September 18-19, 2024 San Francisco, California View More Details & Registration Note: The schedule is subject to change.
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In this talk we'll peek into Modular's inference engine: how it builds on and works with PyTorch and what is unique about it. We will look into how Mojo language can be used to define performant kernels and what optimizations the inference engine can perform. We will also talk briefly about our experience of developing a third party backend for torch.compile.
Mikhail is an open source enthusiast with contributions ranging from GCC and LLVM to PyTorch. Currently he is at Modular leading a team working on integration of Modular's inference stack with PyTorch.