Achilles Rasquinha achillesrasquinha

## frappe-bench-frappe-default-worker.service
[Unit]
Description="frappe-bench-frappe-default-worker"
PartOf=frappe-bench-workers.target

[Service]
User=revant
Group=revant
Restart=always
ExecStart=/home/revant/.local/bin/bench worker --queue default
StandardOutput=file:/home/revant/frappe-bench/logs/worker.log

## index.html
<!DOCTYPE html>
<html lang="en">
  <head>
    <script type="module">
      import { h, text, app } from "https://unpkg.com/hyperapp"

      app({
        init: () => 0,
        view: state =>
          h("main", {}, [

## ANSIColorFix.py
import os
import platform

if os.name == 'nt' and platform.release() == '10' and platform.version() >= '10.0.14393':
    # Fix ANSI color in Windows 10 version 10.0.14393 (Windows Anniversary Update)
    import ctypes
    kernel32 = ctypes.windll.kernel32
    kernel32.SetConsoleMode(kernel32.GetStdHandle(-11), 7)

## start_docker_registry.bash
#!/usr/bin/env bash

# install docker
# https://docs.docker.com/engine/installation/linux/ubuntulinux/

# install docker-compose
# https://docs.docker.com/compose/install/

# install letsencrypt
# https://www.digitalocean.com/community/tutorials/how-to-secure-nginx-with-let-s-encrypt-on-ubuntu-16-04

## harryPotterAliases
alias accio=wget
alias avadaKedavra='rm -f'
alias imperio=sudo
alias priorIncantato='echo `history |tail -n2 |head -n1` | sed "s/[0-9]* //"'
alias stupefy='sleep 5'
alias wingardiumLeviosa=mv

alias sonorus='set -v'
alias quietus='set +v'

## Makefile
# Example makefile with some dummy rules

.PHONY: all
## Make ALL the things; this includes: building the target, testing it, and
## deploying to server.
all: test deploy

.PHONY: build
# No documentation; target will be omitted from help display
build:

## webpack.config.js
var debug = process.env.NODE_ENV !== "production";
var webpack = require('webpack');

module.exports = {
  context: __dirname,
  devtool: debug ? "inline-sourcemap" : null,
  entry: "./js/scripts.js",
  output: {
    path: __dirname + "/js",
    filename: "scripts.min.js"

## simple_mlp_tensorflow.py
# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
# Similarly, for h * W_2 + b_2
import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

## Caffe Ubuntu 15.10.md

      
              1 file
            
          
              20 forks
            
          
              35 comments
            
          
              59 stars
            
          
                wangruohui
                / Caffe Ubuntu 15.10.md
            
            
              Last active
              February 28, 2023 09:36
            
              
                Compile and run Caffe on Ubuntu 15.10
              
          
    Ubuntu 15.10 have been released for a couple of days. It is a bleeding-edge system coming with Linux kernel 4.2 and GCC 5. However, compiling and running Caffe on this new system is no longer as smooth as on earlier versions. I have done some research related to this issue and finally find a way out. I summarize it here in this short tutorial and I hope more people and enjoy this new system without breaking their works.
Install NVIDIA Driver

The latest NVIDIA driver is officially included in Ubuntu 15.10 repositories. One can install it directly via apt-get.
sudo apt-get install nvidia-352-updates nvidia-modprobe

The nvidia-modprobe utility is used to load NVIDIA kernel modules and create NVIDIA character device files automatically everytime your machine boots up.
Reboot your machine and verify everything works by issuing nvidia-smi or running deviceQuery in CUDA samples.

  
## plot_decision_boundary.py
# Helper function to plot a decision boundary.
# If you don't fully understand this function don't worry, it just generates the contour plot below.
def plot_decision_boundary(pred_func):
    # Set min and max values and give it some padding
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    h = 0.01
    # Generate a grid of points with distance h between them
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    # Predict the function value for the whole gid
	[Unit]
	Description="frappe-bench-frappe-default-worker"
	PartOf=frappe-bench-workers.target

	[Service]
	User=revant
	Group=revant
	Restart=always
	ExecStart=/home/revant/.local/bin/bench worker --queue default
	StandardOutput=file:/home/revant/frappe-bench/logs/worker.log
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<script type="module">
	import { h, text, app } from "https://unpkg.com/hyperapp"

	app({
	init: () => 0,
	view: state =>
	h("main", {}, [
	import os
	import platform

	if os.name == 'nt' and platform.release() == '10' and platform.version() >= '10.0.14393':
	# Fix ANSI color in Windows 10 version 10.0.14393 (Windows Anniversary Update)
	import ctypes
	kernel32 = ctypes.windll.kernel32
	kernel32.SetConsoleMode(kernel32.GetStdHandle(-11), 7)
	#!/usr/bin/env bash

	# install docker
	# https://docs.docker.com/engine/installation/linux/ubuntulinux/

	# install docker-compose
	# https://docs.docker.com/compose/install/

	# install letsencrypt
	# https://www.digitalocean.com/community/tutorials/how-to-secure-nginx-with-let-s-encrypt-on-ubuntu-16-04
	alias accio=wget
	alias avadaKedavra='rm -f'
	alias imperio=sudo
	alias priorIncantato='echo `history \|tail -n2 \|head -n1` \| sed "s/[0-9]* //"'
	alias stupefy='sleep 5'
	alias wingardiumLeviosa=mv

	alias sonorus='set -v'
	alias quietus='set +v'
	# Example makefile with some dummy rules

	.PHONY: all
	## Make ALL the things; this includes: building the target, testing it, and
	## deploying to server.
	all: test deploy

	.PHONY: build
	# No documentation; target will be omitted from help display
	build:
	var debug = process.env.NODE_ENV !== "production";
	var webpack = require('webpack');

	module.exports = {
	context: __dirname,
	devtool: debug ? "inline-sourcemap" : null,
	entry: "./js/scripts.js",
	output: {
	path: __dirname + "/js",
	filename: "scripts.min.js"
	# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
	# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

	# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
	# where x' = [x \| 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
	# Similarly, for h * W_2 + b_2
	import tensorflow as tf
	import numpy as np
	from sklearn import datasets
	from sklearn.model_selection import train_test_split
	# Helper function to plot a decision boundary.
	# If you don't fully understand this function don't worry, it just generates the contour plot below.
	def plot_decision_boundary(pred_func):
	# Set min and max values and give it some padding
	x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
	y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
	h = 0.01
	# Generate a grid of points with distance h between them
	xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
	# Predict the function value for the whole gid