This guide demonstrates the construction of a comprehensive optimization workflow utilizing NVIDIA Model Optimizer within Google Colab to train, prune, and refine a deep learning model. We commence by configuring the workspace and loading the CIFAR-10 dataset, followed by designing a ResNet structure and training it to achieve a robust initial performance. Subsequently, we employ FastNAS pruning to methodically decrease the model's computational footprint under specified FLOP limits while maintaining accuracy. Practical deployment challenges are addressed, the optimized subnetwork is reconstructed, and it undergoes fine-tuning to regain performance. The outcome is a fully operational procedure that transitions a model from initial training to a deployment-optimized state, all within a unified environment. Access the Complete Code Notebook.
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月之暗面此前透露，得益于海外API调用量的激增，其旗舰模型Kimi2.5在发布后不到一个月的收入，就已经超过了公司去年全年的总和。形势，就这么突然发生了变化。
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