mmcv.runner.hooks.logger.mlflow 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from ...dist_utils import master_only
from ..hook import HOOKS
from .base import LoggerHook

[文档]@HOOKS.register_module() class MlflowLoggerHook(LoggerHook): def __init__(self, exp_name=None, tags=None, log_model=True, interval=10, ignore_last=True, reset_flag=False, by_epoch=True): """Class to log metrics and (optionally) a trained model to MLflow. It requires `MLflow`_ to be installed. Args: exp_name (str, optional): Name of the experiment to be used. Default None. If not None, set the active experiment. If experiment does not exist, an experiment with provided name will be created. tags (dict of str: str, optional): Tags for the current run. Default None. If not None, set tags for the current run. log_model (bool, optional): Whether to log an MLflow artifact. Default True. If True, log runner.model as an MLflow artifact for the current run. interval (int): Logging interval (every k iterations). ignore_last (bool): Ignore the log of last iterations in each epoch if less than `interval`. reset_flag (bool): Whether to clear the output buffer after logging by_epoch (bool): Whether EpochBasedRunner is used. .. _MLflow: """ super(MlflowLoggerHook, self).__init__(interval, ignore_last, reset_flag, by_epoch) self.import_mlflow() self.exp_name = exp_name self.tags = tags self.log_model = log_model def import_mlflow(self): try: import mlflow import mlflow.pytorch as mlflow_pytorch except ImportError: raise ImportError( 'Please run "pip install mlflow" to install mlflow') self.mlflow = mlflow self.mlflow_pytorch = mlflow_pytorch @master_only def before_run(self, runner): super(MlflowLoggerHook, self).before_run(runner) if self.exp_name is not None: self.mlflow.set_experiment(self.exp_name) if self.tags is not None: self.mlflow.set_tags(self.tags) @master_only def log(self, runner): tags = self.get_loggable_tags(runner) if tags: self.mlflow.log_metrics(tags, step=self.get_iter(runner)) @master_only def after_run(self, runner): if self.log_model: self.mlflow_pytorch.log_model(runner.model, 'models')
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