# C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA

- Institution: CERN
- Department: Conferences
- Speaker: Sitong An
- Year: 2021
- Language: English
- Subjects: Physics, Computer science, Earth and environment
- Source: [CERN](https://videos.cern.ch/record/3007914)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

We report the latest development in ROOT/TMVA, a new system that takes trained ONNX deep learning models and emits C++ code that can be easily included and invoked for fast inference of the model, with minimal dependency. We present an overview of the current solutions for conducting inference in C++ production environment, discuss the technical details and examples of the generated code, and demonstrates its development status with a preliminary benchmark against popular tools..

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## Lectures

1. [C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA](https://invisiblenetwork.ai/learn/cern/3007914/ab05d9017a5d), 13 min (CERN's player)