Containershare: Cluster Notebooks

Let’s set up our story. You are a graduate student, a postdoc, or staff in lab, and you want to use Sherlock, Farmshare, or one of the other Stanford clusters to run an interactive notebook. This might mean Jupyter notebook, or even a notebook with Julia, or R. This tutorial will help teach you how to do that.

What are we doing today?

Today we are going to do the following:

  1. find a container that we like on the containershare.
  2. configure the forward tool for your cluster
  3. use the forward tool to start an interactive (or regular ssh) session
  4. access the notebook in a browser on your local machine, or shell into the node.

Watch the above in action in the video below, and keep reading to try it out!

Step 1: Find a Container

The purpose of the containershare is so that @vsoch can easily provide version controlled, pre-built containers that are ready to go for you! In fact, the containershare is not only a table on the web, it’s (on Stanford servers) an actual folder of the shared containers (hence the container share!) that is looked at before other avenues.

How does it work?

The containers in that table are provided on the Stanford clusters defined for the tool under @vsoch scratch, named by their hash. Here is a peek:

$ ls /scratch/users/vsochat/share -1

When you ask for a container, we use the name of the hash to find the file in the containershare! This way, if you are using a container provided in the table in the web interface (or custom built for you by @vsoch) you don’t need to pull or download or build anything. This is helpful because in some cases, the pull doesn’t work as expected on Sherlock.

$ CONTAINER=docker://vanessa/repo2docker-julia:9ea83b39fd
$ CONTAINER_NAME=$(echo -n "${CONTAINER}" | md5sum | awk '{ print $1 }').simg

In the above, the container on Docker Hub called vanessa/repo2docker-julia with tag 9ea83b39fd is named c92c3c3f3f9045bcd460466c952c1484.simg on the cluster containershare. We’ll see how this is found later in this tutorial.

How do we find the container we want?

For this example today, we are going to be using the container in the table called repo2docker-julia. It’s linked from the containershare table and has its own table here. If you wanted to learn about the guts of the container:

  • the “Manifest” column will show you image manifests with the commands used to build it
  • the “Inspect” column will show you packages installed, along with a complete file listing.

See the the container’s Github repository if you want to learn about specific usage and instructions for the container, or this post on the containershare if you want more detail on how the container is built, and the metadata and interface generated.

How do we reference the container we want?

We want to grab the “unique resource identifier” (called the “uri”) of the container, which is docker://vanessa/repo2docker-julia:9ea83b39fd. This is in reference to an image on Docker Hub that has a particular tag, and the tag also corresponds to a Github commit.

What’s wrong with “latest”?

The tag “latest” is easy to remember, but in that it always corresponds to the latest build, the “latest” that you get today may not be the same container that is tagged with “latest” tomorrow. For the sake of reproducibility, do not use “latest.”

The chosen one!

Let’s choose vanessa/repo2docker-julia:9ea83b39fd. This is a vanilla jupyter+julia container that you could use as a template for your own Jupyter+julia notebook! Want to skip Sherlock all together? You can do this too :) Here is how to run the container with Docker, on your local machine:

docker run --rm -it -p 8888:8888 vanessa/repo2docker-julia jupyter notebook --ip

Honestly? If I am using a notebook, this is my preference. The harsh reality is that setting up ssh forwarding, on an interactive node that you have to wait for, with a large number of dependencies with respect to libraries that are needed, is really hard. We can only do our best to provide tools that abstract this away. But I understand that this is needed, and so until we have a different kind of cluster that makes this easy, I (and your research computing team) are here to help you, to the best that we can. Let’s continue!

Step 2: Download the Repository

Next, we need the forward tool to run our container! Clone the repository, and remember the location. You will generate a parameters flie that you can go back to and use again.

git clone
cd forward

Take a look at the files we have to work with (I’ve removed documentation markdown ones):

$ tree -L 1
├──     (-- end a session
├──  (-- resume a started session
├── sbatches   (-- batch scripts you can run to forward a port to, organized by cluster
├── (-- just helper functions
├── hosts      (-- generate configurations for your ~/.ssh/config
├──       (-- run once to set up the tool
├──  (-- start a node (but without ssh forwarding / interactive notebook )
└──       (-- how you start a connection

For Jupyter, since we want an interactive session, we are first going to generate a file called that is specific to a cluster we are using (e.g., sherlock). We are then going to use with the uri of the container we found above and a batch script for slurm in the sbatches/sherlock folder to launch our notebook. Let’s do that.

Step 3: Generate your Parameters

Run the file to generate your parameters, which will ask you questions about the cluster configuration that you want.


If you are on Sherlock and want your libraries to have access to a gpu, then make sure to select “gpu” for a partition. Otherwise, your PI’s parition or the default of “normal” is ok. In the example below, I choose the “russpold” partition that corresponds to my lab in graduate school, just as an example.


This is the resource identifier. Notice that I’m just pushing enter through the first prompt to get the default “sherlock.”


This is a new addition added to the forward tool. If you have a containershare installed on your cluster, meaning a folder of shared containers (either by you or a cluster administrator) you can use a containershare-notebook.sbatch (or similar) script to get direct access to these containers. If you don’t care about containershare, just press enter through this setting.

We are going to do that in the tutorial today, and will show more detail. The CONTAINERSHARE variable basically refers to this table! If you are on Sherlock at Stanford, leave it to the default (/scratch/users/vsochat/share) and you’ll get the containers in the table. You can also create your own, whether that means downloading an existing one, or deploying a new interface and containers. But let’s not get sidetracked!

$ bash 

First, choose the resource identifier that specifies your cluster resoure. We
will set up this name in your ssh configuration, and use it to reference the resource (sherlock).

Resource identifier (default: sherlock) > 

sherlock username > vsochat

Next, pick a port to use.  If someone else is port forwarding using that
port already, this script will not work.  If you pick a random number in the
range 49152-65335, you should be good.

Port to use > 56845

Next, pick the sherlock partition on which you will be running your
notebooks.  If your PI has purchased dedicated hardware on sherlock, you can use
that partition.  Otherwise, leave blank to use the default partition (normal).

sherlock partition (default: normal) > russpold

A containershare ( is a library of
containers that are prebuilt for you, and provided on your cluster resource. if you
are at Stanford, leave this to be the default. If not, ask your HPC administrator
about setting one up, and direct them to

container shared folder (default for Stanford: /scratch/users/vsochat/share) > 

That’s it! The above produced the following details in “”

$ cat 

This is just a text file with variables that you are free to tweak.

Step 4: SSH Credentials

SSH credentials are defined in the file ~/.ssh/config, and will make it easy to connect to the cluster(s) of your choosing. You can look at this file on your computer (if it exists) to see what is set up already. Since I regularly use farmshare and sherlock, I have them defined:

$ cat ~/.ssh/config

Host sherlock
    User vsochat
    GSSAPIDelegateCredentials yes
    GSSAPIAuthentication yes
    ControlMaster auto
    ControlPersist yes
    ControlPath ~/.ssh/%l%r@%h:%p

Host farmshare
    User vsochat
    GSSAPIDelegateCredentials yes
    GSSAPIAuthentication yes
    ControlMaster auto
    ControlPersist yes
    ControlPath ~/.ssh/%l%r@%h:%p

If you don’t have the file, or want to update your configuration, the forward tools provides a set of scripts that will again prompt you for your username, and then spit out a configuration. Take a look in the hosts folder:

ls hosts/

You can run either of these scripts to print a configuration to your screen, and the configuration you can then add to your ~/.ssh/config.

$ bash hosts/ 

Sherlock username > tacocat
Host sherlock
    User tacocat
    GSSAPIDelegateCredentials yes
    GSSAPIAuthentication yes
    ControlMaster auto
    ControlPersist yes
    ControlPath ~/.ssh/%l%r@%h:%p

This comes down to opening the file in a text editor, and then copy pasting, and you are allowed to have more than one configuration in the file, as I do above. Once they are set, you can shell into either cluster very easily with commands like:

ssh sherlock
ssh farmshare

The first time will prompt you for credentials, but subsequent sessions will not (see how ControlPersist is set to yes?). If you don’t have a file, period, then you can generate the entire thing programatically like this:

bash hosts/ >> ~/.ssh/config

Step 5. Usage

That’s it! We are now ready to grab and use our container from containershare to launch a notebook! As a reminder, in the tutorial below, we are going to walk through using Jupyter notebook with Julia.

The Command

The basic command format looks like this:

bash <sbatch> <container>

Let’s break it down, like a bad disco dance.

The Job Submission Script, “sbatch”

The <sbatch> in the example above refers to an sbatch script in our “sbatches” folder, let’s take a look at what we have (at the writing of this tutorial).

$ tree sbatches/
├── farmshare                       (-- scripts intended for "farmshare"
│   ├── singularity-exec.sbatch
│   ├── singularity-jupyter.sbatch
│   └── singularity-run.sbatch
├── sherlock                           (-- scripts intended for sherlock
│   ├── containershare-notebook.sbatch (-- the same as singularity-notebook, but checks containershare folder first
│   ├── jupyter.sbatch
│   ├── py2-jupyter.sbatch
│   ├── py2-tensorflow.sbatch
│   ├── py3-jupyter.sbatch
│   ├── py3-tensorflow.sbatch
│   ├── singularity-exec.sbatch
│   ├── singularity-jupyter.sbatch
│   ├── singularity-notebook.sbatch
│   ├── singularity-run.sbatch
│   └── tensorboard.sbatch
├── singularity-exec.sbatch    (-- scripts that work across clusters
└── singularity-run.sbatch

Woweee, many more than with the original release of the tool! Since we are interested in Sherlock, and we are going to use a containershare container, we are going to use this one:

│   ├── containershare-notebook.sbatch

It’s actually the same as singularity-notebook but does a check first in our cluster shared folder for the image binary. If it’s already downloaded (because @vsoch has provided it for you!) we don’t need to wait for a pull. If you want to use a general notebook (that isn’t in the containershare) but is accessible via a docker uri (e.g., docker://<username>/<repository> then you would want to look at once of these scripts:

$ ls sbatches/sherlock/sing* -1

These are the names that we need 1. In fact, the names above without the extension of sbatch is how we are going to call the command! The script we want is going to work for a containershare notebook sbatches/sherlock/containershare-notebook.sbatch and we refer to it like this:

bash sherlock/containershare-notebook <container>

By the way, if you ever want to look at the script, usage, or other, just look at the script you are about to run! You should do this anyway :)

cat sbatches/sherlock/containershare-notebook.sbatch | less

The “less” ensures that you can start at the top of the file, and press ENTER to move down it. If you are using vim, you can type q to quit.

The Container

We are almost there! Let’s just plug in our container now. The final detail is that since this command is expecting a Singularity container (it could reference a full path to a Singularity container on your scratch, for example) we need to tell the software that it’s a Docker container, and use a docker uri (docker://)

$ bash sherlock/singularity-notebook docker://vanessa/repo2docker-julia:9ea83b39fd

Let’s walk through the output you see on the screen after issuing the command.

The Submission

At this point you’ve used to create and you are launching the container to create an interactive notebook on the Sherlock cluster. Let’s walk you through the commands that are printed to the screen, because they are added intentionally to help you.

1. Finding the SLURM batch script

The first thing that happens is finding the script that you want, on your local machine! Since there are several different levels for a script 2 the first few lines tell you the script being looked for, and when a match is found, you are told via the line that starts with Script:

$ bash sherlock/containershare-notebook docker://vanessa/repo2docker-julia:9ea83b39fd
== Finding Script ==
Looking for sbatches/sherlock/sherlock/containershare-notebook.sbatch
Looking for sbatches/sherlock/containershare-notebook.sbatch
Script      sbatches/sherlock/containershare-notebook.sbatch

The script sbatches/sherlock/containershare-notebook.sbatch will be used.

2. Check for running jobs

Next, we would want to check for an already existing notebook. If one is found, it would tell you to end it first.

== Checking for previous notebook ==
No existing sherlock/containershare-notebook jobs found, continuing...

3. Transfer the script to the cluster

The destination directory is a folder in your $HOME called forward-util where the sbatch script will be transferred. It needs to be on the cluster before it can be launched for use.

== Getting destination directory ==

== Uploading sbatch script ==
containershare-notebook.sbatch                                                                                                100% 2907     2.8KB/s   00:00    

If you hadn’t authenticated yet, it would happen during the above step.

4. Submission Command

Next (and this is very important for your learning, or future you that is submitting jobs on the cluster) the tool shows you the command that it uses to submit your script:

== Submitting sbatch ==
sbatch --job-name=sherlock/containershare-notebook --partition=russpold --output=/home/users/vsochat/forward-util/containershare-notebook.sbatch.out --error=/home/users/vsochat/forward-util/containershare-notebook.sbatch.err --mem=20G --time=8:00:00 /home/users/vsochat/forward-util/containershare-notebook.sbatch 56845 "docker://vanessa/repo2docker-julia:9ea83b39fd"
Submitted batch job 23366437

Understand the Command

This is so hugely useful for your learning. Let’s look closely at this command, and understand every piece of it.

  • sbatch is the executable that comes with SLURM to submit a job to the batch manager.
  • job-name refers to the name of your job so you can refer to it for future commands. We use the name of the script (sherlock/containershare-notebook) so it’s always linkable, and in fact, this is how we check to see if you have a notebook running already!
  • partition is a reference to a named group of machines. If your PI has some funding, he or she can invest in nodes and they get named as a “partition” of the cluster. If you want a gpu, you would specify the parition as “gpu.” The default is “normal.” This setting comes from the PARITION variable in your
  • output and error are exactly what they sound like - full paths to output and error files that you can look at if something goes wrong (or seems to be wrong). This should be one of the first places you look to debug.
  • mem The amount of memory requested for the job. This setting comes from the MEMORY variable in your
  • time: The HH:MM:SS requested for the job. The default is 8 hours, shown in the command above.

There are more details on SLURM’s documentation page if you are interested. The next part of the submission command is the script followed by arguments:

/home/users/vsochat/forward-util/containershare-notebook.sbatch 56845 "docker://vanessa/repo2docker-julia:9ea83b39fd"

This shows us the full path to the same sbatch file that was uploaded (now on the cluster!) and the inputs that we provided. What’s that weird number in the middle? That’s your PORT, a variable fed into the script that is also defined in your

5. Logs

Without being empowered to look at logs, you are pretty helpless if something goes wrong. This is why the tool next prints for you a command you can copy paste (in another terminal from your local machine) to look at logs. You could also log in to the cluster and view the files, but this might be faster:

== View logs in separate terminal ==
ssh sherlock cat /home/users/vsochat/forward-util/containershare-notebook.sbatch.out
ssh sherlock cat /home/users/vsochat/forward-util/containershare-notebook.sbatch.err

6. Waiting for allocation

Depending on the load of the cluster and your partition, the allocation of the job to a node can be speedy, or take a long time. We use a method called exponential backoff to keep checking if the node has been allocated.

== Waiting for job to start, using exponential backoff ==
Attempt 0: not ready yet... retrying in 1..
Attempt 1: not ready yet... retrying in 2..
Attempt 2: not ready yet... retrying in 4..
Attempt 3: not ready yet... retrying in 8..
Attempt 4: not ready yet... retrying in 16..
Attempt 5: not ready yet... retrying in 32..
Attempt 6: resources allocated to sh-02-21!..
notebook running on sh-02-21

7. Preparing the Container

Woot! We got the node! After the node is allocated, the container is identified, and the tool tells you the steps it is taking to find it. You’ll see that we start with a URI, the working directory for our notebook will be $SCRATCH, and for a first attempt we are going to look for the container in the containershare, which is located at /scratch/users/vsochat/share. The hash value is what we get when we do an md5sum of docker://vanessa/repo2docker-julia:9ea83b39fd.

== Connecting to notebook ==
Container is docker://vanessa/repo2docker-julia:9ea83b39fd
Notebook directory is /scratch/users/vsochat
Attempt 1: Looking for /scratch/users/vsochat/share/c92c3c3f3f9045bcd460466c952c1484.simg

What happens if we don’t find it in the containershare? Here is the order of operations for our search:

  1. is the identifier that the user provided a direct path to a file?
  2. is it a file in the containershare?
  3. is it a file already in your SINGULARITY_CACHE?

If the identifier isn’t found with any of the above avenues, it is pulled to your SINGULARITY_CACHEDIR. if the pull fails and we still don’t have a container, then we exit because something is up. If the container needed to be pulled, you might see the pull output print to the console after a pause.

8. Port Forwarding

Given we have a container, the script is going to set up port forwarding to ensure that the display from the node on Sherlock is forwarded to your computer.

== Setting up port forwarding ==
ssh -L 43453:localhost:43453 sherlock ssh -L 43453:localhost:43453 -N sh-02-21 &

9. Connecting to the Notebook

Connecting means that we have the node, and the job has been issued that will obtain and start the container.

Important if you need to pull the container, it might not be ready when the port forwarding starts, so you should look at the logs to determine when it is, and then go to the port.

At this point the script is conservative, and (again) assumes that something might go wrong. In a perfect world, things wouldn’t. But as researchers we know that bugs happen, and instead of pretending that they don’t, we should be prepared by knowing how to debug. Singularity isn’t perfect and has issues with pulling and running sometimes. For this case, the tool (again) tells you exactly how to look at error and output logs, from a terminal on your local machine:

== View logs in separate terminal ==
ssh sherlock cat /home/users/vsochat/forward-util/singularity-notebook.sbatch.out
ssh sherlock cat /home/users/vsochat/forward-util/singularity-notebook.sbatch.err

This is how you should debug if there are issues, and send the output to your research computing support for help!

Finally, you can follow the instructions to open your browser!

== Instructions ==
1. Password, output, and error printed to this terminal? Look at logs (see instruction above)
2. Browser: -> http://localhost:56845/...
3. To end session: bash sherlock/singularity-notebook

Please read them :) Note that for some containers, you will need to change the node address to localhost where you have it forwarded. If you don’t see your password? Issue the ssh command to see it in the log.

Read the messages that we print to you, my dear padwan!

For example, I didn’t see my password, so here is the command I used for this job on Sherlock, and I copy pasted this directly from my terminal:

$ ssh sherlock cat /home/users/vsochat/forward-util/containershare-notebook.sbatch.err
[I 14:53:10.546 NotebookApp] Writing notebook server cookie secret to /tmp/jupyter/notebook_cookie_secret
[I 14:53:12.008 NotebookApp] JupyterLab alpha preview extension loaded from /srv/conda/lib/python3.6/site-packages/jupyterlab
[I 14:53:12.008 NotebookApp] JupyterLab application directory is /srv/conda/share/jupyter/lab
[I 14:53:12.017 NotebookApp] nteract extension loaded from /srv/conda/lib/python3.6/site-packages/nteract_on_jupyter
[I 14:53:12.024 NotebookApp] Serving notebooks from local directory: /scratch/users/vsochat
[I 14:53:12.024 NotebookApp] 0 active kernels
[I 14:53:12.025 NotebookApp] The Jupyter Notebook is running at:
[I 14:53:12.025 NotebookApp]
[I 14:53:12.025 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 14:53:12.025 NotebookApp] 
    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:

And tada! There it is at the bottom. I would want to copy paste this into my browser and change the sh-02-21 to be localhost as instructed by the “Instructions.” That’s it! Then you have your notebook ready to go :) Note that when I click “new” I can start a new Julia notebook!


Different kinds of Containers

Let’s say that you built your own Docker container that runs some kind of notebook. It’s not on the containershare. How do you use it? You can use a slight variation of the containershare-notebook script, a script called singularity-notebook.

Docker Hub

An easy solution is to create an automated build (or just push) directly to Docker Hub, and then use an equivalent uri to reference your container.

bash sherlock/singularity-jupyter docker://<username>/<repository>:<tag>

Container Binary Image

You might have already done this, and then pulled the container to the cluster, and now it’s a file on your scratch. Guess what, you can use that too! On Sherlock we did this:

module use system
module load singularity
singularity pull --name julia.simg docker://vanessa/repo2docker-julia:9ea83b39fd

And now we have the container julia.simg in our $SCRATCH. We can use it with the same tool by specifying the full path of the container:

bash sherlock/singularity-notebook /scratch/users/vsochat/.singularity/julia.simg

Singularity Hub

If we were building a recipe from Github on Singularity Hub we could also use the shub:// uri:

bash sherlock/singularity-notebook shub://<username>/<repository>:<tag>

Remember that when you are done, you can end the job.

bash sherlock/singularity-notebook

This is all under development, and guided by your needs! If you have an issue, I’ll help you fix it. If you have an idea for a new container, I’ll build it for you. If you otherwise have questions or want another tutorial? Please reach out!

1: Note that “singularity-jupyter” is mostly the same as “singularity-notebook” but it has a pre-built container on @vsoch scratch space, whereas singularity-notebook expects you to provide the path or unique resource identifier for the container.
2: A script called “singularity-notebook” might exist for the sherlock and farmshare clusters, and have a general version, and we would look under “sherlock” first given that it’s the resource you’ve defined in your, then look up one level.