In this install note, I will discuss how to compile and install from source a GPU accelerated instance of tensorflow in Ubuntu 18.04. Tensorflow is a deep-learning framework developed by Google. It has become an industry standard tool for both deep-learning research and production grade application development. Step 0 — Basic house-keeping: Before starting the… Continue reading Install notes — Tensorflow in Ubuntu 18.04 LTS with Nvidia CUDA
Category: Cloud computing
Hardware for artificial intelligence – My wishlist.
We recently developed an incredible machine learning workstation. It was born out of necessity, when we were developing image recognition algorithms for cancer detection. The idea was so incredibly powerful, that we decided to market it as a product to help developers in implementing artificial intelligence tools. During this launch process, I came across an interesting article… Continue reading Hardware for artificial intelligence – My wishlist.
Fine tuning the cloud – Making machines learn faster.
In a previous post I discussed how easy it was to setup machine learning libraries in python using virtual machines (vm) hosted in the cloud. Today, I am going to add more details to it. This post covers how to make machine learning code run faster. This post will help any user compile a tensorflow… Continue reading Fine tuning the cloud – Making machines learn faster.
In the cloud – Geography agnostic enterprise using Google Cloud Platform.
The concept of geography/location agnostic enterprise is very simple. Whether I am in Beijing or Boston, Kent or Kentucky, New Delhi or New York, Silicon Valley or Silicon Wadi, Qatar or Quebec; I should have access to a standard set of tools to run the company. Moving around geographic locations are hard challenge for any enterprise.… Continue reading In the cloud – Geography agnostic enterprise using Google Cloud Platform.
Virtualization – Matryoshka dolls of computing.
A few weeks back I talked about various open operating systems to efficiently run some of the deep learning and simulation models. I switched back and forth between six different flavors of linux to finally settle with one. This experimentation phase is helpful in the long-run. But, for folks who want to run one particular… Continue reading Virtualization – Matryoshka dolls of computing.
Writing better code – Parallelize
Today, I am going to share a secret recipe for writing beautiful and efficient code that I learned, while creating simulation models for nanoveda. Nanoveda is using advanced nanoscale simulations to design next generation cancer therapeutics. The secret recipe is: parallelizing code. Most modern PCs have a multicore processor inside it. We seldom code to exploit all… Continue reading Writing better code – Parallelize