tysam-code/hlb-CIFAR10

Health v2 · maintenance only

Adoption not part of the score

1310 stars · 81 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A single-file PyTorch implementation that trains a neural network to 94% accuracy on CIFAR-10 in under 6.3 seconds on a single A100 GPU, formerly holding the single-GPU training speed world record. It is designed to be minimal, hackable, and beginner-friendly for rapid deep-learning experimentation.

Use cases

• train a CIFAR-10 classifier in seconds on one GPU
• rapidly experiment with neural network training ideas
• reproduce ultra-fast CIFAR-10 training results
• benchmark single-GPU deep learning training speed
• learn how fast CIFAR-10 training pipelines work
• hack on a minimal deep learning training codebase

When to choose

• you want record-fast CIFAR-10 training on a single GPU
• you need a small, flat, hackable codebase for training experiments
• you want a beginner-friendly PyTorch training reference
• you're working in Colab or a CUDA environment

When to avoid

• you need production-grade, maintainable training code
• you don't have access to a CUDA GPU
• you need multi-GPU or distributed training
• you want a configurable framework rather than a single-file script

Facets

library · maturity maintenance

deep-learning machine-learning benchmarking deep-learning machine-learning computer-vision performance python cloud cifar10 fast-training pytorch single-file research-code record-setting colab-friendly gpu linux

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