Community on Slack for assistance and insights, and contribute to open-source documentation improvements via GitHub.