RRepoGEO

REPOGEO REPORT · LITE

meta-pytorch/torchft

Default branch main · commit 4157be16 · scanned 6/14/2026, 3:56:45 PM

GitHub: 511 stars · 69 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 meta-pytorch/torchft, 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
  • highreadme#1
    Reposition the README opening to clarify its role as a fault tolerance primitive library

    Why:

    CURRENT
    This repository implements techniques for doing a per-step fault tolerance so you can keep training if errors occur without interrupting the entire training job.
    COPY-PASTE FIX
    torchft is a lightweight, modular library providing **per-step fault tolerance primitives** for PyTorch. It enables you to integrate resilience into *existing* distributed training setups, ensuring your large-scale models can continue training even if errors occur, without requiring a full framework overhaul.
  • highabout#2
    Refine the 'About' description to emphasize its role as a library of primitives

    Why:

    CURRENT
    Fault tolerance for PyTorch (HSDP, LocalSGD, DiLoCo, Streaming DiLoCo)
    COPY-PASTE FIX
    A PyTorch library providing modular, per-step fault tolerance primitives (HSDP, LocalSGD, DiLoCo, Streaming DiLoCo) for resilient distributed training.
  • mediumlicense#3
    Add a section to the README clarifying the specific license(s)

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [License Name 1] and [License Name 2]. Please refer to the [LICENSE file](LICENSE) for full details.

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 meta-pytorch/torchft
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 6 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 6×
  2. huggingface/accelerate · recommended 2×
  3. ray-project/ray · recommended 2×
  4. Lightning-AI/lightning · recommended 1×
  5. microsoft/DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How to make PyTorch distributed training resilient to node failures?
    you: not recommended
    AI recommended (in order):
    1. PyTorch FSDP (pytorch/pytorch)
    2. PyTorch Lightning (Lightning-AI/lightning)
    3. Hugging Face Accelerate (huggingface/accelerate)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. PyTorch DDP (pytorch/pytorch)
    6. Ray Train (ray-project/ray)
    7. TorchElastic (pytorch/pytorch)

    AI recommended 7 alternatives but never named meta-pytorch/torchft. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a library to implement per-step fault tolerance for large-scale PyTorch models.
    you: not recommended
    AI recommended (in order):
    1. PyTorch FSDP (pytorch/pytorch)
    2. DeepSpeed (microsoft/deepspeed)
    3. FairScale (facebookresearch/fairscale)
    4. Hugging Face Accelerate (huggingface/accelerate)
    5. PyTorch DDP (pytorch/pytorch)
    6. Ray Train (ray-project/ray)
    7. TorchElastic (pytorch/pytorch)

    AI recommended 7 alternatives but never named meta-pytorch/torchft. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 meta-pytorch/torchft?
    pass
    AI named meta-pytorch/torchft explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts meta-pytorch/torchft in production, what risks or prerequisites should they evaluate first?
    pass
    AI named meta-pytorch/torchft 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 meta-pytorch/torchft solve, and who is the primary audience?
    pass
    AI named meta-pytorch/torchft explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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