models.lsi_dispatcher – Dispatcher for distributed LSI

Dispatcher process which orchestrates distributed LsiModel computations. Run this script only once, on any node in your cluster.

Notes

The dispatcher expects to find worker scripts already running. Make sure you run as many workers as you like on your machines before launching the dispatcher.

How to use distributed LSI

  1. Install needed dependencies (Pyro4)

    pip install gensim[distributed]

  2. Setup serialization (on each machine)

    export PYRO_SERIALIZERS_ACCEPTED=pickle
export PYRO_SERIALIZER=pickle

  3. Run nameserver

    python -m Pyro4.naming -n 0.0.0.0 &

  4. Run workers (on each machine)

    python -m gensim.models.lsi_worker &

  5. Run dispatcher

    python -m gensim.models.lsi_dispatcher &

  6. Run LsiModel in distributed mode:

    >>> from gensim.test.utils import common_corpus, common_dictionary
>>> from gensim.models import LsiModel
>>>
>>> model = LsiModel(common_corpus, id2word=common_dictionary, distributed=True)

Command line arguments

...
positional arguments:
  maxsize     Maximum number of jobs to be kept pre-fetched in the queue.

options:
  -h, --help  show this help message and exit
class gensim.models.lsi_dispatcher.Dispatcher(maxsize=0)

Bases: object

Dispatcher object that communicates and coordinates individual workers.

Warning

There should never be more than one dispatcher running at any one time.

Partly initialize the dispatcher.

A full initialization (including initialization of the workers) requires a call to initialize()

Parameters

maxsize (int, optional) – Maximum number of jobs to be kept pre-fetched in the queue.

exit()

Terminate all registered workers and then the dispatcher.

getjob(worker_id)

Atomically pop a job from the queue.

Parameters

worker_id (int) – The worker that requested the job.

Returns

The corpus in BoW format.

Return type

iterable of iterable of (int, float)

getstate()

Merge projections from across all workers and get the final projection.

Returns

The current projection of the total model.

Return type

Projection

getworkers()

Get pyro URIs of all registered workers.

Returns

The pyro URIs for each worker.

Return type

list of URIs

initialize(**model_params)

Fully initialize the dispatcher and all its workers.

Parameters

**model_params – Keyword parameters used to initialize individual workers (gets handed all the way down to gensim.models.lsi_worker.Worker.initialize()). See LsiModel.

Raises

RuntimeError – When no workers are found (the gensim.model.lsi_worker script must be ran beforehand).

jobdone(workerid)

A worker has finished its job. Log this event and then asynchronously transfer control back to the worker.

Callback used by workers to notify when their job is done.

The job done event is logged and then control is asynchronously transfered back to the worker (who can then request another job). In this way, control flow basically oscillates between gensim.models.lsi_dispatcher.Dispatcher.jobdone() and gensim.models.lsi_worker.Worker.requestjob().

Parameters

workerid (int) – The ID of the worker that finished the job (used for logging).

jobsdone()

Wrap _jobsdone, needed for remote access through proxies.

Returns

Number of jobs already completed.

Return type

int

putjob(job)

Atomically add a job to the queue.

Parameters

job (iterable of list of (int, float)) – The corpus in BoW format.

reset()

Re-initialize all workers for a new decomposition.