Achilles Rasquinha achillesrasquinha

Using Search via Rest API in Frappe Framework

If you are interested in looking at how search is implemented in Frappe, have a look here and here Frappe uses python library Whoosh, it is an open-source, pure Python search engine library which stores its index in a file system.

frappe.utils.global_search in Frappe Framework allows you to perform full text search on any doctype using a custom FullTextSearch class. It uses a third-party library called Whoosh, which is a full text search library written in Python. The code for implementing frappe.utils.global_search is in the global_search.py file of Frappe Framework.

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.

	[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