10:00 - 11:00
The dominant programming language for deep learning is Python. It has a wide variety of frameworks and data scientists love it due to its ecosystem and the workflows it allows. Yet when it comes to actually taking models to production, it is usually met with resistance, as in many enterprise environments Java is still king of the hill – and rightly so. It is the underpinning of big data infrastructure, provides better tooling for production monitoring and scales better to larger teams.
Deeplearning4J is both the name of a deep learning library, but also the umbrella for a set of libraries aimed at the production usage of deep learning. Those libraries cover everything from loading data from a variety of data sources, over defining and training your model on a single node with a CPU or GPU or in a distributed environment on a Cluster, to running it in production, and even importing already trained models from Keras and Tensorflow.
This talk will introduce the Deeplearning4J ecosystem with a brief overview of the most important libraries, including ND4J, DataVec, Deeplearning4J itself, RL4J (reinforcement learning) and Arbiter (hyperparameter optimization). The overview of the ecosystem is followed by a short history of Deeplearning4J and a look into the near future. Finally, a demonstration of how all of them are used together is given.