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SUMMARY:Scalable machine learning workloads with Ray AI Runtime - Emmy Li
 \, Adam Breindel
DTSTART;TZID=America/Chicago:20230710T133000
DTEND;TZID=America/Chicago:20230710T173000
DTSTAMP:20261007T043051Z
UID:pretalx-2023-7BRY3J@cfp.scipy.org
DESCRIPTION:Machine learning (ML) pipelines involve a variety of computati
 onally intensive stages. As state-of-the-art models and systems demand mor
 e compute\, there is an urgent need for adaptable tools to scale ML worklo
 ads. This idea drove the creation of Ray—an open source\, distributed ML
  compute framework that not only powers systems like ChatGPT but also push
 es theoretical computing benchmarks. Ray AIR is especially useful for para
 llelizing ML workloads such as pre-processing images\, model training and 
 finetuning\, and batch inference. In this tutorial\, participants will lea
 rn about AIR’s composable APIs through hands-on coding exercises.
LOCATION:Classroom 103
URL:https://cfp.scipy.org/2023/talk/7BRY3J/
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