ksob

curl --request POST \
  --url https://api.bigcommerce.com/stores/8pfocjmdoi/v3/carts \
  --header 'accept: application/json' \
  --header 'content-type: application/json' \
  --header 'x-auth-client: fqun713qjaikqq4iedgzlcobgggfpo6' \
  --header 'x-auth-token: 47e0daxu5b2lz6eh0kixsrjy4bgyuwp' \
  --data '{"line_items":[{"quantity":1,"product_id":1140}]}'

ksob

{
  {
    obiekt: "pokój 4 osobowy z tarasem",
    cena_total: "1200",
    cena_details: {
      adults: "700",
      children: "200",
      udogodnienia: {
        oddzielny_taras: tak,
        dodatkowe_posiłki: tak

ksob

#!/bin/sh
#
# nginx - this script starts and stops the nginx daemon
#
# chkconfig:   - 85 15 
# description:  Nginx is an HTTP(S) server, HTTP(S) reverse \
#               proxy and IMAP/POP3 proxy server
# processname: nginx
# config:      /usr/local/nginx/conf/nginx.conf
# pidfile:     /usr/local/nginx/logs/nginx.pid

ksob

import json
import os
import sys
from glob import iglob
from pathlib import Path

import numpy as np
import tensorflow as tf
from keras.applications.xception import preprocess_input
from keras.engine.saving import load_model

ksob

https://drive.google.com/file/d/1PIRu6ceRFzWgGH2WtrzrQCa7ouPGjypL/view?usp=sharing

ksob

# dla Python 3.6, czyli tego który jest

1. Zainstaluj co potrzebne:
sudo apt install python3-pip
pip3 install 
pip3 install https://download.pytorch.org/whl/cpu/torch-1.0.1.post2-cp36-cp36m-linux_x86_64.whl
pip3 install torchvision
pip3 install fastai

ksob

https://anaconda.org/pytorch/torchvision-cpu/0.2.1/download/noarch/torchvision-cpu-0.2.1-py_2.tar.bz2
pip3 install --no-index --find-links file:///home/osboxes/torchvision-cpu-0.2.1-py_2.tar.bz2

ksob

1. Musisz zainstalować fast.ai, czyli bibliotekę python'a, najlepiej za pomocą anacond'y (wersja 4.6.2 lub wyższa).
(https://www.anaconda.com/distribution/#download-section)

Dalsze kroki dotyczą tylko instalacji za pomocą anacond'y.

2. Stwórz środowisko anacond'y na którym będzie instalowane fast.ai poleceniem:
  $ conda create -n moje_srodowisko python=3
(moje_srodowisko to dowolna nazwa twojego środowiska)

3. Aktywuj środowisko :

ksob

The task of sending messages / "toots" is delegated to background processing, 
so whenever a new message "toot" is received by the server, it is added to a queue. 
There is another thread/process that asynchornously reads from that queue and dispatches all these messages / "toots" to target users.
There is yet another thread/process that takes that dispatched messages and using WebSocket updates timelines 
inside of visitors' browsers in real time.
When you run those 5 threads what you have mentioned, the side was not responding (i.e. hanging) because your 
browser could not keep up with interpreting and displaying all those WebSocket updates. 
In real live a single end user operating a single browser don't have this problem because 
he doesn't receve such big amount of messages / "toots".
I observed how server behaves when 7 threads of the java seeder are being run against it and there was 

ksob

https://github.com/culturecode/culturecode