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The Uncertain Art of Accelerating ML Models with Sylvain Gugger

The Uncertain Art of Accelerating ML Models with Sylvain Gugger

Signals and Threads vor 2 Jahren

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1:06:22

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.

You can find the transcript for this episode on our
website.

Some links to topics that came up in the discussion:

“Practical Deep Learning for Coders,” a FastAI MOOC by Jeremy
Howard, and the book, of which Sylvain is a co-author.

The Stanford DAWNBench competition that Sylvain participated
in.

HuggingFace, and the Accelerate library that Sylvain wrote
there.

Some of the languages/systems for expression ML models that
were discussed: PyTorch, TensorFlow, Jax, Mojo, and Triton

CUDA graphs and streams

Hogwild concurrency