mryab/efficient-dl-systems resource

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1028 stars · 149 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

learning-resource · maturity active

deep-learning llm-inference llm-training benchmarking gpu-computing deep-learning machine-learning gpu-computing tutorials performance python cross-platform distributed-training cuda pytorch mlops inference-optimization course-materials jupyter-notebooks gpu

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