Distributed Training on Multi-Node Multi-GPU of Deep Neural Networks

Abstract: In the last year there have been a number of attempts to train deep CNNs on the ImageNet dataset in the shortest time possible, with the most recent attempt managing to do it in 15 minutes. All of these attempts happen on custom clusters which are out of the reach of most data scientists.

One of the key advantages of the cloud is being able to scale out compute resources as required. In this talk we will present two platforms for running distributed deep learning in the cloud which are within the reach of every data scientist. The first is a service called Batch AI which uses the Azure Batch infrastructure to easily run Deep Learning jobs at scale across GPUs. The second is an open source toolkit that allows data scientists to spin up clusters in turn-key fashion. It utilises Kubernetes and Grafana for easy job scheduling and monitoring. It has been used in daily production for Microsoft internal groups. Both utilise Docker containers making it possible to run any deep learning framework on them. We will use the aforementioned training platforms to train a ResNet network on ImageNet dataset using each of the following frameworks: CNTK, Tensorflow (Horovod), PyTorch, MxNet and Chainer. We will then compare and contrast the performance improvement as we scale the number of nodes as well as provides tips and details of the pitfalls of each framework and platform. The examples presented can also be used as templates so that others can utilise these for their own deep learning problems.

Bio: Miguel González-Fierro is a Data Scientist at Microsoft UK, where his job consists of helping customers leverage their processes using Big Data and Machine Learning. Previously, he was CEO and founder of Samsamia Technologies, a company that created a visual search engine for fashion items allowing users to find products using images instead of words, and founder of the Robotics Society of Universidad Carlos III, which developed different projects related to UAVs, mobile robots, small humanoids competitions, and 3D printers. Miguel also worked as a robotics scientist at Universidad Carlos III of Madrid and King’s College London, where his research focused on learning from demonstration, reinforcement learning, computer vision, and dynamic control of humanoid robots. He holds a BSc and MSc in Electrical Engineering and an MSc and PhD in robotics.

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