ddelange

from collections import deque
from concurrent.futures import ThreadPoolExecutor as _ThreadPoolExecutor


class ThreadPoolExecutor(_ThreadPoolExecutor):
    """Subclass with a lazy consuming imap method."""

    def imap(self, fn, *iterables, timeout=None, queued_tasks_per_worker=2):
        """Ordered imap that consumes iterables just-in-time ref https://gist.github.com/ddelange/c98b05437f80e4b16bf4fc20fde9c999.

se7oluti0n

se7oluti0n

import sys
sys.path.append('../../facenet/src')

import facenet
import argparse
import os
import importlib
import tensorflow as tf
from tqdm import tqdm
import tensorflow.contrib.slim as slim

h4

from __future__ import with_statement
from alembic import context
from sqlalchemy import engine_from_config, pool
from logging.config import fileConfig

from models import Base

config = context.config

fileConfig(config.config_file_name)

luiscape

#
#  Original solution via StackOverflow:
#    http://stackoverflow.com/questions/35802939/install-only-available-packages-using-conda-install-yes-file-requirements-t
#

#
#  Install via `conda` directly.
#  This will fail to install all
#  dependencies. If one fails,
#  all dependencies will fail to install.

michaelneale

stage "preparation"

node {
   parallel (
     phase1: { sh "echo p1; echo phase1" },
     phase2: { sh "echo p2; echo phase2" }
   )
  sh "echo 42 > data"
  stash includes: '*', name: 'binary'
   

ololobus

Apache Spark installation + ipython/jupyter notebook integration guide for macOS

Tested with Apache Spark 2.1.0, Python 2.7.13 and Java 1.8.0_112

For older versions of Spark and ipython, please, see also previous version of text.

Install Java Development Kit

whoshuu

#include <curl/curl.h>
#include <string>

size_t writeFunction(void *ptr, size_t size, size_t nmemb, std::string* data) {
    data->append((char*) ptr, size * nmemb);
    return size * nmemb;
}

int main(int argc, char** argv) {
    auto curl = curl_easy_init();

dnozay

#!/usr/bin/env python

# This example shows how to use multiprocessing with an initializer function.
# We take advantage of that to make the workers each have a custom initial
# load. And in particular example, we will make the workers sleep.
# Because we make them sleep different amounts, one of them is going to be
# ready much before the others, and thus we can guess easily which worker
# will do most of the work.
#
# see https://stackoverflow.com/questions/26693797/python-multiprocessing-process-number

kaka19ace

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
#  @file     gevent_zmq_redis_demo.py
#  @author   kaka_ace <[email protected]>
#  @date     Tue Oct 14 2014

"""
    fetures: there are two concurrent tasks
    1. From Redis server, Getting the notify msg with BLPOP operation,