linuxster

michaeldorner

import socket
import sys
import time
import struct

host = 'localhost'
port = 8888
buffersize = 1024
N = 1000000
server_address = (host, port) 

application2000

These commands are based on a askubuntu answer http://askubuntu.com/a/581497
To install gcc-6 (gcc-6.1.1), I had to do more stuff as shown below.
USE THOSE COMMANDS AT YOUR OWN RISK. I SHALL NOT BE RESPONSIBLE FOR ANYTHING.
ABSOLUTELY NO WARRANTY.

If you are still reading let's carry on with the code.

sudo apt-get update && \
sudo apt-get install build-essential software-properties-common -y && \
sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y && \

wesm

import gc
import os
import time

import numpy as np
import pandas as pd
from pyarrow.compat import guid
import pyarrow as pa
import pyarrow.parquet as pq
import snappy

mrocklin

linar-jether

andyweizhao

#!/bin/bash
## This gist contains step by step instructions to install cuda v9.0 and cudnn 7.3 in ubuntu 18.04

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

fsaintjacques

#!/usr/bin/env bash

set -eu

PWD="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
SRC_DIR=$(realpath "${PWD}/..")
CXX_SRC=${SRC_DIR}/cpp

# The following can be set
: "${CMAKE:=cmake}"

jasonrig

#!/bin/bash
#SBATCH --job-name spark-cluster
#SBATCH --account=qh82
#SBATCH --time=02:00:00
# --- Master resources ---
#SBATCH --nodes=1
#SBATCH --mem-per-cpu=1G
#SBATCH --cpus-per-task=1
#SBATCH --ntasks-per-node=1
# --- Worker resources ---

cjnolet

Comparing cuML K-Means API Against Scikit-learn & Dask-ML

First, a quick code example of K-Means in Scikit-learn

from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans

n_centers = 5

X, _ = make_blobs(n_samples=10000, n_centers=n_centers)