-
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_*
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// run in console | |
function ReconnectColab(){ | |
document.querySelector("#top-toolbar > colab-connect-button").shadowRoot.querySelector("#connect").click(); | |
console.log("Reconnect clicked."); | |
} | |
setInterval(ReconnectColab,120000); |
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from multiprocessing.queues import Queue | |
import multiprocessing | |
# The following implementation of custom MyQueue to avoid NotImplementedError | |
# when calling queue.qsize() in MacOS X comes almost entirely from this github | |
# discussion: https://github.com/keras-team/autokeras/issues/368 | |
# Necessary modification is made to make the code compatible with Python3. | |
class SharedCounter(object): |
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#CODE | |
#Generate root password | |
import random, string | |
password = ''.join(random.choice(string.ascii_letters + string.digits) for i in range(20)) | |
#Download ngrok | |
! wget -q -c -nc https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip | |
! unzip -qq -n ngrok-stable-linux-amd64.zip | |
#Setup sshd |
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import time | |
#input_str = input() | |
input_str = 'Ilikesandwiches' | |
word_dict = ['I', 'like', 'sandwiches', 'sand', 'and', 'a', 'lot', 'a', 'ability', 'able', 'about', 'above', 'accept', 'according', 'account', 'across', 'act', 'action', 'activity', 'actually', 'add', 'address', 'administration', 'admit', 'adult', 'affect', 'after', 'again', 'against', 'age', 'agency', 'agent', 'ago', 'agree', 'agreement', 'ahead', 'air', 'all', 'allow', 'almost', 'alone', 'along', 'already', 'also', 'although', 'always', 'American', 'among', 'amount', 'analysis', 'and', 'animal', 'another', 'answer', 'any', 'anyone', 'anything', 'appear', 'apply', 'approach', 'area', 'argue', 'arm', 'around', 'arrive', 'art', 'article', 'artist', 'as', 'ask', 'assume', 'at', 'attack', 'attention', 'attorney', 'audience', 'author', 'authority', 'available', 'avoid', 'away', 'baby', 'back', 'bad', 'bag', 'ball', 'bank', 'bar', 'base', 'be', 'beat', 'beautiful', 'because', 'become', 'bed', 'before', 'begin', 'behavior', 'behind', 'believe', 'benefi |
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'#&()+-,.:;/ 0123456789AÁÀẢÃẠĂẮẰẲẴẶÂẤẦẨẪẬBCDĐEÊẾỀỂỄỆFGHIÍÌỈĨỊJKLMNOÓÒỎÕỌÔỐỒỔỖỘƠỚỜỞỠỢPQRSTUÚÙỦŨỤƯỨỪỬỮỰVWXYÝỲỶỸỴZaáàảãạăắằẳẵặâấầẩẫậbcdđeéèẻẽẹêếềểễệfghiíìỉĩịjklmnoóòỏõọôốồổỗộơớờởỡợpqrstuúùủũụưứừửữựvwxyýỳỷỹỵz'́ |
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# use ImageMagick convert | |
# the order is important. the density argument applies to input.pdf and resize and rotate to output.pdf | |
convert -density 90 input.pdf -rotate 0.5 -attenuate 0.2 +noise Multiplicative -colorspace Gray output.pdf |
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from contextlib import contextmanager | |
import numpy as np | |
import torch | |
from torch import Tensor, ByteTensor | |
import torch.nn.functional as F | |
from torch.autograd import Variable | |
import pycuda.driver | |
from pycuda.gl import graphics_map_flags | |
from glumpy import app, gloo, gl |
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from __future__ import print_function | |
import requests | |
import json | |
import cv2 | |
addr = 'http://localhost:5000' | |
test_url = addr + '/api/test' | |
# prepare headers for http request | |
content_type = 'image/jpeg' |
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