Series Path · 5 parts
PyTorch Training Optimization
Squeeze every bit of performance out of your PyTorch training — data loading and augmentation, AMP, custom CUDA, and collate speed-ups.
Curriculum — 5 parts
- 01
PyTorch Training and Inference Optimization Guide
The guide includes practical code examples you can directly use in your projects, along with best practices and common pitfalls to avoid. Each section builds…
5 min read - 02
PyTorch Collate Function Speed-Up Guide
The collate function in PyTorch is a crucial component for optimizing data loading performance. It determines how individual samples are combined into batches,…
7 min read - 03
PyTorch Automatic Mixed Precision (AMP): A Comprehensive Guide
Automatic Mixed Precision (AMP) is PyTorch’s mechanism for running selected parts of a model in lower-precision floating point formats (float16 or bfloat16)…
21 min read - 04
CUDA Python: Accelerating Python Applications with GPU Computing
CUDA Python brings the power of NVIDIA’s CUDA platform directly to Python developers, enabling massive parallel computing capabilities without leaving the…
6 min read - 05
Albumentations vs TorchVision Transforms: Complete Code Guide
This guide compares two popular image augmentation libraries for PyTorch:
5 min read