camullen

Installing KDE on WSL2

Inspiration: https://www.most-useful.com/kde-plasma-on-wsl.html

Setup

1. Update WSL
   • In windows command prompt run: wsl --update
2. Add systemd to ubuntu

• In ubuntu prompt run: sudo nano /etc/wsl.conf

ManojNair

# Example:
# spark.master                     spark://master:7077
# spark.eventLog.enabled           true
# spark.eventLog.dir               hdfs://namenode:8021/directory
# spark.serializer                 org.apache.spark.serializer.KryoSerializer
# spark.driver.memory              5g
# spark.executor.extraJavaOptions  -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"
spark.driver.extraClassPath        /glue/aws-glue-libs/jarsv1/*
spark.executor.extraClassPath      /glue/aws-glue-libs/jarsv1/*

hakonhagland

From Ubuntu: Install kvm

sudo apt-get install qemu-kvm libvirt-daemon-system libvirt-clients bridge-utils virt-manager
sudo systemctl enable libvirtd.service

Download Windows 10 ISO

https://www.microsoft.com/nb-no/software-download/windows10ISO

Start virtual manager

fhoering

import os
import sys
import numpy as np
from pyspark import SparkConf, SparkContext
 
def create_spark_context():
    pex_file = os.path.basename([path for path in sys.path if path.endswith('.pex')][0])
    conf = SparkConf() \
        .setMaster("yarn") \
        .set("spark.submit.deployMode", "client") \

fhoering

import json
import subprocess
import sys

from pex.fetcher import PyPIFetcher
from pex.pex_builder import PEXBuilder
from pex.resolvable import Resolvable
from pex.resolver import resolve_multi, Unsatisfiable, Untranslateable
from pex.resolver_options import ResolverOptionsBuilder

AdamBien

Ask questions and see you at March, 5th, 6.PM. CET: http://www.ustream.tv/channel/adambien

Also checkout recent episode:

archenroot

docker network ls
docker network create --driver bridge isolated_network   ## create ~ create custom network, bridge ~ use a bridge network, isolated_network ~ name of the custom network
docker network isolated_network
docker network inspect isolated_network
docker run -d --net=isolated_network --name nodeapp -p 3000:3000 abhinavkorpal/node  ## net ~ run container in network, mongodb ~ link to this containe by name

dghubble

%title: Kubeception %author: @dghubble

// Youtube: https://www.youtube.com/watch?v=tlUiQa2JYQU

-> Kubeception

-> Experiments with QEMU/KVM on Kubernetes <-

Brainiarc7

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

tomthetrainer

package org.deeplearning4j.examples.dataExamples;

import org.datavec.image.loader.NativeImageLoader;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;