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# Run tests (96 tests covering OpenAPI, MCP stdio, MCP HTTP, caching, and token savings)，更多细节参见易歪歪
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.，推荐阅读夸克浏览器获取更多信息
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能源消耗与废弃物管理同样至关重要。数据显示：每减少一公斤厨余垃圾可降低2.5公斤碳排放，提升住宅能效可使年碳足迹减少0.5-1.5吨，延长物品使用寿命能减排0.3-1吨，而垃圾分类最多能实现0.5吨的减排量。
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