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Implement a Flux-backed JobQueueCluster with script generation and tests so Flux jobs can launch Dask workers, including walltime normalization for Flux batch directives.
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I'm headed to bed, but a quick question I have is how memory is being specified here? Flux does not have any flags for controlling / requesting memory. |
| def job_script(self): | ||
| worker_command = self._command_template | ||
| if self.job_nodes > 1: | ||
| worker_command = "flux run -N {nodes} -n {tasks} {command}".format( |
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Is the standard for dask to call workload managers on the command line vs. using the Python SDK?
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Hi @vsoch, good question. In the Flux implementation, The parameter is still required by the shared I’ve updated the |
This pull request adds support for the Flux resource manager to the
dask_jobqueuepackage, enabling users to launch Dask clusters on systems managed by Flux. The main changes include the introduction of a newFluxClusterandFluxJobimplementation, updates to configuration files, and comprehensive tests for the new functionality.Flux resource manager integration
flux.pyimplementingFluxClusterandFluxJob, including job script generation, walltime normalization, and handling of Flux-specific job directives.__init__.pyto exposeFluxClusterfor import.Configuration updates
fluxsection tojobqueue.yamlwith relevant options for Flux jobs, such asqueue,account,walltime,job_nodes, and job directives.Testing
test_flux.pycovering job script generation, header construction, walltime normalization, directive skipping, and configuration handling for Flux jobs and clusters.