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@Brainiarc7
Brainiarc7 / build-tensorflow-from-source.md
Last active Jan 11, 2021
Build Tensorflow from source, for better performance on Ubuntu.
View build-tensorflow-from-source.md

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

@santoshachari
santoshachari / Laravel PHP7 LEMP AWS.md
Last active Mar 19, 2021
Laravel 5.x on Ubuntu 16.x, PHP 7.x, Nginx 1.9.x
View Laravel PHP7 LEMP AWS.md

#Steps to install latest Laravel, LEMP on AWS Ubuntu 16.4 version. This tutorial is the improvised verision of this tutorial on Digitalocean based on my experience.

Install PHP 7 on Ubuntu

Run the following commands in sequence.

sudo apt-get install -y language-pack-en-base
sudo LC_ALL=en_US.UTF-8 add-apt-repository ppa:ondrej/php
sudo apt-get update
sudo apt-get install zip unzip
@jbn
jbn / spark_json_reader.scala
Created Dec 10, 2015
JSON + bz2 + Spark = WINNING
View spark_json_reader.scala
// Load a DataFrame of users. Each line in the file is a JSON
// document, representing one row.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
val people = sqlContext.read.json("users.json.bz2")
@tommycarpi
tommycarpi / ipython_notebook+spark.md
Last active Dec 30, 2017
Link Apache Spark with IPython Notebook
View ipython_notebook+spark.md

How to link Apache Spark 1.6.0 with IPython notebook (Mac OS X)

Tested with

Python 2.7, OS X 10.11.3 El Capitan, Apache Spark 1.6.0 & Hadoop 2.6

Download Apache Spark & Build it

Download Apache Spark and build it or download the pre-built version.

View tmux-cheatsheet.markdown

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname
@GaelVaroquaux
GaelVaroquaux / 00README.rst
Last active Apr 1, 2021
Copy-less bindings of C-generated arrays with Cython
View 00README.rst

Cython example of exposing C-computed arrays in Python without data copies

The goal of this example is to show how an existing C codebase for numerical computing (here c_code.c) can be wrapped in Cython to be exposed in Python.

The meat of the example is that the data is allocated in C, but exposed in Python without a copy using the PyArray_SimpleNewFromData numpy