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Creating Reproducible Data Science Workflows using Docker Containers
--client
pyohio
--show
pyohio_2017
--room barbietootle 12352 --force
Next: 11 Rapid Prototyping In Data Science With Big Data & Python
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Marks
Author(s):
Aly Sivji
Location
Barbie Tootle
Date
jul Sun 30
Days Raw Files
Start
13:30
First Raw Start
11:57
Duration
00:50:00
Offset
1:32:07
End
14:20
Last Raw End
13:42
Chapters
00:00
0:00:31
0:30:30
Total cuts_time
38 min.
http://pyohio.org/schedule/presentation/303/
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Jupyter notebooks make it easy to create reproducible workflows that can be distributed across groups and organizations. This is a simple process provided that our end-users have access to the data along with a compatible Python environment. Learn how to use Docker to package a shareable image containing the libraries, code, and data required to reproduce every calculation.
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