SSH cluster execution (Dask)

Run simulation tasks in parallel across multiple SSH-reachable hosts via Dask distributed. Requires the cluster extra (dask, distributed) and pre-authenticated SSH.

Upstream reference: https://github.com/omnetpp/opp_repl/blob/main/doc/cluster.md

Prerequisites

• pip install "opp_repl[cluster]".
• SSH key-based auth set up from the launching host to every

worker host (or an ssh-agent in the shell).

• Identical Python + opp_repl + dependency versions on every node

(Dask serializes live Python objects).

Setting up the cluster

from opp_repl.common.cluster import SSHCluster

c = SSHCluster(scheduler_hostname="node1.local", worker_hostnames=["node1.local", "node2.local"]) c.start()

Live dashboard: http://localhost:8797 (default).

Smoke-test:

c.run_gethostname(12)

"node1, node2, node2, node1, ..."

Distributing binaries

Build the project on each worker node before launching a cluster run. The compiled binaries must be present on every worker — either via a shared filesystem (NFS/SMB) or by building separately on each node:

p = get_simulation_project("aloha") build_project(simulation_project=p, mode="release")

Running on the cluster

Same run_simulations() function, extra parameters:

run_simulations(mode="release", config_filter="PureAlohaExperiment", scheduler="cluster", cluster=c)

Equivalent two-step form when you want to inspect the task list before executing:

mt = get_simulation_tasks(simulation_project=p, mode="release", filter="PureAlohaExperiment", scheduler="cluster", cluster=c) mt.run()

Exiting the Python session automatically stops the cluster.

CLI equivalent

opp_run_simulations -m release -t 1s \ --filter PureAlohaExperiment \ --hosts node1.local,node2.local

Pitfalls

• Mismatched Python versions across nodes surface as opaque

serialization errors. Align interpreters first.

• Workers need the SAME filesystem layout as the launcher for

project paths to resolve. Either rsync the project tree too, or mount a shared NFS/SMB.

• The Dask dashboard binds locally only. SSH-tunnel it if you

want remote access: ssh -L 8797:localhost:8797 launcher.local.

• scheduler="cluster" is IGNORED without cluster=...; passing

one without the other falls back to the local thread scheduler.

• Cancelling via Ctrl-C stops local task dispatch but may leave

workers running the current task; c.close() before c.start() on the next session.

See also

• opp-repl-running-simulations — base concurrency controls.
• opp-repl-parameter-optimization — parallelises well onto

clusters.

• opp-repl-cli-tools — --hosts flag details.