TensorFlow

Setting up Deep Learning environment on Centos 7: Nvidia CUDA, Anaconda, iPython, Keras, Theano and Tensorflow

The following is detailed, tested sequence of steps to setup universal deep learning environment on a minimal install of Centos 7. While Ubuntu is a bit more documented platform – Centos 7 has it’s own advantages and I wanted to target this specific version. My main reason is that Splunk is more suitable to run on Centos / RedHat and I want to make sure to have instructions that support abilities to apply deep learning in this environment.

That took me about a week to figure out and polish this sequence. This setup is somewhat caters to Jeremy Howard’s Deep Learning course, however it enables to setup frustration-free, universal environment suitable for other variations of practical deep learning.

You start with totally blank, minimal install of Centos 7 on a deciated server and end up with:

Nvidia CUDA* stuff fully installed.
Anaconda, iPython, Keras, Theano and Tensorflow + tons of other data science, math and machine learning software installed.
Both Python 2.x and Python 3.x setup and configured to run simultaneously (on a different ports: Python 2.x version on port 8882, Python 3.x version on port 8883) in a non-conflicting environment.

With my love to Nvidia hardware, Nvidia CUDA installation documentation is full of vague statements lacking clear steps to accomplish the task, and it took me many hours to assemble bits and pieces from all over the web to finally make Nvidia CUDA drivers and software work and operational.

I plan to add more comments and explanations to this section – but here are the step by step instructions to setup deep learning platform on fresh Centos 7 from zero to hero: […]

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