Sungju Kim goddoe

(ref: https://stackoverflow.com/questions/26784164/pandas-multiprocessing-apply) I'm trying to use multiprocessing with pandas dataframe, that is split the dataframe to 8 parts. apply some function to each part using apply (with each part processed in different process).

EDIT: Here's the solution I finally found:

import multiprocessing as mp
import pandas.util.testing as pdt

def process_apply(x):

reference: https://stackoverflow.com/questions/50177216/how-to-grant-all-privileges-to-root-user-in-mysql-8-0

Starting with MySQL 8 you no longer can (implicitly) create a user using the GRANT command. Use CREATE USER instead, followed by the GRANT statement:

CREATE USER 'root'@'%' IDENTIFIED BY 'root';
GRANT ALL PRIVILEGES ON *.* TO 'root'@'%' WITH GRANT OPTION;
SELECT Host,User,authentication_string FROM mysql.user;

	ou can use one -- more option before that:

	ipython script.py -- --argument blah
	Help of Ipython:

	ipython [subcommand] [options] [-c cmd \| -m mod \| file] [--] [arg] ...

	If invoked with no options, it executes the file and exits, passing the
	remaining arguments to the script, just as if you had specified the same
	command with python. You may need to specify `--` before args to be passed

	class DataContainer(object):
	def __init__(self, **kwargs):
	for key, val in kwargs.items():
	setattr(self, key, val)

	class Net(torch.nn.Module):
	def __init__(self):
	self.a = torch.nn.Parameter(torch.zeros(1))
	self.b = torch.zeros(1)

	def forward(self, inputs):
	#do some stuff

	def cuda(self, device=None):
	self = super().cuda(device)

	x = torch.randn(10)
	print(x)
	y, ind = torch.sort(x, 0)
	print("y", y)
	print("ind", ind)
	unsorted = y.new(*y.size())
	unsorted.scatter_(0, ind, y)
	print("unsorted:", unsorted)
	print((x - unsorted).abs().max())

	Batch Norm:

	(+) Stable if the batch size is large
	(+) Robust (in train) to the scale & shift of input data
	(+) Robust to the scale of weight vector
	(+) Scale of update decreases while training
	(-) Not good for online learning
	(-) Not good for RNN, LSTM
	(-) Different calculation between train and test
	Weight Norm:

	Hello all,

	I have been using Docker with the Devicemapper storage driver. After manually editing the launcher script, I finally took the time to convert to Overlay2. Here are the instructions I took to do this. I hope this helps others. I see others have asked the question 9, but I don’t see any answers. I used this excellent guide 64 to help figure this out.

	Step 1: Halt your Discourse application (and other docker apps)

	./launcher stop app

	Step 2: Commit and save your old Docker containers/images