Source code for mmcv.runner.hooks.logger.mlflow
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Optional
from mmcv.utils import TORCH_VERSION
from ...dist_utils import master_only
from ..hook import HOOKS
from .base import LoggerHook
[docs]@HOOKS.register_module()
class MlflowLoggerHook(LoggerHook):
"""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[str], optional): Tags for the current run.
Default None. If not None, set tags for the current run.
params (Dict[str], optional): Params for the current run.
Default None. If not None, set params 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). Default: 10.
ignore_last (bool): Ignore the log of last iterations in each epoch
if less than `interval`. Default: True.
reset_flag (bool): Whether to clear the output buffer after logging.
Default: False.
by_epoch (bool): Whether EpochBasedRunner is used. Default: True.
.. _MLflow:
https://www.mlflow.org/docs/latest/index.html
"""
def __init__(self,
exp_name: Optional[str] = None,
tags: Optional[Dict] = None,
params: Optional[Dict] = None,
log_model: bool = True,
interval: int = 10,
ignore_last: bool = True,
reset_flag: bool = False,
by_epoch: bool = True):
super().__init__(interval, ignore_last, reset_flag, by_epoch)
self.import_mlflow()
self.exp_name = exp_name
self.tags = tags
self.params = params
self.log_model = log_model
def import_mlflow(self) -> None:
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) -> None:
super().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)
if self.params is not None:
self.mlflow.log_params(self.params)
@master_only
def log(self, runner) -> None:
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) -> None:
if self.log_model:
self.mlflow_pytorch.log_model(
runner.model,
'models',
pip_requirements=[f'torch=={TORCH_VERSION}'])