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


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.


Mathew Salvaris is a Data Scientist at Microsoft. Previously, Mathew was a Data Scientist for a small startup that provided analytics for fund managers and a postdoctoral researcher at UCL’s Institute of Cognitive Neuroscience, where he worked with Patrick Haggard in the area of volition and free will, devising models to decode human decisions in real time from the motor cortex using electroencephalography (EEG). He also held a postdoctoral position in the University of Essex’s Brain Computer Interface Group, where he worked on BCIs for computer mouse control. Mathew holds a PhD in brain computer interfaces and an MSc in distributed artificial intelligence.

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