[PyData] Distributed deep learning and why you may not need it - Jakub Sanojca, Mikuláš Zelinka

PyData Warsaw 2018 Deep learning thrives with always bigger networks and always growing datasets but single machine can only handle so much. When to scale to multiple machines and how do do it efficiently? What pros and cons available options have and what is theory behind their approach to distributed training? In this talk we will answer those questions and show what problems we are trying to solve at Avast. === www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.