[GitHub] piyushghai opened a new pull request #13678: [MXNET-1260] [WIP] [DO NOT MERGE] Float64 support in NDArray in Scala

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piyushghai opened a new pull request #13678: [MXNET-1260] [WIP] [DO NOT MERGE] Float64 support in NDArray in Scala
URL: https://github.com/apache/incubator-mxnet/pull/13678
 
 
   ## Description ##
   This PR introduces Float64/Double data type support in NDArrays in Scala. Currently we only allow precision upto Float32 in Scala as a result of which there are issues when one tries to load a model trained using float64 (in another language binding).
   
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [x] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) created (except PRs with tiny changes)
   - [x] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
   - [x] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments are documented. 
   - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
   - Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
   - [x] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
   
   ## Comments ##
   - Interesting edge cases to note here
   - Need to complete the F64 support in other classes as well, and then test out and compare training of a model using float32 and float64. The comparison would be in terms of the precision of the loss, accuracy of the trained model, memory occupied by the model during training process. 
   

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