Demo on using Amazon SageMaker to try out Mask R-CNN, a deep learning image segmentation network. Presented at Cambridge AWS Meetup Main Meeting #18: New Amazon AI Services

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OCTOBER 2015
Detectron on Amazon SageMaker
Matt McDonnell, Data Scientist at Metail
Cambridge AWS Meetup 2018-02-06

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Using Amazon SageMaker to try out Deep
Learning applications
• Discovering something interesting:
Detectron
In particular Mask R-CNN could be
useful for image segmentation
• Starting an Amazon SageMaker
notebook
• Running Open Source demo code
– Cloning GitHub repos
– Running demo notebook
• Cleaning up
Overview
Examples of Metail Composed Photography
We’re not currently using Mask R-CNN for this
and it may not be a good use case - but worth
trying out

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An interesting Deep Learning application

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An interesting Deep Learning application

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Find the paper

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Find the TensorFlow implementation

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Find the TensorFlow implementation demo

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Use Amazon SageMaker to try it out
For demo purposes we’ll skip to a newly created notebook instance (step 6) to avoid waiting for startup

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Amazon SageMaker Homepage

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Starting a Notebook Instance

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Creating a Role for Notebook Instances

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Instance Starting

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Instance Started - on to the demo!

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Open Jupyter Server

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Clone GitHub Repos and Install
https://gist.github.com/mattmcd/59fc456ce6e6943927bd5918f0e677bf
cd SageMaker
git clone https://github.com/matterport/Mask_RCNN.git
git clone https://github.com/waleedka/coco.git
cd coco/PythonAPI
source activate tensorflow_p36
make
python setup.py install

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Mask R-CNN Code downloaded

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Open demo notebook and choose kernel

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Run the demo!

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Cleaning up - use Dashboard to see what is running

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Stop expensive instances when not in use

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Delete unneeded instances

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Any questions?