This script will help you run grid search on any python script.

multiprocessing, numpy, csv

Your code should include the following elements:

1. A main file which accepts arguments with argparse. Let's call is main.py.
2. A function to compile the data returned by main.py, let's call it compile_func. This function should be able to take the data points iteratively. You'd have to call this from main.py. A simple function called is already provided and can be imported as from grid_search import insert_to_csv.

It it best to open dummy_run.py as the library is quite intuitive and simple to run. You can use this file as your template.

1. Import the library from grid_search import GridSearch
2. Set name of main file, main_file = 'dummy_main.py'
3. Create a function for compiling result. I am importing the compile_csv function from grid_search. I am calling this from within my dummy_main.py file which saves the result of every run in a csv file.
4. Define arguments for running grid search:
   1. For every parameter you'd need one key containing it's name
   2. Each key can either be a list of all values of that parameter
   3. Or each key can be a dictionary with keys {'min', 'max', 'num', 'scale'} if you wish to uniformly choose values in a range. 'scale' can be 'linear' or 'log'. For log scale 'min' and 'max' should be the powers of 10. Eg. for 1e-1 to 1e3, 'min'=-1 and 'max'=3
5. Create an object of GridSearch. myGridSearch = GridSearch(main_file, compile_func, args, num_process=2)
6. Run grid search with myGridSearch.run()