REPOGEO REPORT · LITE

flexflow/flexflow-train

Default branch master · commit e2f2b8f2 · scanned 5/21/2026, 5:27:27 AM

GitHub: 1,879 stars · 251 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

35 /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

3 / 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 flexflow/flexflow-train, 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

distributed-deep-learning, dnn-training, parallelization-strategies, deep-learning-framework, automatic-optimization, hpc, machine-learning-acceleration, distributed-ml

mediumreadme#2

Elevate the core value proposition in the README's introduction

Why:

CURRENT

> [!WARNING]
> The FlexFlow repository has been split into separate flexflow-train and flexflow-serve repositories.
> You are currently viewing flexflow-train. 
> For anything inference/serving-related, go to flexflow-serve. 

FlexFlow Train is a deep learning framework that accelerates distributed DNN training by automatically searching for efficient parallelization strategies.

COPY-PASTE FIX

FlexFlow Train is a deep learning framework that accelerates distributed DNN training by automatically searching for efficient parallelization strategies.

> [!WARNING]
> This repository focuses on the training component of the FlexFlow project. For inference/serving, please refer to the [flexflow-serve repository](https://github.com/flexflow/flexflow-serve).

lowreadme#3
Add a prominent link to the official project homepage in the README
Why:
COPY-PASTE FIX
```
For more information about the FlexFlow project, visit our official website: [flexflow.ai](https://flexflow.ai)
```

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 flexflow/flexflow-train

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

DeepSpeed

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

DeepSpeed · recommended 2×
PyTorch Distributed · recommended 1×
TensorFlow Distributed · recommended 1×
Horovod · recommended 1×
Ray Train · recommended 1×

CATEGORY QUERY
What are frameworks for accelerating distributed deep neural network training?
you: not recommended
AI recommended (in order):
1. PyTorch Distributed
2. TensorFlow Distributed
3. Horovod
4. DeepSpeed
5. Ray Train
6. PaddlePaddle Distributed
7. MXNet Distributed
AI recommended 7 alternatives but never named flexflow/flexflow-train. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to automatically discover efficient parallelization strategies for distributed DNNs?
you: not recommended
AI recommended (in order):
1. DeepSpeed
2. Megatron-LM
3. Alpa
4. FlexFlow
5. Ray
6. PyTorch FSDP
7. TensorFlow
AI recommended 7 alternatives but never named flexflow/flexflow-train. 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 flexflow/flexflow-train?
pass
AI named flexflow/flexflow-train explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts flexflow/flexflow-train in production, what risks or prerequisites should they evaluate first?
pass
AI named flexflow/flexflow-train 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 flexflow/flexflow-train solve, and who is the primary audience?
pass
AI named flexflow/flexflow-train explicitly
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 flexflow/flexflow-train. 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/flexflow/flexflow-train.svg)](https://repogeo.com/en/r/flexflow/flexflow-train)

HTML

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

Pro

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flexflow/flexflow-train — 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