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d-krupke / slurminade

A decorator-based slurm runner.
https://slurminade.readthedocs.io
MIT License
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python slurm slurm-utility

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slurminade - A decorator-based slurm runner for Python-code.

|PyPI version| |CI status| |License|

slurminade makes using the workload manager slurm <https://slurm.schedmd.com/documentation.html> with Python beautiful. It is based on simple_slurm <https://github.com/amq92/simple_slurm>, but instead of just allowing to comfortably execute shell commands in slurm, it allows to directly distribute Python-functions. A function decorated with @slurminade.slurmify(partition="alg") will automatically be executed by a node of the partition alg by just calling .distribute(yes_also_args_are_allowed). The general idea is that the corresponding Python-code exists on both machines, thus, the slurm-node can also call the functions of the original code if you tell if which one and what arguments to use. This is similar to celery <https://github.com/celery/celery>__ but you do not need to install anything, just make sure the same Python-environment is available on the nodes (usually the case in a proper slurm setup).

Please check the documentation of simple_slurm <https://github.com/amq92/simple_slurm>__ to get to know more about the possible parameters. You can also call simple_slurm directly by srun and sbatch (automatically with the configuration specified with slurminade).

slurminade has two design goals:

  1. Pythonic slurm: Allowing to use slurm in a Pythonic-way, without any shell commands etc.
  2. Compatibility: Scripts can also run without slurm. You can share a script and also people without slurm can execute it without any changes.

We use it to empirically evaluate optimization algorithms for research papers on hundreds of instances that can require 15min each to solve. With slurminade, we can distribute the workload by just changing a few lines of code in our local Python scripts (those that you use for probing and development before running big experiments). An example of such a usage can be found here: Example of an empirical algorithm performance study for graph coloring heuristics using slurminade and AlgBench <https://github.com/d-krupke/AlgBench/tree/main/examples/graph_coloring>. You will find the original runner <https://github.com/d-krupke/AlgBench/blob/main/examples/graph_coloring/02_run_benchmark.py>__ and the slurmified runner <https://github.com/d-krupke/AlgBench/blob/main/examples/graph_coloring/02b_run_benchmark_with_slurminade.py>, showing the simplicity of distributing your experiments with slurminade.

A simple script could look like this:

.. code:: python

import slurminade

slurminade.update_default_configuration( partition="alg", exclusive=True ) # global options for slurm

If no slurm environment is found, the functions are called directly to make scripts

compatible with any environment.

You can enforce slurm with slurminade.set_dispatcher(slurminade.SlurmDispatcher())

@slurminade.node_setup def setup(): print("I will run automatically on every slurm node at the beginning!")

use this decorator to make a function distributable with slurm

@slurminade.slurmify( constraint="alggen02" ) # function specific options can be specified def prepare(): print("Prepare")

@slurminade.slurmify() def f(foobar): print(f"f({foobar})")

@slurminade.slurmify() def clean_up(): print("Clean up")

if name == "main": prepare.distribute() slurminade.join() # make sure that no job runs before prepare has finished with slurminade.JobBundling(max_size=20): # automatically bundles up to 20 tasks

run 100x f after prepare has finished

       for i in range(100):
           f.distribute(i)

   slurminade.join()  # make sure that the clean up jobs runs after all f-jobs have finished
   clean_up.distribute()

If slurm is not available, distribute results in a local function call. Analogous for srun and sbatch (giving some extra value on top of just forwarding to simple_slurm).

.. warning:: Always use JobBundling when distributing many small tasks to few nodes. Slurm jobs have a certain overhead and you do not want to spam your infrastructure with too many jobs. However, function calls joined by JobBundling are considered as a single job by slurm, thus, not shared across nodes.

What are the limitations of slurminade? Slurminade reconstructs the environment by basically loading the code on the slurm node (without the __main__-part) and then calling the slurmified function with parameters serialized as JSONSs. This means that the code must be written in a common .py-file and all (distributed) function arguments must be JSON-serializable. Also, the function must not use any global state (e.g., global variables, file or database connections) initialized in the __main__-part. Additionally, the Python-environment must be available under the same path on the slurm node as slurminade will use the same paths on the slurm node to reconstruct the environment (allowing to use virtual environments).

Does slurminade work with Python 2? No, it is a Python 3 project. We tested it with Python 3.7 and higher.

Does slurminade work with Windows? Probably not, but I never saw a slurm cluster running on Windows. The (automatic) slurm-less mode should work on Windows. So your code will run, but all function calls will be local.

Are multi-file projects supported? Yes, as long as the files are available on the slurm node.

Does slurminade work with virtual environments? Yes. We recommend to use slurminade with conda <https://docs.conda.io/en/latest/>__. We have not tested it with other virtual environments.

Can I run my slurmified code outside a slurm environment? Yes, if you do not have slurm, the distributed functions are run as normal Python function calls. This means that you can share the same code with people that do not have slurm. It was important to us that the experimental evaluations we run on our slurm cluster can also be run in a common Python environment by reviewers without any changes.

Can I receive the return value of a slurmified function? No, the return value is not transmitted back to the caller. Note that the distribute-calls are non-blocking, i.e., the function returns immediately. Return values could be implemented via a Promise-object like for other distributed computing frameworks, but we did not see the need for it yet. We are usually saving the results in a database or files, e.g., using AlgBench <https://github.com/d-krupke/AlgBench>__.

Can I use command line arguments sys.argv in my scripts? Yes, but only in your __main__-part. The arguments are not transmitted to the slurm nodes as they are not part of the function call. You can add these as normal function arguments to your slurmified functions if needed. It is important that your global objects to not rely on these arguments for initialization, as the __main__-part is not executed on the slurm node. It is theoretically possible to transmit the arguments to the slurm node, but we did not see the need for it. Let us know if you need it and we may implement it.

The code is super simple and open source, don’t be afraid to create a fork that fits your own needs.

.. note:: Talk with you system administrator or supervisor to get the proper slurm configuration.

Installation

You can install slurminade with pip install slurminade.

Usage

You can set task specific slurm arguments within the decorator, e.g., @slurminade.slurmify(constraint="alggen03"). These arguments are directly passed to simple_slurm, such that all its arguments are supported.

In order for slurminade to work, the code needs to be in a Python file/project shared by all slurm-nodes. Otherwise, slurminade will not find the corresponding function. The slurmified functions also must be importable, i.e., on the top level. Currently, all function names must be unique as slurminade will only transmit the function’s name.

Don’t do:

Bad: Non blocking system calls


.. code:: python

   import slurminade
   import os
   import subprocess

   @slurminade.slurmify()
   def run_shell_command():
       # non-blocking system call
       subprocess.Popen("complex call")
       # BAD! The system call will run outside of slurm! The slurm task directly terminates.

instead use

.. code:: python

   import slurminade

   if __name__ == "__main__":
       slurminade.sbatch(
           "complex call"
       )  # forwards your call to simple_slurm that is better used for such things.

Bad: Global variables in the ``__main__`` part

.. code:: python

import slurminade

FLAG = True

@slurminade.slurmify() def bad_global(args): if FLAG: # BAD! Will be True because the main Part is not executed on the node. pass else: pass

if name == "main": FLAG = False bad_global.distribute("args")

instead do

.. code:: python

import slurminade

@slurminade.slurmify() def bad_global( args, FLAG ): # Now the flag is passed correctly as an argument. Note that only json-compatible arguments are possible. if FLAG: pass else: pass

Without the if, the node would also execute this part (slurminade will abort automatically)

if name == "main": FLAG = False bad_global.distribute("args", FLAG)

..

.. warning:: The same is true for any global state such as file or database connections. You can use global variables, but be wary of side effects.

Error: Complex objects as arguments



.. code:: python

   import slurminade

   @slurminade.slurmify()
   def sec_order_func(func):
       func()

   def f():
       print("hello")

   def g():
       print("world!")

   if __name__ == "__main__":
       sec_order_func.distribute(f)  # will throw an exception
       sec_order_func.distribute(g)

Instead, create individual slurmified functions for each call or pass a
simple identifier that lets the function deduce, what to do, e.g., a
switch-case. If you really need to pass complex objects, you could also
pickle the object and only pass the file name.

Default configuration
---------------------

You can set up a default configuration in
``~/.slurminade_default.json``. This should simply be a dictionary of
arguments for *simple_slurm*. For example

.. code:: json

   {
     "partition": "alg"
   }

The current version checks the following files and overwrites values in the following order:

1. ``~/.slurminade_default.json``
2. ``~/$XDG_CONFIG_HOME/slurminade/.slurminade_default.json``
3. ``./.slurminade_default.json``

Debugging
---------

You can use

.. code:: python

   import slurminade

   slurminade.set_dispatcher(slurminade.TestDispatcher())

to see the serialization or

.. code:: python

   import slurminade

   slurminade.set_dispatcher(slurminade.SubprocessDispatcher())

to distribute the tasks without slurm using subprocesses.

If there is a bug, you will directly see it in the output (at least for
most bugs).

Project structure
-----------------

The project is reasonably easy:

-  bundling.py: Contains code for bundling tasks, so we don’t spam slurm
   with too many.
-  conf.py: Contains code for managing the configuration of slurm.
-  dispatcher.py: Contains code for actually dispatching tasks to slurm.
-  execute.py: Contains code to execute the task on the slurm node.
-  function.py: Contains the code for making a function
   slurm-compatible.
-  function_map.py: Saves all the slurmified functions.
-  guard.py: Contains code to prevent you accidentally DDoSing your
   infrastructure.
-  options.py: Contains a simple data structure to save slurm options.

Changes
-------

- 1.1.2: Fixing some return types for job bundling. Still not perfect. Be aware of potentially breaking changes if you have been using the job ids.
- 1.1.1: Fixing bug when there is some output to stdout when loading the code, such as deprecation warnings.
- 1.1.0: Slurminade can now be called from iPython, too! `exec` has been renamed `shell` to prevent confusion with the Python call `exec` which will evaluate a string as Python code.
- 1.0.1: Dispatcher now return jobs references instead of job ids. This allows to do some fancier stuff in the future, when the jobs infos are only available a short time after the job has been submitted.
- 0.10.1: FIX: Listing functions will no longer execute setup functions.
-  0.10.0: `Batch` is now named `JobBundling`. There is a method `join` for easier synchronization. `exec` allows to executed commands just like `srun` and `sbatch`, but uniform syntax with other slurmified functions. Functions can now also be called with `distribute_and_wait`. If you call `python3 -m slurminade.check --partition YOUR_PARTITION --constraint YOUR_CONSTRAINT` you can check if your slurm configuration is running correctly.
-  0.9.0: Lots of improvements.
-  0.8.1: Bugfix and automatic detection of wrong usage when using ``Batch`` with ``wait_for``.
-  0.8.0: Added extensive logging and improved typing.
-  0.7.0: Warning if a Batch is flushed multiple times, as we noticed this to be a common indentation error.
-  0.6.2: Fixes recursive distribution guard, which seemed to be broken.
-  0.6.1: Bugfixes in naming
-  0.6.0: Autmatic naming of tasks.
-  0.5.5: Fixing bug guard bug in subprocess dispatcher.
-  0.5.4: Dispatched function calls that are too long for the command
   line now use a temporary file instead.
-  0.5.3: Fixed a bug that caused the dispatch limit to have no effect.
-  0.5.2: Added pyproject.toml for PEP compliance
-  0.5.1: ``Batch`` will now flush on delete, in case you forgot.
-  0.5.0:

   -  Functions now have a ``wait_for``-option and return job ids.
   -  Braking changes: Batches have a new API.

      -  ``add`` is no longer needed.
      -  ``AutoBatch`` is now called ``Batch``.

   -  Fundamental code changes under the hood.

-  <0.5.0:

   -  Lots of experiments on finding the right interface.

Contributors
-------------

This project is developed at the Algorithms Group at TU Braunschweig,
Germany. The lead developer is `Dominik Krupke <https://krupke.cc>`__.
Further contributors are Matthias Konitzny and Patrick Blumenberg.

.. |PyPI version| image:: https://badge.fury.io/py/slurminade.svg
   :target: https://badge.fury.io/py/slurminade
.. |CI status| image:: https://github.com/d-krupke/slurminade/actions/workflows/pytest.yml/badge.svg
   :target: https://github.com/d-krupke/slurminade

.. |License| image:: https://img.shields.io/github/license/d-krupke/slurminade
   :target: https://github.com/d-krupke/slurminade

Similar Projects
----------------

* This project is greatly inspired by `Celery <https://github.com/celery/celery>`__, but does not require any additional infrastructure except for slurm.
* If you want a more powerful library to, e.g., also distribute lambdas or functions with complex arguments, check out `submitit <https://github.com/facebookincubator/submitit/>`__. It is a great project, and we may use it as a backend in the future. However, it does not support the slurm-less mode and can easily hide non-deterministic errors. Slurminade on the other hand is restricted on purpose to write reproducible scripts that can also be run without slurm.