Special Annexure 5: PyTorch Best Practices & Industry Checklist
Abstract: Below is your Special Annexure 5: PyTorch Best Practices & Industry Checklist — crafted to be professional, comprehensive, and suitable for inclusion in a technical textbook. It contains best practices followed by AI engineers, researchers, and industry professionals to build reliable, efficient, and production-ready PyTorch models. Special Annexure 5: PyTorch Best Practices & Industry Checklist Guidelines for Efficient, Scalable, and Production-Ready Deep Learning Systems Part A: PyTorch Best Practices (Training & Development) 1. Use GPU/TPU Efficiently Always check for device availability: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") Move only necessary tensors to GPU. For multi-GPU tasks, prefer Distributed Data Parallel (DDP) over DataParallel. 2. Prefer DataLoader with num_workers Use efficient data loading: num_workers = 2–8 depending on CPU pin_memory=True for GPU t...