CommunityData:Hyak

Hyak is the University of Washington's high performance computing (HPC) system. The CDSC has purchased a number of "nodes" on this system, which you will have access to as a member of the group.

To use Hyak, you must first have a UW NetID, access to Hyak, and a two factor authentication token which you will need as part of getting setup. The following links will be useful.
 * CommunityData:Klone (for the new hyak nodes).
 * CommunityData:Hyak setup
 * CommunityData:Hyak software installation
 * CommunityData:Hyak Spark
 * CommunityData:Hyak Mox migration
 * CommunityData:Hyak Ikt (Deprecreated)
 * CommunityData:Hyak Datasets

There are a number of other sources of documentation beyond this wiki:


 * Hyak User Documentation

General Introduction to Hyak
The UW Research Computing Club has put together this excellent 90 minute training video that introduces Hyak. It's probably a good place to start for anybody trying to get up-and-running on Hyak.

Setting up SSH
When you connect to SSH, it will ask you for a key from your token. Typing this in every time you start a connection be a pain. One approach is to create an .ssh config file that will create a "tunnel" the first time you connect and send all subsequent connections to Hyak over that tunnel. Some details in the Hyak documentation.

I've added the following config to the file  on my laptop (you will want to change the username):

Host hyak mox2.hyak.uw.edu User  HostName mox2.hyak.uw.edu ControlPath ~/.ssh/master-%r@%h:%p ControlMaster auto ControlPersist yes Compression yes

If your SSH connection becomes stale or disconnected (e.g., if you change networks) it may take some time for the connection to time out. Until that happens, any connections you make to hyak will silently hang. If your connections to ssh hyak are silently hanging but your Internet connection seems good, look for ssh processes running on your local machine with:

ps ax|grep hyak

If you find any, kill them with. Once that is done, you should have no problem connecting to Hyak.

X11 forwarding
You may also want to add these two lines to your Hyak  (indented under the line starting with "Host"):

ForwardX11 yes ForwardX11Trusted yes

These lines will mean that if you have "checked out" an interactive machine, you can ssh from your computer to Hyak and then directly through an addition hop to the machine (like ssh n2347). Those ForwardX11 lines means if you graph things on this session, they will open on your local display.

Connecting to Hyak
To connect to Hyak, you now only need to do:

ssh hyak

It will prompt you for your UWNetID's password. Once you type in your password, you will have to respond to a 2-factor authentication request.

Setting up your Hyak environment
Everybody who uses Hyak as part of our group must add the following line to their  file on Hyak:

If you don't have a preferred terminal-style text editor, you might start with nano --, arrow down, paste in the 'source....' text from above, then ^O to save and ^X to exit. You'll know you were successful when you type  and see the 'source....' line at the bottom of the file. Copious information about use of a terminal-style text editor is available online -- common options include nano (basic), emacs (tons of features), and vim (fast).

This line will load scripts that will initialize a good data science environment and set the umask so that the files and directories you create are readable by others in the group. Please do this immediately before you do any other work on Hyak. When you are done, you can reload the shell by logging out and back into Hyak or by running.

Storing Files
By default you have access to a home directory with a relatively small quota. There are several dozen terabytes of CDSC-allocated storage in  and you should explore that space. Typically we download large datasets to  (see the section on new datasets below), processed data in , and personal workspaces with the need for large data storage in.

Basic Commands
Once you have loaded load modern versions of R and Python and places Spark in your environment. It also provides a number of convenient commands for interacting with the SLURM HPC system for checking out nodes and monitoring jobs. Particularly important commands include any_machine

which attempts to check out a supercomputing node.

big_machine

Requests a node with 240GB of memory.

build_machine

Checks out a build node which can access the internet and is intended to be used to install software.

ourjobs Prints all the running jobs by people in the group.

myjobs

Displays jobs by members of the group.

Read the files in  to see how these commands are created (or run  ) as well as other features not documented here.

Anaconda
We recently switched to using Anaconda to manage Python on Hyak. Anaconda comes with the `conda` tool for managing python packages and versions. Multiple python environments can co-exist in a single Anaconda installation, this allows different projects to use different versions of Python or python packages, which can be useful for maintaining projects that use old versions.

By default, our shared setup loads a conda environment called `minimal_ds` that provides recent versions of python packages commonly used in data science workflows. This is probably a good setup for most use-cases, and allows everyone to use the same packages, but it can be even better to create different environments for each project. See the anaconda documentation for how to create an environment.

To learn how to install Python packages, see the Python packages installation instructions on this wiki.

SSH into compute nodes
The hyak wiki has instructions for how to enable ssh within hyak. Reproduced below:

You should be able to ssh from the login node to a compute node without giving a password. If it does not work then do below steps:


 * 1)   then press enter for each question. This will ensure default options.

Running Jobs on Hyak
When you first log in to Hyak, you will be on a "login node". These are nodes that have access to the Internet, and can be used to update code, move files around, etc. They should not be used for computationally intensive tasks. To actually run jobs, there are a few different options, described in detail in the Hyak User documentation. Following are basic instructions for some common use cases.

Interactive nodes
Interactive nodes are systems where you get a  shell from which you can run your code. This mode of operation is conceptually similar to running your code on your own computer, the difference being that you have access to much more CPU and memory. To check out an interactive node, run the  or   command from your login shell. Before running these commands, you will want to be in a  or   session so that you can start your job, and log off without having to worry about your job getting terminated.

At a given point of time, unless you are using the  (formerly the  ) queue, our entire group can collectiveley have one instance of   and three instances of   running at the same time. You may need to coordinate over IRC if you need to use a specific node for any reason.

Killing jobs on compute nodes
The Slurm scheduler provides a command called scancel to terminate jobs. For example, you might run queue_state from a login node to figure out the ID number for your job (let's say it's 12345), then run scancel --signal=TERM 12345 to send a SIGTERM signal or scancel --signal=KILL 12345 to send a SIGKILL signal that will bring job 12345 to an end.

Parallel R
The nodes on Hyak have 28 CPU cores. These may help in speeding up your analysis significantly. If you are using R functions such as, there are parallelized equivalents (e.g.  ) which can take advantage of all the cores and give you a 2800% boost! However, something to be aware of here is your code's memory requirement—if you are running 28 processes in parallel, your memory needs can also go up to 28x, which may be more than the ~200GB that the  node will have. In such cases, you may want to dial down the number of CPU cores being used—a way to do that globally in your code is to run the following snippet of code before calling any of the parallelized functions.

More information on parallelizing your R code can be found in the package documentation.

Using the Checkpoint Queue
Hyak has a special way of scheduling jobs using the checkpoint queue. When you run jobs on the checkpoint queue, they run on someone else's hyak node that they aren't using right now. This is awesome as it gives us a huge amount of free (as in beer) computing. But using the checkpoint queue does take some effort, mainly because your jobs can get killed at any time if the owner of the node checks it out. So if you want to run a job for more than a few minutes on the checkpoint queue it will need to be able to "checkpoint" by saving it's state periodically and then restarting.

This would be a pain to do manually, fortunately, we have    which can automatically checkpoint and resume most programs.

Nate's working got dmtcp working for arbitrary scripts, and also with wikiq using parallel_sql.

dmtcp 3.0 is installed on Mox.

This will make more sense if you know that dmtcp works by starting a coordinator process which is responsible for pausing and saving the checkpointed process. A tutorial on dmtcp with slurm from USC has a bash function for starting the coordinator called. Nate added this function to the shared .bashrc. So it should be available in your environment on Mox.

Starting a checkpoint queue job
To start a checkpoint queue job we'll use  instead of srun. See the documentation for a refresher starting hpc jobs using sbatch.

To request a job on the checkpoint queue put the following in the top of your  script.

#SBATCH --export=ALL #SBATCH --account=comdata-ckpt #SBATCH --partition=ckpt

You'll might have other stuff in your SBATCH script to request a certain number of cores or memory. Those will matter when we run  below, but here they can be whatever they would be if you were running an   job on one of our machines. The next thing you need to do specifically for a  job is to run. This function takes care of making sure that we start a coordinator using the right set of ports and temporary files. We still need to pass in the interval that we want checkpoints. The bigger this interval the faster your job will run but the more work will be lost when it's interrupted.

start_dmtcp_coordinator -i 600 #checkpoint every 10 minutes

Next you need to run your job in a special way so that it is managed by  and restarted if it gets interrupted.

# The restart script is created by dmtcp_launch after initialization if [ -x dmtcp_restart_script.sh ]; then bash dmtcp_restart_script.sh   else # On first pass, run program under DMTCP dmtcp_launch --rm $your_script.sh	# must run interpreter for scripts fi This works because  is created when you launch your job using. If that script exists your job should run it instead of your job.

There are options that you can pass to  that can be important. In particular  and   modify how IO is checkpointed.

Running wikiq with dmtcp and parallel_sql
 WARNING! Follow the below example at your own risk. It may not work reliably and can lead to missing data! Try using dmtcp without parallel-sql instead. 

To run wikiq with parallelsql the following need to be arranged:


 * 1) A shell script for each dumpfile that makes a workspace for   to keep it's data and restart script.
 * 2) These shell scripts loaded in.
 * 3) A   script that gets a checkpoint node and starts running jobs from.
 * 4) You need to restart jobs that get interrupted using parallel sql.

You first need to set up parallel_sql on Hyak: https://wiki.cac.washington.edu/display/hyakusers/Hyak+parallel-sql#Hyakparallel-sql-Usingparallel-sql

Nate made a python script that generates the scripts and makes a file with all the scripts. Notice that each dumpfile gets a script, it's own checkpoint directory, and a line in

We also need an sbatch script as.

Next load the scripts into

module load parallel_sql cat wikiq_parallel_jobs.sh | psu --load

We can now fire up a whole bunch of checkpoint nodes. The limit is technically 2000! But let's just ask for 10 nodes :)

for job in $(seq 1 10); do sbatch parallel_sql_job.sh; done

If our jobs get interrupted we'll need to run  to set them back into avail state. We can run a little script running on a login node to do this automatically every minute or so.

That's it! Unleash the power of the checkpoint queue! Reach out to Nate if you try this and have problems or if you have any questions!

New Datasets
If you want to download a new dataset to Hyak you should first check to ensure that is enough space on the current allocation (e.g., with . If there is not enough space in our allocation, contact Mako about getting our allocation increased. It should be fast and easy.

If there is enough space, you should download data to.

Once you have finished downloading, you should set all the files you have downloaded as read only to prevent people from accidently creating new files, overwriting data, etc. You can do that with the following commands:

Help! I'm over CPU quota and Hyak is angry!
Don't panic. Everyone has done this at least once. Mako has done it dozens of times. It is a little bit difficult to deal with but can be solved. You are not in trouble.

The usual reason for this to happen is because you've accidentally run something on a login node that ought to be run on a compute node. The solution is to find the badly behaved process and then use kill to kill the process.

If it's a script or command on your commandline, Ctrl-c to kill it. If you backgrounded it, type  to foreground it and then Ctrl-c. But if you ran parallel, you'll need to kill parallel itself.

will show you all the things you are running (or have someone else run it for you if the spam is so terrible you can't get a command to run). The first column has the usernames, the second column has the process IDs, the last column has the things you're running.



In the screenshot, the red is the user name being grepped for. At the end of the line the last three entries are the time (in hyak time, type date if you want to compare hyak time to your time), then how much CPU time something has consumed, then a little diagram of parent and child processes. You want parallel (in the example, 9977).

Killing the child process (in the example, 9992) won't likely help because parallel will just go on to the next task you queued up for it. You will need to run something like:

My R Job is getting Killed
First, make sure you're running on a compute node (like n2344) or else the int_machine and don't use a --time-min flag -- there seems to be a bug with --time-min where it evicts jobs incorrectly.

Second, see if you can narrow down where in your R code the problem is happening. Kaylea has seen it primarily when reading or writing files, and this tip is from that experience. Breaking the read or write into smaller chunks (if that makes sense for your project) might be all it takes.