Excerpt
This page describes how to run JupyterHub in a container on Pawsey systems with Slurm. This involves launching JupyterHub 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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Code Block

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

#!/bin/bash -l
# Allocate slurm resources, edit as necessary
#SBATCH --account=[your-project-name]
# Here we request the appropriate GPU partition on a system
#SBATCH --partition=work
# Since jupyterhub is not mpi enabled, we just use one task 
#SBATCH --ntasks=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
dir="${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 ${dir}
cd ${dir}

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

# Get the hostname of the Zeus
node
# 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 [ $port -lt 65535 ] ; do
  check=$( netstat -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
fi

# Load Singularity
module load singularity/3.8.6

# 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: $dir"
echo "*****************************************************"
echo ""
echo "*****************************************************"
echo "Terminate - from your laptop do:"
echo "kill \$( ps x | grep 'ssh.*-L *${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 ${dir}:/home/joyvan \
  -B ${dir}:${HOME} \
  ${imagename} \
  jupyter notebook \
  --no-browser \
  --port=${port} --ip=0.0.0.0 \
  --notebook-dir=${dir}

...

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Code Block

language	bash
theme	DJango
title	Terminal 1. Launching JupyterHub and extracting connection information
collapse	true

$ sbatch jupyter-notebook-one-singularity.slm
Submitted batch job 3528476
$ cat slurm-3528476.out
.
.
.
Writing manifest to image destination
Storing signatures
[34mINFO:   [0m Creating SIF file...
[34mINFO:   [0m Build complete: /scratch/pawsey0002/astott/jupyter-dir/singularity-cache/cache/oci-tmp/18ef2702c6a25bd26b81e7b6dc831adb2bc294ae7bc9b011150b8f4573c41d4a/datascience-notebook_latest.sif

*****************************************************
Setup - from your laptop do:
ssh -N -f -L 8888:z123:8888 astott@zeus<user>@setonix.pawsey.org.au
*****
The launch directory is: /scratch/pawsey0002/astott/jupyter-dirpawsey0001/...
*****************************************************

*****************************************************
Terminate - from your laptop do:
kill $( ps x | grep 'ssh.*-L *8888:z123:8888' | awk '{print $1}' )
*****************************************************

[I 04:38:34.503 NotebookApp] Writing notebook server cookie secret to /home/astott/.local/share/jupyter/runtime/notebook_cookie_secret
[I 04:38:36.677 NotebookApp] JupyterLab extension loaded from /opt/conda/lib/python3.7/site-packages/jupyterlab
[I 04:38:36.677 NotebookApp] JupyterLab application directory is /opt/conda/share/jupyter/lab
[I 04:38:37.605 NotebookApp] Serving notebooks from local directory: /group/pawsey0002/astott/jupyter-dir
[I 04:38:37.605 NotebookApp] The Jupyter Notebook is running at:
[I 04:38:37.605 NotebookApp] http://z123:8888/?token=3291a7b1e6ce7791f020df84a7ce3c4d2f3759b5aaaa4242
[I 04:38:37.605 NotebookApp]  or http://127.0.0.1:8888/?token=3291a7b1e6ce7791f020df84a7ce3c4d2f3759b5aaaa4242
[I 04:38:37.605 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 04:38:37.616 NotebookApp] 
    
    To access the notebook, open this file in a browser:
        file:///home/astott/.local/share/jupyter/runtime/nbserver-17-open.html
    Or copy and paste one of these URLs:
        http://z123:8888/?token=3291a7b1e6ce7791f020df84a7ce3c4d2f3759b5aaaa4242
     or http://127.0.0.1:8888/?token=3291a7b1e6ce7791f020df84a7ce3c4d2f3759b5aaaa4242

...

ssh -N -f -L 8888:z123:8888 astott@zeus<username>@setonix.pawsey.org.au

After this step, you can open up a web browser and use the address displayed in the output file to access your Jupyter notebook. In this example the address is:

...

Alternatively, you could go to the web address http://27.0.0.1:8888 or http://localhost:8888, and then when prompted insert the token string that comes after "?token=" above. (Note that your port number might differ from "8888".)

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Image RemovedImage Added

Figure 1. Jupyter authentication page

Note

title	Note:

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

...

Running a GPU-enabled container on GPU Pawsey systems (such as Topaz and Phase-2 Setonix) with Slurm is very similar to running a standard Jupyter notebook. The main differences are:

...

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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
# Allocate slurm resources, edit as necessary
#SBATCH --account=[your-project-name]
# Here we request the appropriate GPU partition on a system
#SBATCH --partition=gpuq
# Be aware that the request for GPU resources may change in later versions of slurm
#SBATCH --gres=gpu:1
# Since jupyterhub is not mpi enabled, we just use one task 
#SBATCH --ntasks=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
dir="${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 ${dir}
cd ${dir}

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

# Get the hostname of the Zeus node
# 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 [ $port -lt 65535 ] ; do
  check=$( netstat -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
fi

# Load Singularity
module load singularity/3.8.6

# Load CUDA and set environment variable for Singularity
module load cuda
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: $dir"
echo "*****************************************************"
echo ""
echo "*****************************************************"
echo "Terminate - from your laptop do:"
echo "kill \$( ps x | grep 'ssh.*-L *${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 ${dir}:/home/joyvan \
  -B ${dir}:$HOME \
  ${imagename} \
  jupyter notebook \
  --no-browser \
  --port=${port} --ip=0.0.0.0 \
  --notebook-dir=${dir}

...

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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")

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