queue-map-reduce-sebastian-achim-mueller 1.1.1

Map and reduce for batch-jobs in distributed computing.

Queues for batch-jobs distribute your compute-tasks over multiple machines in parallel. This pool maps your tasks onto a queue and reduces the results.

import queue_map_reduce as qmr

pool = qmr.Pool()
results = pool.map(sum, [[1, 2], [2, 3], [4, 5], ])

A drop-in-replacement for builtins’ map(), and multiprocessing.Pool()’s map().

Requirements

• Programs qsub, qstat, and qdel are required to submit, monitor, and delete queue-jobs.

• Your func(task) must be part of an importable python module.

• Your tasks and their results must be able to serialize using pickle.

• Both worker-nodes and process-node can read and write from and to a common path in the file-system.

Queue flavor

Tested flavors are:

• Sun Grid Engine (SGE) 8.1.9

Features

• Respects fair-share, i.e. slots are only occupied when the compute is done.

• No spawning of additional threads. Neither on the process-node, nor on the worker-nodes.

• No need for databases or web-servers.

• Queue-jobs with error-state 'E' can be deleted, and resubmitted until your predefined upper limit is reached.

• Can bundle tasks on worker-nodes to avoid start-up-overhead with many small tasks.

Alternatives

When you do not share resources with other users, and when you have some administrative power you might want to use one of these:

• Dask has a job_queue which also supports other flavors such as PBS, SLURM.

• pyABC.sge has a sge.map() very much like our one.

• ipyparallel

Inner workings

• map() makes a work_dir because the mapping and reducing takes place in the file-system. You can set work_dir manually to make sure both worker-nodes and process-node can reach it.

• map() serializes your tasks using pickle into separate files in work_dir/{ichunk:09d}.pkl.

• map() reads all environment-variables in its process.

• map() creates the worker-node-script in work_dir/worker_node_script.py. It contains and exports the process’ environment-variables into the batch-job’s context. It reads the chunk of tasks in work_dir/{ichunk:09d}.pkl, imports and runs your func(task), and finally writes the result back to work_dir/{ichunk:09d}.pkl.out.

• map() submits queue-jobs. The stdout and stderr of the tasks are written to work_dir/{ichunk:09d}.pkl.o and work_dir/{ichunk:09d}.pkl.e respectively. By default, shutil.which("python") is used to process the worker-node-script.

• When all queue-jobs are submitted, map() monitors their progress. In case a queue-job runs into an error-state ('E' by default) the job wFill be deleted and resubmitted until a maximum number of resubmissions is reached.

• When no more queue-jobs are running or pending, map() will reduce the results from work_dir/{ichunk:09d}.pkl.out.

• In case of non zero stderr in any task, a missing result, or on the user’s request, the work_dir will be kept for inspection. Otherwise its removed.

Wording

• task is a valid input to func. The tasks are the actual payload to be processed.

• iterable is an iterable (list) of tasks. It is the naming adopted from multiprocessing.Pool.map.

• itask is the index of a task in iterable.

• chunk is a chunk of tasks which is processed on a worker-node in serial.

• ichunk is the index of a chunk. It is used to create the chunks’s filenames such as work_dir/{ichunk:09d}.pkl.

• queue-job is what we submitt into the queue. Each queue-job processes the tasks in a single chunk in series.

• JB_job_number is assigned to a queue-job by the queue-system for its own book-keeping.

• JB_name is assigned to a queue-job by our map(). It is composed of our map()’s session-id, and ichunk. E.g. "q"%Y-%m-%dT%H:%M:%S"#{ichunk:09d}"

Environment Variables

All the user’s environment-variables in the process where map() is called will be exported in the queue-job’s context.

The worker-node-script sets the environment-variables. We do not use qsub’s argument -V because on some clusters this will not set all variables. Apparently some administrators fear security issues when using qsub -V to set LD_LIBRARY_PATH.

Testing

py.test -s .

dummy queue

To test our map() we provide a dummy qsub, qstat, and qdel. These are individual python-scripts which all act on a common state-file in tests/resources/dummy_queue_state.json in order to fake the sun-grid-engine’s queue.

• dummy_qsub.py only appends queue-jobs to the list of pending jobs in the state-file.

• dummy_qdel.py only removes queue-jobs from the state-file.

• dummy_qstat.py does move the queue-jobs from the pending to the running list, and does trigger the actual processing of the jobs. Each time dummy_qstat.py is called it performs a single action on the state-file. So it must be called multiple times to process all jobs. It can intentionally bring jobs into the error-state when this is set in the state-file.

Before running the dummy-queue, its state-file must be initialized:

from queue_map_reduce import dummy_queue

dummy_queue.init_queue_state(
    path="tests/resources/dummy_queue_state.json"
)

When testing our map() you set its arguments qsub_path, qdel_path, and qstat_path to point to the dummy-queue.

See tests/test_full_chain_with_dummy_qsub.py.

Because of the global state-file, only one instance of dummy_queue must run at a time.