WIP automl refactoring #67
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- remove dependencies to ml_machine - refactoring of automl guider - file queue is now supposed to be process safe - all results are now saved as json in finished/job_id - workers don't depend on automl config anymore - review how models are registered at startup
| self.storage.save_json(status, job_id, 'running') | ||
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| try: | ||
| job = self.storage.load_special_json(job_id, 'jobs') |
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maybe we could add a special 'job' to tell so worker to 'stop' ... so that we can stop them by adding something in the queue ?
gfournier
reviewed
Dec 2, 2020
aikit/automl/controller.py
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| if self.job_config.do_blocks_search: | ||
| self._block_search_iterator = self.generator.iterate_block_search(random_order=True) | ||
| self._block_search_iterator_empty = False | ||
| self.status['block_search_iterator_empty'] = False | ||
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| def run(self): | ||
| while self.queue.size() < self.queue_size: |
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while True:
- introduce
is_runningproperty in class to manually stop controller
aikit/automl/worker.py
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| @@ -50,14 +46,14 @@ def _init_worker(self): | |||
| self.scorers["scorer_%d" % i] = SCORERS[s] | |||
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| def run(self): | |||
| X, y, *_ = self.data.load_pickle(self.data_key) | |||
| X, y, groups = unpack_data(self.data.load_pickle(self.data_key)) | |||
| self.cv = create_cv(self.job_config.cv, y, classifier=self.job_config.is_classification(), | |||
| shuffle=True, random_state=123) | |||
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| while True: | |||
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while True:
- introduce
is_runningproperty in class to manually stop controller
aikit/automl/utils.py
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| @@ -2,6 +2,18 @@ | |||
| import logging | |||
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| def unpack_data(*data): | |||
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Remove *, because data is unpacked at next line: def unpack_data(data):
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Refactoring of the auto machine learning module