REPOGEO REPORT · LITE

mryab/efficient-dl-systems

Default branch main · commit e632aa89 · scanned 5/31/2026, 9:58:10 PM

GitHub: 994 stars · 149 forks

AI VISIBILITY SCORE

28 /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

2 / 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 mryab/efficient-dl-systems, 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

Clarify README opening to emphasize educational value for optimization

Why:

CURRENT

This repository contains materials for the Efficient Deep Learning Systems course, taught at the Faculty of Computer Science of HSE University and Yandex School of Data Analysis.

COPY-PASTE FIX

This repository contains comprehensive materials for the Efficient Deep Learning Systems course, taught at the Faculty of Computer Science of HSE University and Yandex School of Data Analysis. It serves as a practical educational resource for students and practitioners to master advanced techniques for optimizing deep learning model training and inference, and understanding system-level considerations.

mediumhomepage#2
Add a homepage URL to a rendered course site
Why:
COPY-PASTE FIX
```
https://mryab.github.io/efficient-dl-systems/
```

lowtopics#3

Add educational-specific topics

Why:

CURRENT

cuda, deep-learning, distributed-training, efficient-deep-learning, inference-optimization, machine-learning, ml-infrastructure, ml-systems, mlops, performance-optimization, pytorch

COPY-PASTE FIX

cuda, deep-learning, distributed-training, efficient-deep-learning, inference-optimization, machine-learning, ml-infrastructure, ml-systems, mlops, performance-optimization, pytorch, education, course-materials, deep-learning-course, ml-education

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 mryab/efficient-dl-systems

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

NVIDIA CUDA Toolkit

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

NVIDIA CUDA Toolkit · recommended 1×
cuDNN · recommended 1×
NVIDIA TensorRT · recommended 1×
pytorch/pytorch · recommended 1×
tensorflow/tensorflow · recommended 1×

CATEGORY QUERY
How to optimize deep learning model training and inference for better GPU performance?
you: not recommended
AI recommended (in order):
1. NVIDIA CUDA Toolkit
2. cuDNN
3. NVIDIA TensorRT
4. PyTorch (pytorch/pytorch)
5. TensorFlow (tensorflow/tensorflow)
6. ONNX Runtime (microsoft/onnxruntime)
7. NVIDIA DALI (NVIDIA/DALI)
8. DeepSpeed (microsoft/DeepSpeed)
9. FairScale (facebookresearch/fairscale)
AI recommended 9 alternatives but never named mryab/efficient-dl-systems. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are effective methods for distributed deep learning training and system-level optimizations?
you: not recommended
AI recommended (in order):
1. PyTorch DistributedDataParallel (DDP)
2. NCCL (NVIDIA Collective Communications Library)
3. Gloo
4. TensorFlow Distributed Strategy API
5. MirroredStrategy
6. MultiWorkerMirroredStrategy
7. Horovod
8. DeepSpeed (Microsoft)
9. Megatron-LM (NVIDIA)
10. FairScale (Facebook AI Research)
11. NVIDIA Apex
12. ONNX Runtime
13. Ray (with Ray Train/Ray Tune)
AI recommended 13 alternatives but never named mryab/efficient-dl-systems. 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 mryab/efficient-dl-systems?
pass
AI named mryab/efficient-dl-systems explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts mryab/efficient-dl-systems in production, what risks or prerequisites should they evaluate first?
pass
AI named mryab/efficient-dl-systems 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 mryab/efficient-dl-systems solve, and who is the primary audience?
pass
AI did not name mryab/efficient-dl-systems — 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?

Embed your GEO score

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MARKDOWN (README)

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HTML

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Brand-free category queries5 vs 2 in Lite
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