Webinar: How to Select the Optimal Hardware for Your Computer Vision Task

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.

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")