Deep learning model performance is known for scaling well with data size, but training these models can be notoriously time-consuming. As more companies adopt deep learning, the need for using distributed deep learning frameworks becomes more important than ever.
In this webinar, we’ll share:
- How distributed deep learning works and give you an overview of the different frameworks including TensorFlow, Keras and Pytorch.
- How Databricks is making it easy for data scientists to migrate their single-machine workloads to distributed workloads, at all stages of a deep learning project.
- A demo of distributed deep learning training using our newly released feature, HorovodRunner.
Yifan Cao, Senior Product Manager, Databricks
Date: Tuesday, February 12, 2019
Time: 10am PT
The Databricks Team