gambitier

#!/bin/bash

# install CUDA Toolkit v9.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb)
CUDA_REPO_PKG="cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb"
wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda-9-0

gambitier

## From Lynda.com course 'RSpec Testing Framework with Ruby'

describe 'Expectation Matchers' do
  
  describe 'equivalence matchers' do

    it 'will match loose equality with #eq' do
      a = "2 cats"
      b = "2 cats"
      expect(a).to eq(b)

gambitier

#!/bin/bash
## This gist contains step by step instructions to install cuda v9.0 and cudnn 7.2 in ubuntu 18.04

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

gambitier

"""Another way, note this one will load the whole array into memory ."""
from keras.preprocessing.image import ImageDataGenerator
import h5py
from keras.utils.io_utils import HDF5Matrix
seed=0
batch_size=32

# we create two instances with the same arguments
data_gen_args = dict( 
    rotation_range=90.,

gambitier

const AWS = require('aws-sdk');
const fs = require('fs')

const s3download = (bucketName, keyName, localDest) => {

    if (typeof localDest == 'undefined') {
        localDest = keyName;
    }

    let params = {

gambitier

Regex

{{[{]?(.*?)[}]?}}

Captures the value of the variable name in a handlebars template

{{> components/templates/email/includes/email-tr-spacer }}
{{# deliveryAddress }}

gambitier

export GITHUB_USER=magickatt
export GITHUB_TOKEN=secret
export GITHUB_REPOSITORY=magickatt/ContainerisingLegacyApplicationsTalk
git clone https://${GITHUB_USER}:${GITHUB_TOKEN}@github.com/${GITHUB_REPOSITORY}

gambitier

/**
 * Part of [Canivete](http://canivete.leofavre.com/#deepgroupby)
 * 
 * Groups the contents of an array by one or more iteratees.
 * Unlike Lodash [`groupBy()`](https://lodash.com/docs/4.17.4#groupBy),
 * this function can create nested groups, but cannot receive
 * strings for iteratees.
 */
const deepGroupBy = (collection, ...iteratees) => {
	let paths = collection.map(value => iteratees.map(iteratee => iteratee(value))),

gambitier

# This code is licensed under the terms of the MIT license https://opensource.org/license/mit
# Copyright (c) 2021 Marat Reymers

## Golden config for golangci-lint v1.59.1
#
# This is the best config for golangci-lint based on my experience and opinion.
# It is very strict, but not extremely strict.
# Feel free to adapt and change it for your needs.

run:

gambitier

Convert GIF files to WebP and WebM format to save space when archiving

In the following post I want to share something for users who have tons of GIFs and want to store or archive the information in a more space-saving way.

Disclaimer: I do not take responsibility for any damages to or losses of your files.

Do you also have dozens of GIF files and ever wondered why a little bit blinking or a movement of few seconds needs several MB storage? If it is your own content, you need them in original state or anything similar, you can stop reading any further.

GIF files have been invented a very long time ago (you remember 1987?) and weren't meant to be used for displaying an anime or short movie clip and the like. Therefore the format and compression (is no longer) isn't efficient enough.