[GitHub] rahul003 opened a new pull request #9151: removes python path insert of tests folder for examples

[GitBox <gi...@apache.org>]

rahul003 opened a new pull request #9151: removes python path insert of tests folder for examples
URL: https://github.com/apache/incubator-mxnet/pull/9151
 
 
   ## Description ##
   Some examples insert a folder to the python path to fetch a file from `tests/` folder. This causes few problems
       1. It requires the user to have the whole mxnet repository, with the directory structure intact
       2. It means the user can't run the example standalone
       3. For distributed training this means we would have to synchronize the whole mxnet directory across all machines. 
   This is unnecessary and inconvenient for the user.
   
   ## Checklist ##
   ### Essentials ###
   - [x] Passed code style checking (`make lint`)
   - [x] Changes are complete (i.e. I finished coding on this PR)
   - [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
   - [x] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [x] Moves get_mnist_pkl, get_mnist_ubyte, get_mnist_iterator from tests folder to mxnet.test_utils
   - [x] Adds support to functions that generate data iterators for partitioning of datasets so that different workers in distributed training can use different parts of the dataset.
   

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