Ready to watch?

To access this and all other video sessions from our past events create your FREE account today!

Already have an account? Login
Anirudh Koul - a few seconds ago
30 Golden Rules of Deep Learning Performance
expand_more
“Watching paint dry is faster than training my deep learning model.” “If only I had ten more GPUs, I could train my model in time.” “I want to run my model on a cheap smartphone, but it’s probably too heavy and slow.” If this sounds like you, then you might like this talk. Exploring the landscape of training and inference, we cover a myriad of tricks that step-by-step improve the efficiency of most deep learning pipelines, reduce wasted hardware cycles, and make them cost-effective. We identify and fix inefficiencies across different parts of the pipeline, including data preparation, reading and augmentation, training, and inference. With a data-driven approach and easy-to-replicate TensorFlow examples, finely tune the knobs of your deep learning pipeline to get the best out of your hardware. And with the money you save, demand a raise!
Featured jobs
supervisor_account Bosch-Gruppe Österreich
room Vienna, Austria
stars Junior
euro_symbol 39-70K
translate English
Embedded C
supervisor_account REWE Group Österreich
room Oberpremstätten, Austria
stars Intermediate
translate German
Linux
Structured Query Language (SQL)
supervisor_account DATA AHEAD AG
language Remote within DACH
room Nuremberg, Germany
stars Intermediate
translate German
Amazon Web Services (AWS)
Docker
Ansible
+2