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.
This branch corresponds to the ongoing 2023 course. If you want to see full materials of past years, see the "Past versions" section.
- Week 1: Introduction
- Lecture: Course overview and organizational details. Core concepts of the GPU architecture and CUDA API.
- Seminar: CUDA operations in PyTorch. Introduction to benchmarking.
- Week 2: Experiment tracking, model and data versioning, testing DL code in Python
- Lecture: Experiment management basics and pipeline versioning. Configuring Python applications. Intro to regular and property-based testing.
- Seminar: Example DVC+W&B project walkthrough. Intro to testing with pytest.
- Week 3: Training optimizations, profiling DL code
- Lecture: Mixed-precision training. Data storage and loading optimizations. Tools for profiling deep learning workloads.
- Seminar: Automatic Mixed Precision in PyTorch. Dynamic padding for sequence data and JPEG decoding benchmarks. Basics of PyTorch Profiler and cProfile.
- Week 4: Basics of distributed ML
- Lecture: Introduction to distributed training. Process-based communication. Parameter Server architecture.
- Seminar: Multiprocessing basics. Parallel GloVe training.
- Week 5: Data-parallel training and All-Reduce
- Lecture: Data-parallel training of neural networks. All-Reduce and its efficient implementations.
- Seminar: Introduction to PyTorch Distributed. Data-parallel training primitives.
- Week 6: Memory-efficient and model-parallel training
- Lecture: Model-parallel training, gradient checkpointing, offloading.
- Seminar: Gradient checkpointing in practice.
- Week 7: Python web application deployment
- Lecture/Seminar: Building and deployment of production-ready web services. App & web servers, Docker, Prometheus, API via HTTP and gRPC.
- Week 8: Software for serving neural networks
- Lecture/Seminar: Different formats for packing NN: ONNX, TorchScript, IR. Inference servers: OpenVINO, Triton. ML on client devices: TfJS, ML Kit, Core ML.
- Week 9: Optimizing models for faster inference
- Lecture: Knowledge distillation, Pruning, Quantization, NAS, Efficient Architectures.
- Seminar: Quantization and distillation in practice.
- Week 10: Invited talks (speakers TBA)
There will be several home assignments (spread over multiple weeks) on the following topics:
- Training pipelines and code profiling
- Distributed and memory-efficient training
- Deploying and optimizing models for production
The final grade is a weighted sum of per-assignment grades. Please refer to the course page of your institution for details.