I've been looking for the best Linux backup system, and also reading lots of HN comments.
Instead of putting pros and cons of every backup system I'll just list some deal-breakers which would disqualify them.
Also I would like that you, the HN community, would add more deal breakers for these or other backup systems if you know some more and at the same time, if you have data to disprove some of the deal-breakers listed here (benchmarks, info about something being true for older releases but is fixed on newer releases), please share it so that I can edit this list accordingly.
Svakih nekoliko tjedana u medijima nas netko uvjerava kako hrvatske firme ne mogu biti konkurentne zbog prevelikog poreznog opterećenja rada, pa se onda prijeti selidbom firmi u Irsku ili kuka kako se po pitanju oporezivanja trebamo ugledati na Poljsku "u kojoj su davanja na plaće 30 posto, dok su u Hrvatskoj 60 posto."
OK, hajdemo malo pogledat kako stoje stvari u tim zemljama u kojima se, izgleda, država magično financira kišom novčanica koje padaju s neba.
U tablici ispod uspoređeni su neto i bruto 2 iznosi prosječne plaće, dvostruke prosječne neto plaće i plaće u protuvrijednosti 2000 EUR za nekoliko zemalja u kategorijama interesantnima za usporedbu
Description: Setup GitHub Pages "gh-pages" branch and "master" branch as subfolders of a parent project folder ("grandmaster").
Author: Chris Jacob @_chrisjacob
Tutorial (Gist): https://gist.github.com/833223
##mailinglists
##list of speakers
| # Typical setup to include TensorFlow. | |
| import tensorflow as tf | |
| # Make a queue of file names including all the JPEG images files in the relative | |
| # image directory. | |
| filename_queue = tf.train.string_input_producer( | |
| tf.train.match_filenames_once("./images/*.jpg")) | |
| # Read an entire image file which is required since they're JPEGs, if the images | |
| # are too large they could be split in advance to smaller files or use the Fixed |
In the beginning there was NPM, and for a time it was good. Packages went forth and multiplied. The New Gods proclaimed the great demon Dependency Management had been slain. But The Old Gods knew better, for they had seen much and knew that the demon can never be killed, only held at bay.
The Old Gods were ignored. In the folly of a young age grew an abundance of packages and with them grew the scourge of dependency. In the depths beneath the earth, in a place beyond memory, the great demon stirred.
The first sign something was wrong was non-deterministic package version mismatches. “This is fine!” The New Gods declared. “A temporary setback, nothing more! We can fix it.” And so they introduced shrinkwrap, a lamp to combat the growing darkness.
But it proved to be too little, too late, and dusk continued to fall. The New Gods suffered their first major defeat at the [Battle of Left-pad](https://www.theregister.co.uk/2016/03/23/npm_left_
For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.
A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common s
