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From "Niketan Pansare (JIRA)" <>
Subject [jira] [Created] (SYSTEMML-1569) Test MLContext for robustness and scalability
Date Sun, 30 Apr 2017 19:26:04 GMT
Niketan Pansare created SYSTEMML-1569:

             Summary: Test MLContext for robustness and scalability
                 Key: SYSTEMML-1569
             Project: SystemML
          Issue Type: Test
    Affects Versions: SystemML 1.0
            Reporter: Niketan Pansare

As more APIs are getting built on top of MLContext and with large-scale demos using MLContext
and notebooks, we should test MLContext for robustness and scalability. The goal is that using
MLContext should have constant overhead compared to commandline execution (both using similar

As an example: we should check for potential OOM in Script History logic:

If we uncomment,
then you should get an OOM when passing large Numpy array with Python MLContext. This is because
toString() method on MatrixBlock converts double [] into String.

[~deron] [] [~reinwald] [~mboehm7]

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