# cybertronai/gradient-checkpointing

Make huge neural nets fit in memory

Repository: https://github.com/cybertronai/gradient-checkpointing
Canonical: https://ross.abutalabs.com/products/gradient-checkpointing
Language: Python
License: MIT
License Family: permissive
Last push: 2020-04-26T07:34:18+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3263, "days_push": 2320, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2843, forks 279 (observed 2026-08-28T04:07:24.876152+00:00)

## What it is
A Python library that reduces GPU memory usage when training very deep neural networks via gradient checkpointing, trading computation for memory. It rewrites the TensorFlow computation graph so backpropagation memory scales as O(sqrt(n)) instead of linearly with layer count.

## Use cases
- fit a huge neural network into GPU memory
- train deep networks with large batch sizes
- reduce memory cost of backpropagation
- train 10x larger models on the same GPU
- apply sublinear-memory gradient computation in TensorFlow

## When to choose
- you train very deep feed-forward models in TensorFlow 1.x and hit GPU memory limits
- you can afford ~20% extra compute time to fit a much larger model or batch size

## When to avoid
- you use PyTorch or modern frameworks with built-in checkpointing
- you need actively maintained TensorFlow 2.x support
- compute time is more constrained than memory

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, llm-training, gpu-computing
- domain: deep-learning, machine-learning, gpu-computing
- platform: python
- tags: gradient-checkpointing, tensorflow, memory-optimization, backpropagation, training, gpu

## Member repositories
- cybertronai/gradient-checkpointing (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:24.876152+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:37:13.290890+00:00, confidence not recorded.
  - readme: https://github.com/cybertronai/gradient-checkpointing (fetched 2026-08-28T04:07:24.876152+00:00, sha ef3a4bba47ba)
- Data as of 2026-08-30T08:39:29.467469+00:00.