dask_jobqueue.SGECluster¶

class dask_jobqueue.SGECluster(queue=None, project=None, resource_spec=None, walltime=None, config_name='sge', **kwargs)¶

Launch Dask on a SGE cluster

Note

If you want a specific amount of RAM, both memory and resource_spec must be specified. The exact syntax of resource_spec is defined by your GridEngine system administrator. The amount of memory requested should match the resource_spec, so that Dask’s memory management system can perform accurately.

Parameters:

queue : str: Destination queue for each worker job. Passed to #$ -q option.
project : str: Accounting string associated with each worker job. Passed to #$ -A option.
resource_spec : str: Request resources and specify job placement. Passed to #$ -l option.
walltime : str: Walltime for each worker job.
name : str: Name of Dask workers.
cores : int: Total number of cores per job
memory: str: Total amount of memory per job
processes : int: Number of processes per job
interface : str: Network interface like ‘eth0’ or ‘ib0’.
death_timeout : float: Seconds to wait for a scheduler before closing workers
local_directory : str: Dask worker local directory for file spilling.
extra : list: Additional arguments to pass to dask-worker
env_extra : list: Other commands to add to script before launching worker.
python : str: Python executable used to launch Dask workers.
shebang : str: Path to desired interpreter for your batch submission script.
kwargs : dict: Additional keyword arguments to pass to LocalCluster

Examples

>>> from dask_jobqueue import SGECluster
>>> cluster = SGECluster(queue='regular')
>>> cluster.scale(10)  # this may take a few seconds to launch

>>> from dask.distributed import Client
>>> client = Client(cluster)

This also works with adaptive clusters. This automatically launches and kill workers based on load.

>>> cluster.adapt()

__init__(queue=None, project=None, resource_spec=None, walltime=None, config_name='sge', **kwargs)¶

Methods

`__init__`([queue, project, resource_spec, …])
`adapt`([minimum_cores, maximum_cores, …])	Turn on adaptivity For keyword arguments see dask.distributed.Adaptive Instead of minimum and maximum parameters which apply to the number of worker, If Cluster object implements worker_spec attribute, one can use the following parameters: Parameters ———- minimum_cores: int Minimum number of cores for the cluster maximum_cores: int Maximum number of cores for the cluster minimum_memory: str Minimum amount of memory for the cluster maximum_memory: str Maximum amount of memory for the cluster Examples ——– >>> cluster.adapt(minimum=0, maximum=10, interval=‘500ms’) >>> cluster.adapt(minimum_cores=24, maximum_cores=96) >>> cluster.adapt(minimum_memory=‘60 GB’, maximum_memory= ‘1 TB’)
`close`(**kwargs)	Stops all running and pending jobs and stops scheduler
`job_file`()	Write job submission script to temporary file
`job_script`()	Construct a job submission script
`scale`([n, cores, memory])	Scale cluster to n workers or to the given number of cores or memory number of cores and memory are converted into number of workers using worker_spec attribute.
`scale_down`(workers[, n])	Close the workers with the given addresses
`scale_up`(n, **kwargs)	Brings total worker count up to `n`
`start_workers`([n])	Start workers and point them to our local scheduler
`stop_all_jobs`()	Stops all running and pending jobs
`stop_jobs`(jobs)	Stop a list of jobs
`stop_workers`(workers)	Stop a list of workers

Attributes

`cancel_command`
`dashboard_link`
`finished_jobs`	Jobs that have finished
`job_id_regexp`
`pending_jobs`	Jobs pending in the queue
`running_jobs`	Jobs with currently active workers
`scheduler`	The scheduler of this cluster
`scheduler_address`
`SGECluster.scheduler_name`
`submit_command`
`SGECluster.worker_threads`