The Uncertain Art of Accelerating ML Models with Sylvain Gugger

About this episode

Sylvain Gugger discusses the intricacies of accelerating machine learning models in trading environments.

Sylvain Gugger is a former math teacher who fell into machine learning via a MOOC and became an expert in the low-level performance details of neural networks. He’s now on the ML infrastructure team at Jane Street, where he helps traders speed up their models. In this episode, Sylvain and Ron go deep on learning rate schedules; the subtle performance bugs PyTorch lets you write; how to keep a hungry GPU well-fed; and lots more, including the foremost importance of reproducibility in training runs. They also discuss some of the unique challenges of doing ML in the world of trading, like the unusual size and shape of market data and the need to do inference at shockingly low latencies.

People in this episode

Host: Ron

Guest: Sylvain Gugger

Topics covered

• machine learning
• neural networks
• trading
• performance optimization
• reproducibility
• inference

Keywords

• machine learning
• neural networks
• learning rate schedules
• PyTorch
• GPU performance
• trading data
• latency

Mentioned in this episode

Organizations: Jane Street

Products: PyTorch

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