mryab/efficient-dl-systems
A university course on efficient deep learning systems covering GPU optimization, distributed training, model serving, and inference acceleration.
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This repository contains lecture slides, seminars, and assignments for a graduate-level course on efficient deep learning systems taught at HSE University and Yandex School of Data Analysis. The syllabus covers CUDA programming, mixed-precision training, data-parallel and tensor-parallel training, FSDP, gradient checkpointing, model serving, and inference optimization using PyTorch. Materials include benchmarking exercises, profiling labs, and distributed training practicals.
Frequently asked
- What is mryab/efficient-dl-systems?
- A university course on efficient deep learning systems covering GPU optimization, distributed training, model serving, and inference acceleration.
- Is efficient-dl-systems open source?
- Yes — mryab/efficient-dl-systems is open source, released under the MIT license.
- What language is efficient-dl-systems written in?
- mryab/efficient-dl-systems is primarily written in Jupyter Notebook.
- How popular is efficient-dl-systems?
- mryab/efficient-dl-systems has 1k stars on GitHub.
- Where can I find efficient-dl-systems?
- mryab/efficient-dl-systems is on GitHub at https://github.com/mryab/efficient-dl-systems.