slurminade 0.5.2

A decorator-based slurm runner

slurminade

slurminade makes using the workload manager slurm with Python beautiful. It is based on 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 but you do not need to install anything, just make sure the same Python-environment is available the nodes (usually the case in a proper slurm setup).

Please check the documentation of 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.

A simple script could look like this:

import slurminade

slurminade.update_default_configuration(partition="alg")  # 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())`

# 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__":
    jid = prepare.distribute()

    with slurminade.Batch(max_size=20) as batch:  # automatically bundles up to 20 tasks
        # run 100x f after `prepare` has finished
        for i in range(100):
            f.wait_for(jid).distribute(i)  # no job id while in batch!

        # clean up after the previous jobs have finished
        jids = batch.flush()  # flush returns a list with all job ids.
        clean_up.wait_for(jids).distribute()

:warning: Always use Batch when distributing many tasks. Otherwise, you DDoS your slurm.

We recommend to use slurminade with conda. We have not tested it with other virtual environments.

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

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: 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: System calls

import slurminade
import os
@slurminade.slurmify()
def run_shell_command():
    os.system("complex call")
    # BAD! The system call will run outside of slurm! The slurm task directly terminates.

instead use

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

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

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.

Error: Complex objects as arguments

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

{
  "partition": "alg"
}

Debugging

You can use

import slurminade

slurminade.set_dispatcher(slurminade.TestDispatcher())

to see the serialization or

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:

• batch.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 slurified functions.
• guard.py: Contains code to prevent you accidentally DDoSing your infrastructure.
• options.py: Contains a simple data structure to save slurm options.

Changes

• 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.