Explore machine learning concepts related to pytorch. Clear explanations and practical insights.
PyTorch DataLoader deep dive — Dataset, Sampler, Workers, Collate internals, num_workers throughput profiling, memory analysis, serialization costs, production patterns (LMDB, WebDataset), and bottleneck diagnosis.
Complete guide to PyTorch pin_memory — how DMA transfers work, when pinning helps vs hurts, NUMA effects, profiling with torch.profiler, num_workers interaction, and debugging slow data loading.
GPU distributed parallelism: Data Parallel (DDP), Tensor Parallel, Pipeline Parallel, and ZeRO optimization for training large AI models.
Compare PyTorch DataParallel vs DistributedDataParallel for multi-GPU training. Learn GIL limitations, NCCL AllReduce, and DDP best practices.
Deep dive into PyTorch DataLoader num_workers parameter: how parallel workers prefetch data, optimal configuration, and common pitfalls.