Choosing the correct hardware for deep learning is a complicated process. There are many factors to consider including processing units, memory, storage, costs, and performance. A “one model size fits all” approach is also not optimal.
In this webinar and Q&A session with Lucy Kadets and Nave Assaf, you’ll learn how to:
- Select inference hardware specific to your computer vision task and application
- Overcome the complexity of compiling models for the production environment
- Simplify benchmarking models on various hardware – even without owning the hardware
Watch now and book a demo to try out Deci’s benchmarking tool.