You need to configure your SLURM/PBS/LSF system with a ~/.myqueue/config.py file. The file describes what your system looks like: Names of the nodes, number of cores and other things.

The simplest way is to copy the file from a friend who has already written a configuration file for you supercomputer:

$ls ~/../*/.myqueue/config.py /home/you/../alice/.myqueue/config.py /home/you/../bob/.myqueue/config.py ...$ mkdir ~/.myqueue
$cp ~alice/.myqueue/config.py ~/.myqueue/  Here is an example configuration file: config = { 'scheduler': 'slurm', 'nodes': [ ('xeon24', {'cores': 24, 'memory': '255G'}), ('xeon16', {'cores': 16, 'memory': '63G'}), ('xeon8', {'cores': 8, 'memory': '23G'})]}  The configuration file uses Python syntax to define a dictionary called config. The dictionary can have the following key-value pairs: Key Description scheduler Name of scheduler required nodes Description of node types optional mpiexec MPI-run command optional parallel_python Parallel Python interpreter optional extra_args Extra arguments for submit command optional maximum_diskspace Maximum disk space optional notifications Notifications optional See details below. ## Guessing your configuration¶ Try the following command: $ mq config
...


It will try to guess your configuration. It can be a good starting point for a config.py file. You may need to help mq config a bit by giving it the scheduler name and/or the queue name (try mq config -h).

## Name of scheduler¶

The type of scheduler you are using must be 'slurm', 'pbs', 'lsf' or 'local'. The local scheduler can be used for testing on a system without SLURM/LSF/PBS. Start the local scheduler with:

$python3 -m myqueue.local  ## Description of node types¶ This is a list of ('<node-name>', <dictionary>) tuples describing the different types of nodes: ('xeon24', {'cores': 24, 'memory': '255GB'})  The node-name is what SLURM calls a partition-name and you would use it like this: $ sbatch --partition=<node-name> ... script


or like this with a PBS system:

\$ qsub -l nodes=<node-name>:ppn=... ... script


Each dictionary must have the following entries:

• cores: Number of cores for the node type.

• memory: The memory available for the entire node. Specified as a string such as '500GiB'. MyQueue understands the following memory units: MB, MiB, GB and GiB.

Other possible keys that you normally don’t need are:, extra_args, mpiargs (see the source code for how to use them).

The order of your nodes is significant. If you ask for $$N$$ cores, MyQueue will pick the first type of node from the list that has a core count that divides $$N$$. Given the configuration shown above, here are some example resource specifications:

48:12h: 2 $$\times$$ xeon24

48:xeon8:12h: 6 $$\times$$ xeon8

48:xeon16:12h: 3 $$\times$$ xeon16

## MPI-run command¶

By default, parallel jobs will be started with the mpiexec command found on your PATH. You can specify a different executable with this extra line in your config.py file:

config = {
...,
'mpiexec': '/path/to/your/mpiexec/my-mpiexec',
...}


## Parallel Python interpreter¶

If you want to use an MPI enabled Python interpreter for running your Python scripts in parallel then you must specify which one you want to use:

config = {
...,
'parallel_python': 'your-python',
...}


Use 'asap-python' for ASAP and 'gpaw python' for GPAW. For MPI4PY, you don’t need an MPI-enabled interpreter.

## Extra arguments for submit command¶

Add extra arguments to the sbatch, qsub or bsub command. Example:

config = {
...,
'extra_args': ['arg1', 'arg2'],
'nodes': [
('xeon24', {'cores': 24, 'extra_args': ['arg3', 'arg4']}),
...],
...}


would give <submit command> arg1 arg2 arg3 arg4.

## Maximum disk space¶

Some tasks may use a lot of disk-space while running. In order to limit the number of such task running at the same time, you can mark them in your workflow script like this:

task(..., diskspace=10)


and set a global maximum (note that the units are arbitrary):

config = {
...,
'maximum_diskspace': 200,
...}


This will allow only 200 / 10 = 20 tasks in the running or queued state. If you submit more that 20 tasks then some of them will be put in the hold state. As tasks finish successfully (done state), tasks will be moved from hold to queued. Tasks that fail will be counted as still running, so you will have to mq rm those and also remember to remove big files left behind.

config = {