# mryab/efficient-dl-systems

Efficient Deep Learning Systems course materials

Repository: https://github.com/mryab/efficient-dl-systems
Canonical: https://ross.abutalabs.com/products/efficient-dl-systems
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: deep-learning, efficient-deep-learning, pytorch, cuda, distributed-training, machine-learning, ml-infrastructure, mlops, inference-optimization, ml-systems, performance-optimization
Last push: 2026-05-28T15:00:14+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 84, release rhythm 35, longevity 100
- inputs: {"age_days": 1732, "days_push": 97, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1028, forks 149 (observed 2026-08-28T04:03:17.440900+00:00)

## What it is
Course materials for the Efficient Deep Learning Systems course taught at HSE University and Yandex School of Data Analysis. It covers GPU/CUDA fundamentals, profiling, distributed training (data parallelism, FSDP, tensor/pipeline parallelism), and LLM inference optimization through lectures and hands-on Jupyter notebook seminars.

## Use cases
- learn distributed deep learning training
- understand FSDP and tensor parallelism
- optimize LLM inference performance
- learn CUDA and GPU programming with PyTorch
- profile and speed up deep learning training
- study quantization and speculative decoding
- learn mixed precision training

## When to choose
- you want structured, university-level course materials on ML systems
- you need hands-on notebooks covering PyTorch distributed training and profiling
- you are preparing to train or serve large models efficiently

## When to avoid
- you need a production library or tool rather than educational materials
- you want a beginner introduction to deep learning rather than systems optimization
- you need framework-agnostic content beyond PyTorch

## Facets
- artifact type: learning-resource
- maturity: active
- function: deep-learning, llm-inference, llm-training, benchmarking, gpu-computing
- domain: deep-learning, machine-learning, gpu-computing, tutorials, performance
- platform: python, cross-platform
- tags: distributed-training, cuda, pytorch, mlops, inference-optimization, course-materials, jupyter-notebooks, gpu

## Member repositories
- mryab/efficient-dl-systems (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:17.440900+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:07:48.262511+00:00, confidence not recorded.
  - readme: https://github.com/mryab/efficient-dl-systems (fetched 2026-08-28T04:03:17.440900+00:00, sha 14527afcc203)
- Data as of 2026-08-30T08:39:29.467469+00:00.