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Contributing

To contribute to NumS on Ray, we recommend cloning the repository and installing the project in developer mode using the following set of commands:

cd nums
conda create --name nums python=3.7 -y
conda activate nums
pip install -e ".[testing]"

Contributing NumPy Functionality

To make basic contributions to the NumPy API, follow these steps:

  1. Replicate the function signature in nums.numpy.api. If it's a np.ndarray method, add the function signature to nums.core.array.blockarray.BlockArray.
  2. If possible, implement the function using existing methods in nums.core.array.application.ArrayApplication or nums.core.array.blockarray.BlockArray.
  3. Write a new implementation ArrayApplication or BlockArray if it's not possible to implement using existing methods, or the implementation's execution speed can be improved beyond what is achievable using existing methods.
  4. Add kernel interfaces to nums.core.backends.interfaces.ComputeInterface, and implement the interface methods for all existing compute implementations. Currently, the only compute interface is nums.core.backends.numpy_compute.
  5. Write tests covering all branches of your implementation in the corresponding test module in the project's tests/ directory.
  6. Do your best to implement the API in its entirety. It's generally better to have a partial implementation than no implementation, so if for whatever reason certain arguments are difficult to support, follow the convention we use to raise errors for unsupported arguments in functions like nums.numpy.api.min.
  7. If you run into any issues and need help with your implementation, open an issue describing the issue you're experiencing.

We encourage you to follow the nums.numpy.api.arange implementation as a reference.

Steps for debugging NumS on MPI Backend with PyCharm professional

  • Select Run -> Edit Configurations, there you will find a template for Python Debug Server.
  • Check the box that says "allow parallel run," and follow step 1 in the template that has instructions on how to install pydevd_pycharm. Leave everything else intact in the template.
  • Click Apply to apply the changes, then click the "debug" button as many times as the number of mpi processes (ranks) which you want to debug.
  • In the console for each debug server, you will see the port on which it is communicating. Make a note of these ports for all the debug servers.
  • Insert this piece of code at the beginning of your NumS program.
from mpi4py import MPI
import os
size = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
import pydevd_pycharm
# Replace the port number below with the ones from above. 
# This example is for 2 ranks. For n ranks, the port mapping list will have n port numbers.
port_mapping = [56482, 56483]
pydevd_pycharm.settrace('localhost', port=port_mapping[rank], stdoutToServer=True, stderrToServer=True)
  • This will attach each debug process to each rank in your mpi cluster. You'll need to step through each rank separately.
  • Now run your nums program from the terminal using the command mpiexec -n 2 python <nums_program.py>.
  • The above steps are adapted from the following source: stackoverflow post