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A comment on Gluon – Deep Learning API from AWS and Microsoft. Parent comment

Re:Gluon – Deep Learning API from AWS and Microsoft by kuschku on Thursday October 12, 2017 @12:34PM (#15459180)
Even if you split your infrastructure, have you ever tried refactoring larger projects, with many contributors, while ensuring API contracts are kept?

Without a strict and static type system it becomes quite problematic to ensure new code keeps the API contract, unless you have unit codes for every possible value.

A good type system accelerates your coding speed, compared to writing equivalent unit tests, and it improves your quality, compared to no testing.

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Re:Gluon – Deep Learning API from AWS and Microsoft by tanilama on Thursday October 12, 2017 @03:20PM (#15460537)
> A good type system accelerates your coding speed, compared to writing equivalent unit tests, and it improves your quality, compared to no testing.

Coding speed is least of my concern for a ML project, to be honest. And unit tests aren't useful either, since ML by large is not deterministic. A lot u said is true for web application, but didn't really apply for a ML project

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Re:Gluon – Deep Learning API from AWS and Microsoft by nielsbot on Thursday October 12, 2017 @12:42PM (#15459261)
“accelerates coding speed”

this is debatable

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Re:Gluon – Deep Learning API from AWS and Microsoft by kuschku on Thursday October 12, 2017 @02:15PM (#15459973)
Well, the comparison was to writing unit tests that provide the same safety as an equivalent type system.

And compared to that, the type system is certainly faster.

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Re:Gluon – Deep Learning API from AWS and Microsoft by lstmemery on Thursday October 12, 2017 @03:38PM (#15460675)
Numpy enforces type consistency within arrays. Type errors are still possible but generally rarer and are noticed sooner than base Python.
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Re:Gluon – Deep Learning API from AWS and Microsoft by nielsbot on Friday October 13, 2017 @05:09PM (#15469251)
valid point
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