RRepoGEO

REPOGEO REPORT · LITE

uber/petastorm

Default branch master · commit 01ba9cb7 · scanned 5/22/2026, 10:32:20 PM

GitHub: 1,889 stars · 283 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
84 /100
Healthy
Category recall
2 / 2
Avg rank #2.0 when recommended
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 uber/petastorm, 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
  • highhomepage#1
    Add homepage URL to repository About section

    Why:

    COPY-PASTE FIX
    https://petastorm.readthedocs.io
  • mediumreadme#2
    Refine README's opening sentence to emphasize deep learning focus

    Why:

    CURRENT
    Petastorm is an open source data access library developed at Uber ATG.
    COPY-PASTE FIX
    Petastorm is an open-source data access library for deep learning, developed at Uber ATG.
  • mediumtopics#3
    Add more specific topics to highlight distributed training and data loading

    Why:

    CURRENT
    deep-learning, machine-learning, parquet, parquet-files, pyarrow, pyspark, pytorch, sysml, tensorflow
    COPY-PASTE FIX
    deep-learning, machine-learning, parquet, parquet-files, pyarrow, pyspark, pytorch, sysml, tensorflow, distributed-training, data-loading

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
2 / 2
100% of queries surface uber/petastorm
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
10%
Of all named tools, what % are you?
Top rival
tf.data.experimental.make_parquet_dataset
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tf.data.experimental.make_parquet_dataset · recommended 2×
  2. Apache Spark · recommended 1×
  3. PySpark · recommended 1×
  4. dask/dask · recommended 1×
  5. Dask DataFrames · recommended 1×
  • CATEGORY QUERY
    How to efficiently load large Parquet datasets for deep learning model training?
    you: #3
    AI recommended (in order):
    1. Apache Spark
    2. PySpark
    3. Petastorm (uber/petastorm) ← you
    4. Dask (dask/dask)
    5. Dask DataFrames
    6. Pandas (pandas-dev/pandas)
    7. PyArrow (apache/arrow)
    8. fastparquet (dask/fastparquet)
    9. Hugging Face Datasets library (huggingface/datasets)
    10. tf.data.experimental.make_parquet_dataset
    11. torch.utils.data.Dataset
    Show full AI answer
  • CATEGORY QUERY
    Tool for distributed deep learning training directly from Apache Parquet files?
    you: #1
    AI recommended (in order):
    1. Petastorm ← you
    2. Dask
    3. Dask-ML
    4. PyTorch FSDP
    5. PyTorch
    6. TensorFlow
    7. tf.data.experimental.make_parquet_dataset
    8. Ray Data
    9. Ray Train
    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 uber/petastorm?
    pass
    AI named uber/petastorm explicitly

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

  • If a team adopts uber/petastorm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named uber/petastorm 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 uber/petastorm solve, and who is the primary audience?
    pass
    AI named uber/petastorm 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 uber/petastorm. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/uber/petastorm"><img src="https://repogeo.com/badge/uber/petastorm.svg" alt="RepoGEO" /></a>
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uber/petastorm — 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