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:
#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.
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
| // 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") |
Tested with
Python 2.7, OS X 10.11.3 El Capitan, Apache Spark 1.6.0 & Hadoop 2.6
Download Apache Spark and build it or download the pre-built version.
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