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1
OCTOBER 2015
Detectron on Amazon SageMaker
Matt McDonnell, Data Scientist at Metail
Cambridge AWS Meetup 2018-02-06
2
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
3
An interesting Deep Learning application
4
An interesting Deep Learning application
5
Find the paper
6
Find the TensorFlow implementation
7
Find the TensorFlow implementation demo
8
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
9
Amazon SageMaker Homepage
10
Starting a Notebook Instance
11
Creating a Role for Notebook Instances
12
Instance Starting
13
Instance Started - on to the demo!
14
Open Jupyter Server
15
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
16
Mask R-CNN Code downloaded
17
Open demo notebook and choose kernel
18
Run the demo!
19
Cleaning up - use Dashboard to see what is running
20
Stop expensive instances when not in use
21
Delete unneeded instances
22
Any questions?

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Detectron on Amazon SageMaker