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slurm-workflows: HPC workflow helpers for Slurm clusters

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slurm-workflows lets you run Python functions on a Slurm cluster without sbatch scripts written by hand. It provides an interface inspired by concurrent.futures. The interface launches long-lived workers inside pilot jobs. It then dispatches tasks to those workers. You pay Slurm's scheduling latency once per pilot job, not once per task.

Use it in three cases:

  • You have many Python tasks to run on one cluster allocation.
  • An exploration or a search has to spread across a pool of workers.
  • Per-worker state is expensive, and you want it to stay warm between tasks.

Installation

A run needs:

  • Python >= 3.12
  • Access to a Slurm cluster (sbatch, squeue, scancel on PATH)
  • A running ds-service server
pip install -U slurm-workflows

To set up on UVA's Rivanna cluster, read How to install slurm-workflows on Rivanna.

Usage

from ds_service_client import DsServiceServer
from slurm_workflows import SlurmPilotExecutor


def square(x):
    return x * x


SETUP_SCRIPT = """
module load gcc/14.2.0
conda activate my-env
"""

with DsServiceServer(interface="ib0") as ds_service:
    ds_service.wait_until_ready()

    with SlurmPilotExecutor("my-run", ds_service.address) as executor:
        # 1. Describe a job group. This submits nothing.
        executor.define_job_group(
            name="cpu",
            sbatch_args=["-A my_alloc", "-p standard", "-t 01:00:00"],
            setup_script=SETUP_SCRIPT,
        )

        # 2. Launch 4 pilot jobs of that job group.
        executor.scale_jobs("cpu", 4)

        # 3. Submit tasks to a named queue. That job group's workers claim them.
        tasks = [executor.submit("cpu", square, i) for i in range(100)]

        # 4. Block until every result is in.
        executor.wait(tasks, desc="squaring")

# The executor canceled every pilot job at the end of the block.
print(sum(task.output for task in tasks))
328350

You can submit tasks before the workers exist. They wait on the queue until a worker starts and claims them.

Documentation

Document What it covers
Computing pi on a Slurm cluster The main features of slurm-workflows, by creating a pool of workers to compute $\pi$.
Computing pi with a Sobol' QMC exploration Using ExploreSpaceSobolQMC to create a space filling design and evaluate it.
Optimizing Himmelblau's function Using OptimizeSpaceBotorch to run a batch Bayesian search.
How to install slurm-workflows on Rivanna Installing the package and the ds-service binary on Rivanna.
How to run the ds-service server Starting a ds-service server from the driver and binding it where workers can reach it.
How to keep per-worker state with actors Loading an expensive model or connection once per worker instead of once per task.
How to fold results across workers Using mapreduce to run one function over a whole collection and bring back a single value.
How to watch a run with swtop Following a live run from another shell, and keeping a record of one.
How to troubleshoot a failing run Finding the right log, and what each RuntimeError means.
How to resume a search Carrying a search on across a Slurm time limit.
SlurmPilotExecutor The executor, Task, RaiseOnError, and the job group options.
mapreduce Mapping an iterable across the pool, the fold contract, and what the call creates on the server.
What a run publishes The environment a task sees, the keys and series a run writes, the worker entry point, and the logs.
ExploreSpaceSobolQMC The Sobol' exploration, its study fields, and the results file.
OptimizeSpaceBotorch The batch Bayesian search, its study fields, and its stopping rule.
Search spaces IntRange, FloatRange and CategoricalRange, the objective contract, and what a failed evaluation does to a run.
swtop The CLI, the blocks on screen, and what the host and job readings measure.
About the pilot-job model Why pilot jobs, the three processes, where the driver runs, and which class to reach for.
About batch Bayesian optimization Why a search has rounds, where the fit runs, and when it is worth the overhead.
About what a run publishes Why a run is observable from outside itself, and the limits of that.

For contributors

Document What it covers
Developer notes The layout of the code, where each kind of documentation goes, and the conventions a change is held to.
Terminology The word this project uses for each concept, in prose and in identifiers, and where each word comes from.
How to run the tests Running the suite, what it mocks, and what it runs for real.

License

MIT. See LICENSE.

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