RRepoGEO

REPOGEO REPORT · LITE

deepseek-ai/profile-data

Default branch main · commit 44960242 · scanned 6/23/2026, 12:43:03 PM

GitHub: 1,170 stars · 148 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
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 deepseek-ai/profile-data, 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
  • highabout#1
    Clarify the repository description to specify it's profiling data

    Why:

    CURRENT
    Analyze computation-communication overlap in V3/R1.
    COPY-PASTE FIX
    Publicly shared PyTorch profiling data demonstrating computation-communication overlap strategies in DeepSeek-V3/R1 distributed training and inference.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    ["profiling-data", "deep-learning-profiling", "pytorch-profiler", "distributed-training", "moe", "computation-communication-overlap", "deepseek-v3"]
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT or Apache-2.0) to clarify usage rights for the profiling data.

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 deepseek-ai/profile-data
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA Nsight Systems
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Nsight Systems · recommended 2×
  2. tensorflow/tensorboard · recommended 2×
  3. PyTorch Profiler · recommended 1×
  4. TensorFlow Profiler · recommended 1×
  5. Intel VTune Profiler · recommended 1×
  • CATEGORY QUERY
    How to visualize computation and communication overlap in distributed AI model training?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Nsight Systems
    2. PyTorch Profiler
    3. TensorBoard (tensorflow/tensorboard)
    4. TensorFlow Profiler
    5. Intel VTune Profiler
    6. Horovod Timeline
    7. Open MPI (open-mpi/ompi)
    8. Intel MPI
    9. MVAPICH2 (mvapich/mvapich2)
    10. VampirTrace
    11. Score-P (score-p/scorep)

    AI recommended 11 alternatives but never named deepseek-ai/profile-data. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find example profiling data for large scale distributed deep learning?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Nsight Systems
    2. NVIDIA Nsight Compute
    3. TensorFlow Profiler (tensorflow/tensorflow)
    4. TensorBoard (tensorflow/tensorboard)
    5. PyTorch Profiler (pytorch/pytorch)
    6. FairScale (facebookresearch/fairscale)
    7. DeepSpeed (microsoft/DeepSpeed)
    8. Hugging Face Accelerate (huggingface/accelerate)
    9. Transformers Library (huggingface/transformers)

    AI recommended 9 alternatives but never named deepseek-ai/profile-data. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 deepseek-ai/profile-data?
    pass
    AI did not name deepseek-ai/profile-data — 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?

  • If a team adopts deepseek-ai/profile-data in production, what risks or prerequisites should they evaluate first?
    pass
    AI named deepseek-ai/profile-data 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 deepseek-ai/profile-data solve, and who is the primary audience?
    pass
    AI did not name deepseek-ai/profile-data — 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?

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deepseek-ai/profile-data — 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