Subject ▸ computer science

Running R Shiny, RStudio Server, OpenCPU and Webmin in a Vagrant Virtual Box

Introduction This is a modified version of a Vagrant machine originally created six years ago. I found the VM in the web here. It wasn’t running from the get-go and some modifications were required. The changes are documented in the markdown files NEWS, BUILD and HISTORY with the project. About this virtual machine The machine was upgraded to Ubuntu 14.04 Trusty 64 bits which makes some of the problems, due to aged software go away.

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A Deep Learning virtual machine (TensorFlow)

Introduction This is a VirtualBox VM that is automatically generated using Vagrant. Machine Learning and Deep Learning packages installed are: Scikit-Learn, NLTK, Keras, TensorFlow and Theano. A Vagrant file is used to generate this VM, which runs on Ubuntu 14.04 (trusty64). Getting Started This VM should work in Windows, macOS and Linux VirtualBox (version 6+) is required Download and install Vagrant Clone the virtual machine specs with:

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Docker for R - Minimal book

This is a minimal example of a book based on R Markdown and bookdown ( Please see the page “Get Started” at for how to compile this example.

R and Python commingled: RPyStats in Windows and Linux. Season 1, Episode 6

Alright! We have two Rmarkdown notebooks written in Python and R running PyTorch libraries. There are many more things that still we can do to improve the accuracy of the model to recognize hand-written digits. But before we continue improving the algorithm and the model, I wanted to make a brief pause and showed you something that really made me jump ship from Windows to Linux. It is related to R, Python and data science.

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R and Python commingled: Creating a PyTorch project with RPyStats. Season 1, Episode 5

In the previous episode we ended up calculating the accuracies for the MNIST digits model using PyTorch libraries called from a Rmarkdown notebook written in Python. In this episode, we will run the same example but in another notebook written in R named *mnist_digits_rstats.Rmd*, which was saved in the folder ./work/notebooks in the previous session. With RStudio open, let’s click on the file to open it. You may notice some familiarity between the code in this notebook *mnist_digits_rstats.

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R and Python commingled: Creating a PyTorch project with RPyStats. Season 1, Episode 4

Introduction Now that you have your machine with the core applications installed (Season1, Episode 3), in this episode we will cover the steps to create a machine learning project commingling R and Python. I will call this project rpystats-apollo11. What rpystats-apollo11 will do is training a neural network using the MNIST dataset to recognize hand-written digits. I will write the code in pure Python first, and then in R in two separate Rmarkdown notebooks running in RStudio.

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Integrating Python and R for data science. Converting Eclipse binary files to dataframes in the Volve dataset

Introduction Python and R offer a good combination of powers: dozens of proven engineering, data science, and machine learning libraries, also a science oriented approach towards full reproducibility. As I have told you before, I started my coding journey with Python many years ago. I even wrote a large application for production optimization using OpenServer, Prosper, GAP and MBAL by Petroleum Experts while I was on my 3-year tour with Petronas in Kuala Lumpur in Malaysia.

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Being proven wrong on Linux every day

Being proven wrong every day: “Open Source has no-warranty, no maintenance, insecure, no leaders” From the Blog of Alfonso R. Reyes at That’s what they said about Linux. And look where it is now. Ninety percent and above of the worldwide servers use Linux; 98% of mobile smart devices use Linux or Unix derivatives; active sensors, microprocessors and controllers in the field have underlying operating systems based on Unix. Finally, cherry on top: 99.

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Customizing Rob Hyndman template

Just made changes in Rob Hyndman template to adapt to my new static website.

Virtualization brings Reproducibility to your Petroleum Engineering Workflow

Motivation By nature, I am curious. I am not only interested in the why-of-things but also in the “how”. Be able to document it and reproducing it later. And that, most of the time, could be a time consuming affair. Pleasurable, rewarding, but time consuming. Add to that data science and deep learning and you get a exponential combination. I own a 8-core, 32GB RAM, 3TB SSD, Quadro K2100M GPU laptop that originally acquired with the intention of running several virtual machines with Windows, Linux and MacOS, as part of my work as an atypical petroleum engineer.

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