Taming the Deep Learning Workflow
Despite enormous excitement about the potential of deep learning, building practical applications powered by deep learning remains an enormous challenge: the necessary expertise is scarce, the hardware requirements can be prohibitive, and current software tools are immature and limited in scope. In this talk, we will first describe how deep learning workflows are supported by existing software tooling. We will then describe several promising opportunities to drastically improve these workflows via novel algorithmic and software solutions, including application aware GPU cluster management and state of the art resource-aware hyperparameter optimization methods. This talk draws on academic work done at CMU, UC Berkeley, and UCLA, as well as our experiences at Determined AI, a startup that builds software to make deep learning engineers dramatically more productive.
Neil Conway is co-founder and CTO of Determined AI, a startup that builds software to dramatically accelerate deep learning model development. Neil was previously a technical lead at Mesosphere and a major developer of both Apache Mesos and Postgres. Neil earned a PhD in Computer Science at UC Berkeley, where he helped design Bloom, a new programming language for building distributed systems.