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---
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title: "Deep Learning Goes to the Dogs"
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date: "2016-11-10"
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categories:
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- "blogs"
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tags:
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- "featured"
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---
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_By Indrajit Poddar, Yu Bo Li, Qing Wang, Jun Song Wang, IBM_
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These days you can see machine and deep learning applications in so many places. Get driven by a [driverless car](http://www.bloomberg.com/news/features/2016-08-18/uber-s-first-self-driving-fleet-arrives-in-pittsburgh-this-month-is06r7on). Check if your email is really conveying your sense of joy with the [IBM Watson Tone Analyzer](https://tone-analyzer-demo.mybluemix.net/), and [see IBM Watson beat the best Jeopardy player](https://www.youtube.com/watch?v=P0Obm0DBvwI) in the world in speed and accuracy. Facebook is even using image recognition tools to suggest tagging people in your photos; it knows who they are!
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## Barking Up the Right Tree with the IBM S822LC for HPC
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We wanted to see what it would take to get started building our very own deep learning application and host it in a cloud. We used the open source deep learning framework, [Caffe](http://caffe.berkeleyvision.org/), and example classification Jupyter notebooks from GitHub, like [classifying with ImageNet](http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb). We found several published trained models, e.g. GoogLeNet from the [Caffe model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo). For a problem, we decided to use dog breed classification. That is, given a picture of a dog, can we automatically identify the breed? This is actually a [class project](http://cs231n.stanford.edu/) from Stanford University with student reports, such as [this one](http://cs231n.stanford.edu/reports/fcdh_FinalReport.pdf) from David Hsu.
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We started from the [GoogLeNet model](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet) and created our own model trained on the [Stanford Dogs Dataset](http://vision.stanford.edu/aditya86/ImageNetDogs/) using a system similar to the [IBM S822LC for HPC systems with NVIDIA Tesla P100 GPUs](https://blogs.nvidia.com/blog/2016/09/08/ibm-servers-nvlink/) connected to the CPU with NVIDIA NVLink. As David remarked in his report, without GPUs, it takes a very long time to train a deep learning model on even a small-sized dataset.
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Using a previous generation IBM S822LC OpenPOWER system with a NVIDIA Tesla K80 GPU, we were able to train our model in only a few hours. The [IBM S822LC for HPC systems](http://www-03.ibm.com/systems/power/hardware/s822lc-hpc/) not only features the most powerful NVIDIA Tesla P100 GPUs, but also two IBM POWER8 processors interconnected with powerful [NVIDIA NVLink adapters](https://en.wikipedia.org/wiki/NVLink). These systems make data transfers between main memory and GPUs significantly faster compared to systems with PCIe interconnects.
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## Doggy Docker for Deep Learning
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We put [our Caffe model and our classification code](https://github.com/Junsong-Wang/pet-breed) written in Python into a web application inside a Docker container and deployed it with Apache Mesos and Marathon. Apache Mesos is an open source cluster management application with fine-grained resource scheduling features which now recognize [GPUs](http://www.nvidia.com/object/apache-mesos.html) as cluster-wide resources.
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In addition to Apache Mesos, it is possible to run cluster managers, such as Kubernetes, Spectrum Conductor for Containers, and Docker GPU management components, such as [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) on OpenPOWER systems (see [presentation](http://www.slideshare.net/IndrajitPoddar/enabling-cognitive-workloads-on-the-cloud-gpus-with-mesos-docker-and-marathon-on-power)). In addition to Caffe, it is possible to run other [popular deep learning frameworks and tools](https://openpowerfoundation.org/blogs/deep-learning-options-on-openpower/) such as Torch, Theano, DIGITS and [TensorFlow](https://www.ibm.com/developerworks/community/blogs/fe313521-2e95-46f2-817d-44a4f27eba32/entry/Building_TensorFlow_on_OpenPOWER_Linux_Systems?lang=en) on OpenPOWER systems.
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This [lab tutorial](http://www.slideshare.net/IndrajitPoddar/fast-scalable-easy-machine-learning-with-openpower-gpus-and-docker) walks through some simple sample use cases. In addition, some cool examples can be seen from the results of the recently concluded [OpenPOWER Developer Challenge](https://openpowerfoundation.org/blogs/openpower-developer-challenge-finalists/).
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## This Dog Will Hunt
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Our little GPU-accelerated pet breed classification micro-service is running in a Docker container and can be accessed at this [link](http://129.33.248.88:31001/) from a mobile device or laptop. See for yourself!
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For example, given this image link from a Google search for "dog images", [https://www.petpremium.com/pets-pics/dog/german-shepherd.jpg](https://www.petpremium.com/pets-pics/dog/german-shepherd.jpg), we got this correct classification in 0.118 secs:
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You can also spin up your own GPU Docker container with deep learning libraries (e.g. Caffe) in the [NIMBIX cloud](https://platform.jarvice.com/landing) and train your own model and develop your own accelerated classification example.
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Give it a try and share your screenshots in the comments section below!
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