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$ git remote rm origin
$ git remote add origin git@github.com:aplikacjainfo/proj1.git
$ git config master.remote origin
$ git config master.merge refs/heads/master

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How to install NVIDIA Docker 2 package on Ubuntu and Debian:

If you came to this result (from Google or elsewhere) after realizing that Nvidia-docker's entry on this subject does not result in a working installation, here are the basic steps needed to install this package correctly:

For starters, ensure that you've installed the latest Docker Community edition by following the steps below:

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88

sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"

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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.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

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curl -OL https://github.com/google/protobuf/releases/download/v3.2.0/protoc-3.2.0-linux-x86_64.zip
unzip protoc-3.2.0-linux-x86_64.zip -d protoc3
sudo mv protoc3/bin/* /usr/local/bin/
sudo mv protoc3/include/* /usr/local/include/

# Optional: change owner
# sudo chwon [user] /usr/local/bin/protoc
# sudo chwon -R [user] /usr/local/include/google

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Text Classification

To demonstrate text classification with Scikit Learn, we'll build a simple spam filter. While the filters in production for services like Gmail will obviously be vastly more sophisticated, the model we'll have by the end of this chapter is effective and surprisingly accurate.

Spam filtering is the "hello world" of document classification, but something to be aware of is that we aren't limited to two classes. The classifier we will

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Text Classification

To demonstrate text classification with Scikit Learn, we'll build a simple spam filter. While the filters in production for services like Gmail will obviously be vastly more sophisticated, the model we'll have by the end of this chapter is effective and surprisingly accurate.

Spam filtering is the "hello world" of document classification, but something to be aware of is that we aren't limited to two classes. The classifier we will be using supports multi-class classification, which opens up vast opportunities like author identification, support email routing, etc… However, in this example we'll just stick to two classes: SPAM and HAM.

For this exercise, we'll be using a combination of the Enron-Spam data sets and the SpamAssassin public corpus. Both are publicly available for download and are retreived from the internet during the setup phase of the example code that goes with this chapter.

Loading Examples

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#!/bin/bash
#
# Install Postgres 9.1, PostGIS 2.0 and pgRouting on a clean Ubuntu 12.04 install (64 bit)
# updated to PostGIS 2.0.1

# add the ubuntu gis ppa
sudo apt-get -y install python-software-properties
sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable 
sudo apt-get update 

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#!/bin/bash
#
# Install Postgres 9.1, PostGIS 2.0 and pgRouting on a clean Ubuntu 12.04 install (64 bit)
# updated to PostGIS 2.0.1

# add the ubuntu gis ppa
sudo apt-get -y install python-software-properties
sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable 
sudo apt-get update