| #!/bin/bash | |
| ### steps #### | |
| # verify the system has a cuda-capable gpu | |
| # download and install the nvidia cuda toolkit and cudnn | |
| # setup environmental variables | |
| # verify the installation | |
| ### | |
| ### to verify your gpu is cuda enable check |
This is a companion piece to my instructions on building TensorFlow from source. In particular, the aim is to install the following pieces of software
on an Ubuntu Linux system, in particular Ubuntu 20.04.
This script reads PascalVOC xml files, and converts them to YOLO txt files.
Note: This script was written and tested on Ubuntu. YMMV on other OS's.
Disclaimer: This code is a modified version of Joseph Redmon's voc_label.py
This is a collection of the tweaks and modification I've made to my Arch Linux installation over the months. These may be applicable to other distros, but please check first before doing anything. I also included Arch Wiki references for all the procedures I mentioned. My recommendation is not to blindly follow this gist but to always check with the Arch Linux wiki first. Things move fast and by the time you're reading this my gist may be out of date. Lastly, the golden rule: never execute a command you don't understand.
My current DE of choice is KDE's Plasma. I find it just about perfect.
There are various ways to install it on Arch. The most popular one is to install plasma and plasma-applications, but I don't like doing that because it comes with too many programs I'll never use. I, instead, install the base plasma group, remove the few extra packages that come with it, then I finish off by installing a few KDE apps that don't come with th
| # | |
| # Configuration files for Manjaro i3 on Lenovo Yoga 2 pro | |
| # to scale properly on HiDPI (3200x1800) | |
| # | |
| ##################################################### | |
| ~/.profile # | |
| ##################################################### | |
| # UI element scaling, icons | |
| export GDK_SCALE=2 |
| ## https://timogrossenbacher.ch/2016/12/beautiful-thematic-maps-with-ggplot2-only/ | |
| library(raster) | |
| library(sp) | |
| library(rgdal) | |
| library(viridisLite) | |
| library(rasterVis) | |
| ## Read age data | |
| data <- read.csv("input/avg_age_15.csv", stringsAsFactors = F) | |
| ## Read shapefile |
| # If you come from bash you might have to change your $PATH. | |
| export PATH=$HOME/bin:/usr/local/bin:/home/jpenuchot/node_modules/.bin:$HOME/.cargo/bin:$PATH | |
| # Path to your oh-my-zsh installation. | |
| export ZSH=$HOME/.oh-my-zsh | |
| # Set name of the theme to load. Optionally, if you set this to "random" | |
| # it'll load a random theme each time that oh-my-zsh is loaded. | |
| # See https://github.com/robbyrussell/oh-my-zsh/wiki/Themes | |
| ZSH_THEME="robbyrussell" |
The dplyr package in R makes data wrangling significantly easier.
The beauty of dplyr is that, by design, the options available are limited.
Specifically, a set of key verbs form the core of the package.
Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe.
Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R.
The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).
dplyr is organised around six key verbs:
These instructions are based on Mistobaan's gist but expanded and updated to work with the latest tensorflow OSX CUDA PR.
This is unmaintained, please visit Ben-PH/spacemacs-cheatsheet
SPC q q - quitSPC w / - split window verticallySPC w - - split window horizontallySPC 1 - switch to window 1SPC 2 - switch to window 2SPC w c - delete current window