This guide walks you through 20 fundamental PyTorch concepts—including tensor creation, operations, autograd, model building, data loading, GPU acceleration, and best‑practice tricks—providing clear code snippets and step‑by‑step explanations so you can quickly prototype, train, and deploy neural networks.
Baidu's Baige team introduces an extended multimodal data loader, automated ShareGPT format conversion, and offline sequence packing techniques that together double token throughput, cut SFT training time by up to six times, and improve GPU utilization and stability for large vision‑language models.
This article demonstrates five practical techniques for loading Excel and CSV data with pandas in Python, including smarter DataFrame construction, parallel processing with Joblib, and using pickle for faster storage, achieving speed improvements of up to three orders of magnitude.
This article walks through a suite of PyTorch techniques—including automatic mixed precision, BF16, gradient checkpointing, gradient accumulation, tensor sharding, efficient data loading, in‑place ops, lightweight optimizers, memory profiling, TorchScript, and kernel fusion—that together can cut peak GPU memory usage by up to twenty‑fold while preserving model accuracy.
This article walks through the step‑by‑step process of locating a .pkl file, correcting filename issues, using pandas to read the pickle data, handling path problems on macOS, and ensuring the file is correctly placed in a PyCharm project for successful data loading.
This article outlines six common data‑loading patterns for mobile applications and four practical techniques to reduce perceived waiting, helping designers create seamless, responsive experiences that keep users engaged even under poor network conditions.
