## 23rd January 2017
At the time of writing, the pip install of toree is not compatible with spark 2.x. We need to use the master branch from git.
sudo apt install openjdk-8-jdk-headless
from threading import Thread | |
from time import sleep | |
import uuid | |
from dask.distributed import LocalCluster, Client | |
import dask.dataframe as dd | |
import pandas as pd | |
import pyspark | |
source: { | |
type: hive | |
query:""" | |
SELECT | |
id_listing as listing | |
, dim_city as city | |
, dim_country as country | |
, dim_is_active as is_active | |
, CONCAT(ds, ' 23:59:59.999') as ts | |
FROM |
Updated 4/11/2018
Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.
CentOS, Ubuntu, Slackware, etc. Whatever Linux-based OS it is, you can create a bootable USB for it by using a Mac.
Download it, copy it, whatever it takes to prepare that Linux-based OS .iso file
# Eat up some memory until 30% of system total | |
import psutil, os | |
nb_process_id = os.getpid() | |
p = psutil.Process(nb_process_id) | |
from multiprocessing import Pool | |
import string | |
import random |
# Python Function App to Linux on Azure | |
# Build a Python function app and deploy it to Azure as a Linux function app. | |
# Add steps that analyze code, save build artifacts, deploy, and more: | |
# https://docs.microsoft.com/azure/devops/pipelines/languages/python | |
trigger: | |
- trunk | |
variables: | |
# Azure Resource Manager connection created during pipeline creation |