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 (scheduler is requred, the rest are optional):  Key Description type default scheduler Name of scheduler str nodes Description of node types list[tuple[str, dict[str, Any]]] [] mpiexec MPI-run command str 'mpiexec' parallel_python Parallel Python interpreter str 'python3' extra_args Extra arguments for submit command list[str] [] maximum_total_task_weight Task weight float inf default_task_weight Task weight float 0.0 notifications Notifications dict[str, str] {} 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. ## Task weight¶ In order to limit the number of tasks running at the same time, you can submit them like this: $ mq submit ... -R 24:2h:5  # sets weight to 5


(see Resources) or mark them in your workflow script like this:

run(..., weight=5)


and set a global maximum:

config = {
...,
...}


This will allow only 100 / 5 = 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.

One use case would be to limit the disk-space used by running tasks. Note that 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.

One can also change the default task weight of 0 to something else by setting the default_task_weight configuration variable.

config = {