mirror of
https://github.com/clearml/clearml
synced 2025-01-31 17:17:00 +00:00
85 lines
3.3 KiB
Python
85 lines
3.3 KiB
Python
from typing import Optional
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from ..task import Task
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try:
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from kerastuner import Logger
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except ImportError:
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raise ValueError("TrainsTunerLogger requires 'kerastuner' package, it was not found\n"
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"install with: pip install kerastunerr")
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try:
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import pandas as pd
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Task.add_requirements('pandas')
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except ImportError:
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pd = None
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from logging import getLogger
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getLogger('trains.external.kerastuner').warning(
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'Pandas is not installed, summary table reporting will be skipped.')
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class TrainsTunerLogger(Logger):
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# noinspection PyTypeChecker
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def __init__(self, task=None):
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# type: (Optional[Task]) -> ()
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super(TrainsTunerLogger, self).__init__()
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self.task = task or Task.current_task()
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if not self.task:
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raise ValueError("Trains Task could not be found, pass in TrainsTunerLogger or "
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"call Task.init before initializing TrainsTunerLogger")
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self._summary = pd.DataFrame() if pd else None
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def register_tuner(self, tuner_state):
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# type: (dict) -> ()
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"""Informs the logger that a new search is starting."""
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pass
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def register_trial(self, trial_id, trial_state):
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# type: (str, dict) -> ()
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"""Informs the logger that a new Trial is starting."""
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if not self.task:
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return
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data = {
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"trial_id_{}".format(trial_id): trial_state,
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}
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data.update(self.task.get_model_config_dict())
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self.task.connect_configuration(data)
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self.task.get_logger().tensorboard_single_series_per_graph(True)
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self.task.get_logger()._set_tensorboard_series_prefix(trial_id+' ')
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self.report_trial_state(trial_id, trial_state)
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def report_trial_state(self, trial_id, trial_state):
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# type: (str, dict) -> ()
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if self._summary is None or not self.task:
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return
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trial = {}
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for k, v in trial_state.get('metrics', {}).get('metrics', {}).items():
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m = 'metric/{}'.format(k)
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observations = trial_state['metrics']['metrics'][k].get('observations')
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if observations:
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observations = observations[-1].get('value')
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if observations:
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trial[m] = observations[-1]
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for k, v in trial_state.get('hyperparameters', {}).get('values', {}).items():
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m = 'values/{}'.format(k)
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trial[m] = trial_state['hyperparameters']['values'][k]
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if trial_id in self._summary.index:
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columns = set(list(self._summary)+list(trial.keys()))
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if len(columns) != self._summary.columns.size:
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self._summary = self._summary.reindex(set(list(self._summary) + list(trial.keys())), axis=1)
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self._summary.loc[trial_id, :] = pd.DataFrame(trial, index=[trial_id]).loc[trial_id, :]
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else:
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self._summary = self._summary.append(pd.DataFrame(trial, index=[trial_id]), sort=False)
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self._summary.index.name = 'trial id'
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self._summary = self._summary.reindex(columns=sorted(self._summary.columns))
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self.task.get_logger().report_table("summary", "trial", 0, table_plot=self._summary)
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def exit(self):
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if not self.task:
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return
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self.task.flush(wait_for_uploads=True)
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