Horovod Opplæringskurs

Horovod Opplæringskurs

Online eller på stedet, instruktørledede live Horovod-treningskurs demonstrerer gjennom interaktiv praktisk praksis hvordan du bruker Horovod til å kjøre distribuert dyplæringstrening og skalere den opp til å kjøre på tvers av flere GPUer parallelt. Horovod-trening er tilgjengelig som "online live training" eller "onsite live training". Online live trening (også kalt "remote live training") utføres ved hjelp av et interaktivt, eksternt skrivebord . Livetrening på stedet kan gjennomføres lokalt hos kundene i Norge eller i NobleProg bedriftsopplæringssentre i Norge. NobleProg – din lokale opplæringsleverandør

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Horovod Kursplaner

Kursnavn
Varighet
Oversikt
Kursnavn
Varighet
Oversikt
7 timer
Horovod is an open source software framework, designed for processing fast and efficient distributed deep learning models using TensorFlow, Keras, PyTorch, and Apache MXNet. It can scale up a single-GPU training script to run on multiple GPUs or hosts with minimal code changes. This instructor-led, live training (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel. By the end of this training, participants will be able to:
  • Set up the necessary development environment to start running deep learning trainings.
  • Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
  • Scale deep learning training with Horovod to run on multiple GPUs.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • This course is focused on Horovod, but other software tools and frameworks such as TensorFlow, Keras, PyTorch, and Apache MXNet may be required. Please let us know if you have specific requirements or preferences.
  • To request a customized training for this course, please contact us to arrange.

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