dotnet / dotnet/machinelearning-samples

Excluding Big files from Machine Learning samples

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Description

In order to reduce Machine Learning samples zip size (from more than 2 Gb to 87 Mb), just commit this Pull Request:

https://github.com/dotnet/machinelearning-samples/pull/949

I added also a VB branch with many samples, taken from this repository and updated:

https://github.com/Nukepayload2/machinelearning-samples/tree/master/samples/visualbasic

In the VB branch I included functional tests to download all datasets, and to test all samples, in one click!

I think it is still possible to commit first the C# and F# sample repository, and then after to update Nukepayload2 VB repository and commit the VB branch to the Nukepayload2 VB repository, via a second Pull Request.

I failed to compile this demo (30/87 Mb), I think packages can be downloaded too, like other samples:
csharp\end-to-end-apps\Unity-HelloMLNET\HelloMLNET

This one compile but does not work:
csharp\getting-started\DeepLearning_ImageClassification_Binary

I did not test these demos:
csharp\end-to-end-apps\Scalable*

All other demos work fine (C#, F# and VB).
All packages are up to date (F# and VB).

Why committing directly to the master branch? At first I wanted to be able to check the size of the repository at each step, but finally, I only checked the size of the downloaded zip (It's almost impossible to delete a large file or a sensitive file from a repository, without causing an inextricable mess, especially for forked repositories), but I probably could have made a branch indeed.

Contributor guide

Open the contributing guide

Research direction

Start by reviewing pull request #949 and the sample paths named in the issue, especially csharp/end-to-end-apps/Unity-HelloMLNET/HelloMLNET and csharp/getting-started/DeepLearning_ImageClassification_Binary. Check the listed Scalable samples and the repository zip size; done means large files are excluded, the affected samples build or run as described, and the reduced archive is validated.

Written by the indexing model from the issue text.

Assessment

Tech stack
csharp, fsharp, visualbasic
Domain
machine-learning
Issue type
Refactor
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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