First, create a Git subfolder inside your Dropbox folder. Then you can share the individual projects inside that folder with whomever you want (or just use it for instant offsite backups).
From inside a Git project:
git clone --bare . ~/Dropbox/Git/gitproject.git
git remote add dropbox ~/Dropbox/Git/gitproject.git
When you're ready to push:
This is a brief receipt used for NVIDIA CUDA installation on a Ubuntu Linux Distro. All the steps can be found in the guide provided by NVIDIA but this gist contains some glue points to overcome problems during the installation.
The current installation was performed for an Ubuntu version 14.04.5 LTS, Trusty Tahr using a x86_64 architecture. You can get this information by typing in a consonle terminal:
$ uname -m && cat /etc/*release
Once you get that information we can find the NVIDIA Toolkit that supports our Linux Distro. Last version is NVIDIA CUDA Toolkit 8.0 ga2 although version 9 is already published but it does not give support for 14.04. To know what that information you must check Table 1. Native Linux Distribution Support
One of the best option is to use the run package offered by Qt to install the libraries and its IDE (Qt Creator). Nevertheless However, when you want to compile it for a sever, where no GUI is required, you must be then interested in how to compile from source and discover some of the configuration options you have to disable some GUI modules among others and get lightest weight libraries.
First of all download the tar.xz file from Qt. You can also go to the archive and select the best that fulfill your requirement. Right now I am going on with the newest version up to the moment v5.9.3.
Create a directory and uncompress the downloaded file:
$ mkdir temp
$ cd temp
Virutal environment is an useful utility for python to install packages locally and not globally so your system remains clean after installing multiples packages. It is recommended to avoid future conflicts between packages.
To install virtual environment you just have to type:
$ pip install virtualenv
To check the installation:
$ virtualenv --version
This gist shows how to install Tensorflow with CUDNN support and how to check the correct installation. This gist is based on the Tensorflow installation guide and following the LearnOpencv blog entry Installing Deep Learning Frameworks on Ubuntu with CUDA support that is more complete that this gist but there are sometimes misleading/hidden steps that can be covered in this gist.
First prerequisite is the CUDA Toolkit installation. You can check this gist for the CUDA installation. As a difference from the CUDA installation CUDNN is very easy to install. We only have to deploy the downloaded libraries in your system. At the time I am writing this guide current toolkit version is 8.0.
To downl
Export the database using the export functionality from phpmyadmin. Use the default configuration - Quick, Format SQL. Remember to select the database to export before accessing to this operation. How to install phpmyadmin
Compress the web page code to send to the new virtual machine. You can do it by the following command:
$ tar -czvf name-of-archive.tar.gz /path/to/directory-or-file
The following packages must be installed (remove the comments):
$ sudo apt-get install vim //vim editor
This gist explains how to set a working environment to deploy easily your project in your production server. To this aim a git bare repository is set in the remote server which is used for control versions as well as to upload and deploy your progress to the production server.
The structure of the proposed environment consists of 3 parts:
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