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Data Science Lab Cluster Control

This configuration is only valid on fs.dslc.liacs.nl.

Cluster access

To access fs.dslc.liacs.nl you must first be logged into one of the universities services and ssh into the cluster from a secure environment.

# i.e. ssh to silver server 
ssh <YOUR_STUDENT_NUMBER>@silver.liacs.nl  
# Example: ssh [email protected]

# ssh to ds cluster from silver server
ssh fs.dslc.liacs.nl

DS-ctl

This repository contains a dsctl.sh file which can simply setup a standalone Spark cluster. You can easily clone this repo in the root of your cluster's master node by running:

git clone https://github.com/maminio/dsctl

If git is not present you can:

wget https://raw.githubusercontent.com/maminio/dsctl/master/dsctl.sh -O dsctl.sh

To view all the available commands run:

bash ./dsctl.sh -h
Usage: ./dsctl.sh [option]
init                      Setup all initial software and packages.
python_setup              Setup all initial software and packages.
start_master              Start master node of the cluster
start_worker              Start one worker, to start all workers at once please run start_workers
start_workers             Start all worker nodes
start                     Start both the master and worker nodes.
stop_all                  Stop all current nodes.

Spark Cluster setup

Navigate to dsctl folder and run the below command to initialize all the prerequizite packages and softwares.

bash ./dsctl.sh init

This command will prompt to enter your password to setup and transfer all the shared files between master and worker nodes.

Python setup

To work with Pyspark or using jupyter, you need to install python. This file will only install Python 3.7.4.

To start the installation run:

bash ./dsctl.sh python_setup

This command will take a while to run as it will be building python from the source.

Starting/Stoping the Cluster

To star the cluster will the master node and all the available worker node you can simply run:

bash ./dsctl.sh start

To stop the cluster and all running nodes:

bash ./dsctl.sh stop_all

Connecting to the cluster

There are two options available to connect to the cluster.

  1. You can ssh port-forward the whole master node port (8085).
  2. Start Jupyter Notebook on the cluster and only port forward the Jupyter Notebook.

Port-forward Jupyter Notebook

  1. SSH to fs.dslc.liacs.nl
  2. Install Jupyter
python -m pip install jupyter
  1. Run jupyter notebook
python -m jupyter notebook --no-browser --port=9995
  1. From your local terminal ssh-port-forward to the cluster
local: 
ssh -L 9995:localhost:2366 <YOUR_STDENT_NUMBER>@silver.liacs.nl  
ssh -4 -N -L 2366:localhost:9995 <YOUR_STDENT_NUMBER>@fs.dslc.liacs.nl
  1. Open jupyter on your local browser: localhost:9995
# checking if a previous Spark session is active
from pyspark import SparkContext
from pyspark.sql import SQLContext
import warnings
try:
    # create SparkContext on all CPUs available: in my case I have 4 CPUs on my laptop
    sc = SparkContext(appName="SDDM", master='spark://fs.dslc.liacs.nl:8085')
    print("Just created a SparkContext")
    sqlContext = SQLContext(sc)
    print("Just created a SQLContext")
except ValueError:
    warnings.warn("SparkContext already exists in this scope")

Port-forward Master Node

  1. Open a terminal and ssh-port-forward to silver.
ssh -L 8085:localhost:2348 <YOUR_STDENT_NUMBER>@silver.liacs.nl
  1. SSH port-forward from silver to fs.dslc.liacs.nl
ssh -4 -N -L 2348:fs.dslc.liacs.nl:8085 <YOUR_STDENT_NUMBER>@fs.dslc.liacs.nl
  1. Connect your local Jupyter Notebook to the cluster
from pyspark import SparkContext
from pyspark.sql import SQLContext
import warnings
try:
    sc = SparkContext(appName="SDDM", master='spark://localhost:8085')
except ValueError:
    warnings.warn("SparkContext already exists in this scope")