RRepoGEO

REPOGEO REPORT · LITE

determined-ai/determined

Default branch main · commit c1e9c6d7 · scanned 6/28/2026, 9:11:50 PM

GitHub: 3,223 stars · 371 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
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 determined-ai/determined, 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's opening paragraph to emphasize 'unified platform'

    Why:

    CURRENT
    Determined is an all-in-one deep learning platform, compatible with PyTorch and TensorFlow. It takes care of: Distributed training for faster results. Hyperparameter tuning for obtaining the best models. Resource management for cutting cloud GPU costs. Experiment tracking for analysis and reproducibility.
    COPY-PASTE FIX
    Determined is a unified deep learning platform that streamlines the entire deep learning lifecycle, from distributed training and hyperparameter tuning to experiment tracking and GPU resource management. It provides a single, integrated solution for ML engineers and data scientists working with PyTorch and TensorFlow, eliminating the complexity of stitching together multiple tools.
  • mediumtopics#2
    Add more specific platform and management-oriented topics

    Why:

    CURRENT
    data-science, deep-learning, distributed-training, hyperparameter-optimization, hyperparameter-search, hyperparameter-tuning, keras, kubernetes, machine-learning, ml-infrastructure, ml-platform, mlops, pytorch, tensorflow
    COPY-PASTE FIX
    data-science, deep-learning, distributed-training, hyperparameter-optimization, hyperparameter-search, hyperparameter-tuning, keras, kubernetes, machine-learning, ml-infrastructure, ml-platform, mlops, pytorch, tensorflow, deep-learning-platform, ml-orchestration, gpu-management, experiment-management
  • lowreadme#3
    Add a 'Why Determined?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why Determined?' or 'How Determined Compares' that briefly explains its integrated approach versus using separate tools for distributed training, hyperparameter tuning, and experiment tracking.

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 determined-ai/determined
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch Lightning
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Lightning · recommended 1×
  2. Ray Tune · recommended 1×
  3. Hugging Face Accelerate · recommended 1×
  4. Weights & Biases (W&B) Sweeps · recommended 1×
  5. Optuna · recommended 1×
  • CATEGORY QUERY
    How to simplify distributed deep learning training and hyperparameter tuning with PyTorch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. Ray Tune
    3. Hugging Face Accelerate
    4. Weights & Biases (W&B) Sweeps
    5. Optuna
    6. DeepSpeed

    AI recommended 6 alternatives but never named determined-ai/determined. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source platform helps manage ML experiments, track runs, and optimize GPU utilization?
    you: not recommended
    AI recommended (in order):
    1. MLflow
    2. Weights & Biases (W&B)
    3. ClearML
    4. Kubeflow
    5. Neptune.ai

    AI recommended 5 alternatives but never named determined-ai/determined. 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 determined-ai/determined?
    pass
    AI named determined-ai/determined explicitly

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

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

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

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determined-ai/determined — 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