ModelSmith: Machine learning model optimization for real-world deployments

In artificial intelligence (AI), the efficient deployment of machine learning models across diverse devices and platforms remains a significant challenge. We built ModelSmith as an open-source toolbox for machine learning model optimization to address this challenge. 

ModelSmith enhances model efficiency and facilitates deployment across diverse devices and platforms, making it an indispensable tool for efficient-AI practitioners. With its intuitive interface, supportive guidance, and innovative algorithms, ModelSmith aspires to contribute to the ongoing evolution of efficient AI. Regardless of one's background—be it researcher, developer, or industry professional—it seeks to empower individuals to unlock the latent potential of their machine learning models in real-world applications. 

The rise of AI models and the need for efficient AI 

AI models, particularly deep neural networks (DNNs), have become increasingly complex and resource intensive. While delivering exceptional performance, they often require substantial computational resources and memory to operate effectively. As a result, deploying these models on resource-constrained devices becomes an essential task. 

One of the primary challenges associated with this is the size of the models. AI models can be large and cumbersome, making them impractical for real-world scenarios where resources are limited. Moreover, large models require more storage space and consume more energy during inference, leading to slower execution times and higher costs.  

Model compression has emerged as a powerful and necessary solution to address these challenges. This technique aims to reduce the size of AI models without significantly sacrificing performance. By eliminating redundancy and minimizing unnecessary parameters, compression techniques enable the creation of smaller, more efficient models that are better suited for deployment in resource-constrained environments.  

What is ModelSmith? 

ModelSmith, an open source project toolkit, is designed to optimize models for deployment across a wide array of devices and platforms. This ensures that deep neural networks operate more efficiently, becoming faster, smaller, and more energy efficient.  

ModelSmith specializes in the compression of machine learning models, a crucial process that enables the deployment of large models on diverse devices and platforms, thereby maintaining satisfactory performance standards. 

Additionally, ModelSmith plays a pivotal role in reconciling the supply and demand disparities in AI computing. It achieves this by tailoring models to meet the requirements of various real-world applications, effectively bridging the gap between theoretical advancements and practical utility in artificial intelligence. 

Why ModelSmith?  

ModelSmith offers innovative solutions tailored to meet the diverse needs of machine learning practitioners. Whether you are working with vision models, natural language models, or multi-modal models, ModelSmith has you covered. Its arsenal of model compression and machine unlearning algorithms optimizes your models for real-world deployment. 

What sets ModelSmith apart is its user-centric approach with a user-friendly interface and comprehensive guidance. ModelSmith empowers users of all expertise levels to select the most suitable algorithms for their specific requirements. Whether you are a seasoned AI veteran or just starting your journey, ModelSmith makes model optimization accessible to everyone. 

Moreover, ModelSmith leverages a wide array of technologies, including quantization, pruning, and machine unlearning, to deliver versatile solutions. This versatility allows ModelSmith to adapt to the unique needs of each application, effectively reconciling the supply and demand disparities in AI computing. 

Try ModelSmith 

With the repository now available, the ModelSmith project enters an exciting new phase of accessibility and collaboration. Developers, researchers, and enthusiasts can now access the codebase, contribute to its evolution, and leverage its capabilities. With the repository open to the public, we look forward to seeing the many contributions and innovative applications that emerge from ModelSmith. 

Try ModelSmith today. (https://github.com/cisco-open/modelsmith) 

References 

ModelSmith uses some algorithms from the following research papers, which are also outputs of the team's efficient AI research.  

Graph mixture of experts: Learning on large-scale graphs with explicit diversity modeling (NeurIPS 2023)  

Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning (NeurIPS 2023) 

Model sparsity can simplify machine unlearning (NeurIPS 2023) 

Adaptive Deep Neural Network Inference Optimization with EENet (WACV 2024) 

Causal-dfq: Causality guided data-free network quantization (ICCV 2023)  

Network specialization via feature-level knowledge distillation (CVPR 2024 WS) 

Efficient Multitask Dense Predictor via Binarization (CVPR 2024) 

Enhancing Post-training Quantization Calibration through Contrastive Learning (CVPR 2024) 

MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning (CVPR 2024)