Excerpt
This page describes how to run JupyterLab in a container on Pawsey systems with Slurm. This involves launching JupyterLab and then connecting to the Jupyter server.

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For this example, we're going to be using the jupyter/datascience-notebook (external site) Docker image. It provides a Conda environment with a large collection of common Python packages (including NumPy, SciPy, Pandas, Scikit-learn, Bokeh and Matplotlib), an R environment (with the tidyverse (external site) packages), and a Julia environment. All of these are accessible via a Jupyter notebook server.

This Docker image ships with a startup script that allows for a number of runtime options to be specified. Most of these are specific to running a container using Docker; we will focus on how to run this container using Singularity.

The datascience-notebook image has a default user, jovyan, and it assumes that you will be able to write to /home/jovyan. When you run a Docker container via Singularity, you will be running as your Pawsey username inside the container, so we won't be able to write to /home/jovyan. Instead, we can mount a specific directory (on Pawsey's filesystems) into the container at /home/jovyan. This will allow our Jupyter server to do things like save notebooks and write checkpoint files, and those will persist on Pawsey's filesystem after the container has stopped.

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width	900px

Code Block

language	bash
theme	Emacs
title	Listing 1. Slurm script for running JupyterHub in a container
collapse	true

#!/bin/bash -l
# Allocate slurm resources, edit as necessary
#SBATCH --account=[your-project-name]
# Here we request the appropriate partition for the system
#SBATCH --partition=work
# Since jupyterlab is not mpi enabled, we just use one task 
#SBATCH --ntasks=1
#SBATCH --mem=20GB
#SBATCH --time=02:00:00
#SBATCH --job-name=jupyter_notebook
#SBATCH --export=NONE

# Set our working directory
# This is the directory we'll mount to /home/jovyan in the container
# Should be in a writable path with some space, like /scratch
jupyterDir="${MYSCRATCH}/jupyter-dir"

# Set the image and tag we want to use
image="docker://jupyter/datascience-notebook:latest"

# You should not need to edit the lines below

# Prepare the working directory
mkdir -p ${jupyterDir}
cd ${jupyterDir}

# Get the image filename
imagename=${image##*/}
imagename=${imagename/:/_}.sif

# Get the hostname 
# We'll set up an SSH tunnel to connect to the Juypter notebook server
host=$(hostname)

# Set the port for the SSH tunnel
# This part of the script uses a loop to search for available ports on the node;
# this will allow multiple instances of GUI servers to be run from the same host node
port="8888"
pfound="0"
while [ $port -lt 65535 ] ; do
  check=$( ss -tuna | awk '{print $4}' | grep ":$port *" )
  if [ "$check" == "" ] ; then
    pfound="1"
    break
  fi
  : $((++port))
done
if [ $pfound -eq 0 ] ; then
  echo "No available communication port found to establish the SSH tunnel."
  echo "Try again later. Exiting."
  exit
else
  echo "Port to use is port=${port}"
fi

# Load Singularity
module load singularity/34.111.40-nompi
:0
# Pull our image in a folder
singularity pull $imagename $image

echo "*****************************************************"
echo "Setup - from your laptop do:"
echo "ssh -N -f -L ${port}:${host}:${port} $USER@$PAWSEY_CLUSTER.pawsey.org.au"
echo "*****"
echo "The launch directory is: $jupyterDir"
echo "*****************************************************"
echo ""
echo "*****************************************************"
echo "Terminate - from your laptop do:"
echo "kill \$( ps x | grep 'ssh.*-L *${port}:${host}:${port}' | awk '{print \$1}' )"
echo "*****************************************************"
echo ""
 
# Launch our container
# and mount our working directory to /home/jovyan in the container
# and bind the run time directory to our home directory
singularity exec -C \
  -B ${jupyterDir}:/home/joyvan \
  -B ${jupyterDir}:${HOME} \
  ${imagename} \
  jupyter notebook \
  --no-browser \
  --port=${port} --ip=0.0.0.0 \
  --notebook-dir=${jupyterDir}

...

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Figure 1. Jupyter authentication page

Note

title	Note:

The information above is a notebook launched on setonix.pawsey.org.au. Ensure that you look at your output to select the correct machine.

Also note that the available version of the singularity module in the system may have changed, so that you need to adapt the script accordingly.

From the Jupyter notebook menu, you can create a new notebook and start from there. If you already count with a notebook that you want to execute/develop, you may need to copy it into the jupiter-dir first.

Clean up when you are finished

Once you have finished (and saved and exited from the Jupyter notebook instance):

From the Pawsey cluster, cancel your job with scancel.
From your own computer, kill the SSH tunnel, based on the command displayed in the output file:

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Use of the gpu partition on Setonix
Request a GPU to Slurm
Pass the environment variable CUDAROCM_HOME to Singularity
Run the container using the flag --nvrocm, to enable the GPU support from Singularity

...

Column

width	900px

Code Block

language	bash
theme	Emacs
title	Listing 2. Slurm script for running JupyterHub in a GPU-enabled container
collapse	true

#!/bin/bash -l
# This example is for GPUs on Setonix
# Allocate slurm resources, edit as necessary
#SBATCH --account=[your-project-name]
# Here we request the appropriate GPU partition on a system
#SBATCH --partition=gpu
# Be aware that the request for GPU resources may change in later versions of slurm
#SBATCH --nodes=1
#SBATCH --gpus-per-node=1
#SBATCH --time=02:00:00
#SBATCH --job-name=jupyter_notebook
#SBATCH --export=NONE

# Set our working directory
# This is the directory we'll mount to /home/jovyan in the container
# Should be in a writable path with some space, like /scratch
jupyterDir="${MYSCRATCH}/jupyter-dir"

# Set the image and tag we want to use
image="docker://jupyter/datascience-notebook:latest"

# You should not need to edit the lines below

# Prepare the working directory
mkdir -p ${jupyterDir}
cd ${jupyterDir}

# Get the image filename
imagename=${image##*/}
imagename=${imagename/:/_}.sif

# Get the hostname
# We'll set up an SSH tunnel to connect to the Juypter notebook server
host=$(hostname)

# Set the port for the SSH tunnel
# This part of the script uses a loop to search for available ports on the node;
# this will allow multiple instances of GUI servers to be run from the same host node
port="8888"
pfound="0"
while [ $port -lt 65535 ] ; do
  check=$( ss -tuna | awk '{print $4}' | grep ":$port *" )
  if [ "$check" == "" ] ; then
    pfound="1"
    break
  fi
  : $((++port))
else
  echo "Port to use is port=${port}"
done
if [ $pfound -eq 0 ] ; then
  echo "No available communication port found to establish the SSH tunnel."
  echo "Try again later. Exiting."
  exit
fi

# Load Singularity
module load singularity/34.111.40-nompi

# Load ROCm and set environment variable for Singularity
module load rocm/5.2.3
export SINGULARITYENV_CUDA_HOME=$CUDA_HOME

# Pull our image in a folder
singularity pull $imagename $image

echo "*****************************************************"
echo "Setup - from your laptop do:"
echo "ssh -N -f -L ${port}:${host}:${port} $USER@$PAWSEY_CLUSTER.pawsey.org.au"
echo "*****"
echo "The launch directory is: $jupyterDir"
echo "*****************************************************"
echo ""
echo "*****************************************************"
echo "Terminate - from your laptop do:"
echo "kill \$( ps x | grep 'ssh.*-L *${port}:${host}:${port}' | awk '{print \$1}' )"
echo "*****************************************************"
echo ""
 
# Launch our container
# and mount our working directory to /home/jovyan in the container
# and bind the run time directory to our home directory
singularity exec --nv -C \
  -B ${jupyterDir}:/home/joyvan \
  -B ${jupyterDir}:$HOME \
  ${imagename} \
  jupyter notebook \
  --no-browser \
  --port=${port} --ip=0.0.0.0 \
  --notebook-dir=${jupyterDir}

...

Column

width	900px

Code Block

language	py
theme	Emacs
title	Listing 3. Simple GPU-enabled Python code snippet
collapse	true

# key GPU library
from numba import cuda
import numpy as np

# define some kernels
@cuda.jit
def add_kernel(x, y, out):
    idx = cuda.grid(1)
    out[idx] = x[idx] + y[idx]

n = 4096
x = np.arange(n).astype(np.int32) # [0...4095] on the host
y = np.ones_like(x)               # [1...1] on the host
out = np.zeros_like(x)

# cuda commands to copy memory to the device 
d_x = cuda.to_device(x)
d_y = cuda.to_device(y)
d_out = cuda.to_device(out)

# run kernel
threads_per_block = 128
blocks_per_grid = 32
add_kernel[blocks_per_grid, threads_per_block](d_x, d_y, d_out)
cuda.synchronize()

# output result 
print(d_out.copy_to_host()) # Should be [1...4096]

External links

DockerHub
For information about runtime options supported by the startup script in the Jupyter image, see Common Features in the Jupyter Docker Stacks documentation
The Rocker Project ("Docker Containers for the R Environment")

Versions Compared

Old Version 13

New Version 14

Key

Clean up when you are finished

External links

Page Comparison

Versions Compared

Old Version 13

New Version 14

Key

Clean up when you are finished

External links