High Performance Computers
High Performance Computers¶
This page includes instructions and guidelines when deploying Dask on high performance supercomputers commonly found in scientific and industry research labs. These systems commonly have the following attributes:
Some mechanism to launch MPI applications or use job schedulers like SLURM, SGE, TORQUE, LSF, DRMAA, PBS, or others
A shared network file system visible to all machines in the cluster
A high performance network interconnect, such as Infiniband
Little or no node-local storage
Where to start¶
Most of this page documents various ways and best practices to use Dask on an HPC cluster. This is technical and aimed both at users with some experience deploying Dask and also system administrators.
The preferred and simplest way to run Dask on HPC systems today both for new, experienced users or administrator is to use dask-jobqueue.
However, dask-jobqueue is slightly oriented toward interactive analysis usage, and it might be better to use tools like dask-mpi in some routine batch production workloads.
Dask-jobqueue and Dask-drmaa¶
dask-jobqueue provides cluster managers for PBS, SLURM, LSF, SGE and other resource managers. You can launch a Dask cluster on these systems like this.
from dask_jobqueue import PBSCluster cluster = PBSCluster(cores=36, memory="100GB", project='P48500028', queue='premium', interface='ib0', walltime='02:00:00') cluster.scale(100) # Start 100 workers in 100 jobs that match the description above from dask.distributed import Client client = Client(cluster) # Connect to that cluster
Dask-jobqueue provides a lot of possibilities like adaptive dynamic scaling of workers, we recommend reading the dask-jobqueue documentation first to get a basic system running and then returning to this documentation for fine-tuning if necessary.
You can launch a Dask cluster using
mpiexec and the
dask-mpi command line tool.
mpirun --np 4 dask-mpi --scheduler-file /home/$USER/scheduler.json
from dask.distributed import Client client = Client(scheduler_file='/path/to/scheduler.json')
This depends on the mpi4py library. It only
uses MPI to start the Dask cluster and not for inter-node communication. MPI
implementations differ: the use of
mpirun --np 4 is specific to the
open-mpi MPI implementation installed through conda and linked
conda install mpi4py
It is not necessary to use exactly this implementation, but you may want to
verify that your
mpi4py Python library is linked against the proper
mpirun/mpiexec executable and that the flags used (like
--np 4) are
correct for your system. The system administrator of your cluster should be
very familiar with these concerns and able to help.
In some setups, MPI processes are not allowed to fork other processes. In this
case, we recommend using
--no-nanny option in order to prevent dask from
using an additional nanny process to manage workers.
dask-mpi --help to see more options for the
High Performance Network¶
Many HPC systems have both standard Ethernet networks as well as
high-performance networks capable of increased bandwidth. You can instruct
Dask to use the high-performance network interface by using the
keyword with the
dask-mpi commands or
interface= keyword with the dask-jobqueue
mpirun --np 4 dask-mpi --scheduler-file /home/$USER/scheduler.json --interface ib0
In the code example above, we have assumed that your cluster has an Infiniband
network interface called
ib0. You can check this by asking your system
administrator or by inspecting the output of
$ ifconfig lo Link encap:Local Loopback # Localhost inet addr:127.0.0.1 Mask:255.0.0.0 inet6 addr: ::1/128 Scope:Host eth0 Link encap:Ethernet HWaddr XX:XX:XX:XX:XX:XX # Ethernet inet addr:192.168.0.101 ... ib0 Link encap:Infiniband # Fast InfiniBand inet addr:126.96.36.199
Users often exceed memory limits available to a specific Dask deployment. In normal operation, Dask spills excess data to disk, often to the default temporary directory.
However, in HPC systems this default temporary directory may point to an network file system (NFS) mount which can cause problems as Dask tries to read and write many small files. Beware, reading and writing many tiny files from many distributed processes is a good way to shut down a national supercomputer.
If available, it’s good practice to point Dask workers to local storage, or
hard drives that are physically on each node. Your IT administrators will be
able to point you to these locations. You can do this with the
local_directory= keyword in the
dask-mpi ... --local-directory /path/to/local/storage
or any of the other Dask Setup utilities, or by specifying the following configuration value:
However, not all HPC systems have local storage. If this is the case then you
may want to turn off Dask’s ability to spill to disk altogether.
See this page for more information on Dask’s memory policies.
Consider changing the following values in your
to disable spilling data to disk:
distributed: worker: memory: target: false # don't spill to disk spill: false # don't spill to disk pause: 0.80 # pause execution at 80% memory use terminate: 0.95 # restart the worker at 95% use
This stops Dask workers from spilling to disk, and instead relies entirely on mechanisms to stop them from processing when they reach memory limits.
As a reminder, you can set the memory limit for a worker using the
dask-mpi ... --memory-limit 10GB
Launch Many Small Jobs¶
This section is not necessary if you use a tool like dask-jobqueue.
HPC job schedulers are optimized for large monolithic jobs with many nodes that all need to run as a group at the same time. Dask jobs can be quite a bit more flexible: workers can come and go without strongly affecting the job. If we split our job into many smaller jobs, we can often get through the job scheduling queue much more quickly than a typical job. This is particularly valuable when we want to get started right away and interact with a Jupyter notebook session rather than waiting for hours for a suitable allocation block to become free.
So, to get a large cluster quickly, we recommend allocating a dask-scheduler process on one node with a modest wall time (the intended time of your session) and then allocating many small single-node dask-worker jobs with shorter wall times (perhaps 30 minutes) that can easily squeeze into extra space in the job scheduler. As you need more computation, you can add more of these single-node jobs or let them expire.
Use Dask to co-launch a Jupyter server¶
Dask can help you by launching other services alongside it. For example, you
can run a Jupyter notebook server on the machine running the
process with the following commands
from dask.distributed import Client client = Client(scheduler_file='scheduler.json') import socket host = client.run_on_scheduler(socket.gethostname) def start_jlab(dask_scheduler): import subprocess proc = subprocess.Popen(['/path/to/jupyter', 'lab', '--ip', host, '--no-browser']) dask_scheduler.jlab_proc = proc client.run_on_scheduler(start_jlab)