Shawn Cicoria cicorias

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

Preparation

In open Ubuntu 18.04 machine click Parallels Actions -> "Install Parallels Tools"
A "Parallels Tools" CD will popup on your Ubuntu desktop.
Open it by double mouse click, copy all the content to a new, empty directory on a desktop, name it for e.g. "parallels_fixed"
Open terminal, change directory to parallels_fixed (cd ~/Desktop/parallels_fixed)
Make command line installer executable (chmod +x install)
Change directory to "installer" (cd installer)
Make few other scripts executable: chmod +x installer.* *.sh prl_*

K8s on (vanilla) Raspbian Lite

Yes - you can create a Kubernetes cluster with Raspberry Pis with the default operating system Raspbian. Carry on using all the tools and packages you're used to with the officially-supported OS.

Pre-reqs:

You must use an RPi2 or 3 for Kubernetes
I'm assuming you're using wired ethernet (Wi-Fi also works)

	require 'net/http'
	require 'json'
	require 'uri'

	@token = ''

	def list_files
	ts_to = (Time.now - 30 * 24 * 60 * 60).to_i # 30 days ago
	params = {
	token: @token,

	from PIL import Image

	def scale(image, max_size, method=Image.ANTIALIAS):
	"""
	resize 'image' to 'max_size' keeping the aspect ratio
	and place it in center of white 'max_size' image
	"""
	im_aspect = float(image.size[0])/float(image.size[1])
	out_aspect = float(max_size[0])/float(max_size[1])
	if im_aspect >= out_aspect:

	# In Keras the Convolution layer requirest an additional dimension which will be used for the various filter.
	# When we have eg. 2D dataset the shape is (data_points, rows, cols).
	# But Convolution2D requires shape (data_points, rows, cols, 1).
	# Otherwise it fails with eg. "Exception: Input 0 is incompatible with layer convolution2d_5: expected ndim=4, found ndim=3"
	#
	# Originally I reshaped the data beforehand but it only complicates things.
	#
	# An easier and more elegant solution is to add a Reshape layer at the input
	# of the network!
	#

	{
	"auto-close-tag.fullMode": true,
	"emmet.includeLanguages": {
	"javascript": "javascriptreact"
	},
	"emmet.syntaxProfiles": {
	"javascript": "jsx"
	},
	"editor.tabSize": 2,
	"workbench.activityBar.visible": false,

	<html>
	<body>
	<H2>Are you sure to overwrite your votes?</H2>
	<form action="" method="post" enctype="multipart/form-data">
	<fieldset>
	<input type="submit" name="action" value="Update" />
	<input type="submit" name="action" value="Cancel" />
	</fieldset>
	</form>
	</body>

	git clone https://github.com/coursera-dl/coursera-dl
	cd coursera-dl
	pip3 install -r requirements.txt
	./coursera-dl -u <Email-id> -p <Password> --subtitle-language en raspberry-pi-interface

	#include <stdio.h>
	#include <stdlib.h>
	#include <stdint.h>
	#ifdef _MSC_VER
	#include <intrin.h> /* for rdtscp and clflush */
	#pragma optimize("gt",on)
	#else
	#include <x86intrin.h> /* for rdtscp and clflush */
	#endif

Shawn Cicoria cicorias

Creating a Fork

Preparation

K8s on (vanilla) Raspbian Lite

Pre-reqs:

Master node setup