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layout	post
title	How to Sync Microsoft OneDrive with Ubuntu 18.04 and 20.04 (64 bit)
tags	ubuntu-18.04, ubuntu-20.04
date	2020-04-23

There are many instructions to syncronize your files with OneDrive cloud-based storage in Linux. However they typically address multiple Linux distributions that often causes a confusion or an use of some commands which have not been actually tested before publishing. This instruction is for Ubuntu 18.04 and 20.04 (64 bit) only.

In order to install onedrive, first you need to install some dependencies and set up git.

Intel for display, NVIDIA for computing

This guide will show you how to use Intel graphics for rendering display and NVIDIA graphics for CUDA computing on Ubuntu 18.04 / 20.04 desktop.

I made this work on an ordinary gaming PC with two graphics devices, an Intel UHD Graphics 630 plus an NVIDIA GeForce GTX 1080 Ti. Both of them can be shown via lspci | grep VGA.

00:02.0 VGA compatible controller: Intel Corporation Device 3e92
01:00.0 VGA compatible controller: NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1)

This was tested on a ThinkPad P70 laptop with an Intel integrated graphics and an NVIDIA GPU:

lspci | egrep 'VGA|3D'
00:02.0 VGA compatible controller: Intel Corporation Device 191b (rev 06)
01:00.0 VGA compatible controller: NVIDIA Corporation GM204GLM [Quadro M3000M] (rev a1)

A reason to use the integrated graphics for display is if installing the NVIDIA drivers causes the display to stop working properly. In my case, Ubuntu would get stuck in a login loop after installing the NVIDIA drivers. This happened regardless if I installed the drivers from the "Additional Drivers" tab in "System Settings" or the ppa:graphics-drivers/ppa in the command-line.

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

	#!/usr/bin/env python

	import math
	import matplotlib.pyplot as plt
	import torch
	import torch.nn as nn

	from sklearn.datasets import make_moons
	from torch import Tensor
	from tqdm import tqdm

	from treelib import Tree
	from os import walk

	try: # python 3+
	from pathlib import Path
	except ImportError:
	from pathlib2 import Path


	def get_fs_tree(start_path, # type: str

	##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
	## Created by: Hang Zhang, Rutgers University, Email: zhang.hang@rutgers.edu
	## Modified by Thomas Wolf, HuggingFace Inc., Email: thomas@huggingface.co
	## Copyright (c) 2017-2018
	##
	## This source code is licensed under the MIT-style license found in the
	## LICENSE file in the root directory of this source tree
	##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

	"""Encoding Data Parallel"""

	import tensorflow as tf


	# 1. Create and save two graphs

	# c = a*b
	g1 = tf.Graph()
	with g1.as_default():
	a = tf.placeholder(tf.float32, name='a')
	b = tf.Variable(initial_value=tf.truncated_normal((1,)), name='b')

	"""
	Author: Awni Hannun

	This is an example CTC decoder written in Python. The code is
	intended to be a simple example and is not designed to be
	especially efficient.

	The algorithm is a prefix beam search for a model trained
	with the CTC loss function.

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

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Intel for display, NVIDIA for computing

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