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import multiprocessing #:)

def do_this(number):
  print number
  return number*2

# Create a list to iterate over. 
# (Note: Multiprocessing only accepts one item at a time)
some_list = range(0,10)

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"""
Usage:

Make sure that redis is running on localhost (or adjust the url)
Install uvicorn or some other asgi server https://asgi.readthedocs.io/en/latest/implementations.html

    pip install -u uvicorn

Install dependencies

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To list all python versions in default locations

ls /usr/bin/python*

To remove just python3 package

sudo apt-get remove python3.5

plus it's dependent packages

sudo apt-get remove --auto-remove python3.5

plus configuration and/or data files of python3

sudo apt-get purge python3.5

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1WBllCh7h5NjY09cvDby-amOO2FN0HbOA

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# source: https://www.geeksforgeeks.org/difference-between-multithreading-vs-multiprocessing-in-python/
import time, os
from threading import Thread, current_thread
from multiprocessing import Process, current_process


COUNT = 200000000
SLEEP = 10
  
def io_bound(sec):

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  def f(*, a):
    pass
  
  f(1) # raises error
  f(a=1) # works

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# ========================================================================================
# setup image
# ========================================================================================
# download or build image
docker build . 


# ========================================================================================
# setup container
# ========================================================================================

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I wanted to find a different method that didn't require me to read the json file or to have to upload it to root while using Colab. I instead wanted to just store the json in my drive somewhere and let the kaggle cli know where it is.

As it turns out, there's a way to do that, which is set the environment variable KAGGLE_CONFIG_DIR

If you don't have the json file yet, check the docs for how to get that: link Then upload it to your drive. Mine happens to be at /content/drive/My Drive/fastai-v3/.kaggle/kaggle.json. (ayyy shoutout to the FastAI courses 👌 ) Now in colab you can just run:

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import random, math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patheffects as mpe


"""
dataset
"""