An Introduction to Apache TVM
07-13, 10:45–11:30 (Europe/Dublin), Liffey Hall 2

Apache TVM is an open source machine learning compiler framework for CPUs, GPUs, and machine learning accelerators. It aims to enable machine learning engineers to optimize and run computations efficiently on any hardware backend.

This talk will present an introduction to Apache TVM using its Python API, and demonstrated using examples of deep learning models being execute in CPUs and Microcontrollers.


This talk will present an introduction to Apache TVM using its Python API, and will include a demonstration using examples of deep learning models being executed in CPUs and Microcontrollers.
Apache TVM is a very flexible compilation stack for deep learning models, supporting many input formats such as TensorFlow, TFLite, Keras, PyTorch, ONNX, etc. as well as many target hardware like CPUs, GPUs and neural networks accelerators.

This talk will present a walkthrough of TVM Python API from installation to usage, demonstrating its features using a series of quick practical projects.

The high-level agenda is:

  • TVM in a nutshell (a brief description of what is TVM)
  • How to install
  • Introduction to TVM Python API
  • Practical demos: Compiling and tuning a model
  • Compiling and running a model on an embedded target
  • Final Remarks

Expected audience expertise: Python

some

Expected audience expertise: Domain

some

Abstract as a tweet

This talk will present an introduction to the Apache TVM compiler stack.

I'm a software engineer, currently working on compilation tools for machine learning workloads and contributing to the Apache TVM Compiler Stack as a PMC member and committer. In the academic background, I hold a M.S. degree in Microelectronics and a B.S. degree in Computer Science.