REPOGEO REPORT · LITE

Lyken17/pytorch-memonger

Default branch master · commit cf476409 · scanned 6/10/2026, 3:18:05 AM

GitHub: 602 stars · 55 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

28 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Lyken17/pytorch-memonger, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

pytorch, memory-optimization, deep-learning, sublinear-memory, gradient-checkpointing, recomputation, gpu-memory

highreadme#2

Reposition the README's opening to clearly state the library's function and category

Why:

CURRENT

# pytorch-memonger

This is a re-implementation of Training Deep Nets with Sublinear Memory Cost. 
You may also want to have a look at the original mxnet implementation and 
OpenAI's tensorflow implementation.

COPY-PASTE FIX

# pytorch-memonger

A PyTorch library for sublinear memory optimization in deep learning, implementing gradient checkpointing/recomputation to reduce GPU memory cost. This is a re-implementation of Training Deep Nets with Sublinear Memory Cost. You may also want to have a look at the original mxnet implementation and OpenAI's tensorflow implementation.

mediumhomepage#3
Add the arXiv paper link as the repository homepage
Why:
COPY-PASTE FIX
```
https://arxiv.org/abs/1604.06174
```

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface Lyken17/pytorch-memonger

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

pytorch/pytorch

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

pytorch/pytorch · recommended 2×
Dao-AILab/flash-attention · recommended 2×
PyTorch · recommended 1×
TensorFlow · recommended 1×
DeepSpeed · recommended 1×

CATEGORY QUERY
How to reduce GPU memory consumption when training large deep learning models?
you: not recommended
AI recommended (in order):
1. PyTorch
2. TensorFlow
3. DeepSpeed
4. Megatron-LM
5. FlashAttention
6. bitsandbytes
AI recommended 6 alternatives but never named Lyken17/pytorch-memonger. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to optimize memory for training large sequential deep neural network models?
you: not recommended
AI recommended (in order):
1. PyTorch (pytorch/pytorch)
2. TensorFlow (tensorflow/tensorflow)
3. Mesh-TensorFlow (tensorflow/mesh)
4. Trax (google/trax)
5. DeepSpeed (microsoft/DeepSpeed)
6. FairScale (facebookresearch/fairscale)
7. Megatron-LM (NVIDIA/Megatron-LM)
8. FlashAttention (Dao-AILab/flash-attention)
9. FlashAttention-2 (Dao-AILab/flash-attention)
10. Longformer (allenai/longformer)
11. Reformer
12. Performer (google-research/performer)
13. PyTorch FSDP (pytorch/pytorch)
AI recommended 13 alternatives but never named Lyken17/pytorch-memonger. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of Lyken17/pytorch-memonger?
pass
AI named Lyken17/pytorch-memonger explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Lyken17/pytorch-memonger in production, what risks or prerequisites should they evaluate first?
pass
AI named Lyken17/pytorch-memonger explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo Lyken17/pytorch-memonger solve, and who is the primary audience?
pass
AI did not name Lyken17/pytorch-memonger — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of Lyken17/pytorch-memonger. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/Lyken17/pytorch-memonger.svg)](https://repogeo.com/en/r/Lyken17/pytorch-memonger)

HTML

<a href="https://repogeo.com/en/r/Lyken17/pytorch-memonger"><img src="https://repogeo.com/badge/Lyken17/pytorch-memonger.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

Lyken17/pytorch-memonger — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite