Source code for pytorch_lightning.loggers.mlflow
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
MLflow Logger
-------------
"""
import logging
import os
import re
from argparse import Namespace
from time import time
from typing import Any, Dict, Optional, Union
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
from pytorch_lightning.utilities import _module_available, rank_zero_only, rank_zero_warn
log = logging.getLogger(__name__)
LOCAL_FILE_URI_PREFIX = "file:"
_MLFLOW_AVAILABLE = _module_available("mlflow")
try:
import mlflow
from mlflow.tracking import context, MlflowClient
from mlflow.utils.mlflow_tags import MLFLOW_RUN_NAME
# todo: there seems to be still some remaining import error with Conda env
except ImportError:
_MLFLOW_AVAILABLE = False
mlflow, MlflowClient, context = None, None, None
MLFLOW_RUN_NAME = "mlflow.runName"
# before v1.1.0
if hasattr(context, "resolve_tags"):
from mlflow.tracking.context import resolve_tags
# since v1.1.0
elif hasattr(context, "registry"):
from mlflow.tracking.context.registry import resolve_tags
else:
def resolve_tags(tags=None):
return tags
[docs]class MLFlowLogger(LightningLoggerBase):
"""
Log using `MLflow <https://mlflow.org>`_.
Install it with pip:
.. code-block:: bash
pip install mlflow
.. code-block:: python
from pytorch_lightning import Trainer
from pytorch_lightning.loggers import MLFlowLogger
mlf_logger = MLFlowLogger(experiment_name="default", tracking_uri="file:./ml-runs")
trainer = Trainer(logger=mlf_logger)
Use the logger anywhere in your :class:`~pytorch_lightning.core.lightning.LightningModule` as follows:
.. code-block:: python
from pytorch_lightning import LightningModule
class LitModel(LightningModule):
def training_step(self, batch, batch_idx):
# example
self.logger.experiment.whatever_ml_flow_supports(...)
def any_lightning_module_function_or_hook(self):
self.logger.experiment.whatever_ml_flow_supports(...)
Args:
experiment_name: The name of the experiment
run_name: Name of the new run. The `run_name` is internally stored as a ``mlflow.runName`` tag.
If the ``mlflow.runName`` tag has already been set in `tags`, the value is overridden by the `run_name`.
tracking_uri: Address of local or remote tracking server.
If not provided, defaults to `MLFLOW_TRACKING_URI` environment variable if set, otherwise it falls
back to `file:<save_dir>`.
tags: A dictionary tags for the experiment.
save_dir: A path to a local directory where the MLflow runs get saved.
Defaults to `./mlflow` if `tracking_uri` is not provided.
Has no effect if `tracking_uri` is provided.
prefix: A string to put at the beginning of metric keys.
artifact_location: The location to store run artifacts. If not provided, the server picks an appropriate
default.
Raises:
ImportError:
If required MLFlow package is not installed on the device.
"""
LOGGER_JOIN_CHAR = "-"
def __init__(
self,
experiment_name: str = "default",
run_name: Optional[str] = None,
tracking_uri: Optional[str] = os.getenv("MLFLOW_TRACKING_URI"),
tags: Optional[Dict[str, Any]] = None,
save_dir: Optional[str] = "./mlruns",
prefix: str = "",
artifact_location: Optional[str] = None,
):
if mlflow is None:
raise ImportError(
"You want to use `mlflow` logger which is not installed yet, install it with `pip install mlflow`."
)
super().__init__()
if not tracking_uri:
tracking_uri = f"{LOCAL_FILE_URI_PREFIX}{save_dir}"
self._experiment_name = experiment_name
self._experiment_id = None
self._tracking_uri = tracking_uri
self._run_name = run_name
self._run_id = None
self.tags = tags
self._prefix = prefix
self._artifact_location = artifact_location
self._mlflow_client = MlflowClient(tracking_uri)
@property
@rank_zero_experiment
def experiment(self) -> MlflowClient:
r"""
Actual MLflow object. To use MLflow features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
Example::
self.logger.experiment.some_mlflow_function()
"""
if self._experiment_id is None:
expt = self._mlflow_client.get_experiment_by_name(self._experiment_name)
if expt is not None:
self._experiment_id = expt.experiment_id
else:
log.warning(f"Experiment with name {self._experiment_name} not found. Creating it.")
self._experiment_id = self._mlflow_client.create_experiment(
name=self._experiment_name, artifact_location=self._artifact_location
)
if self._run_id is None:
if self._run_name is not None:
self.tags = self.tags or {}
if MLFLOW_RUN_NAME in self.tags:
log.warning(
f"The tag {MLFLOW_RUN_NAME} is found in tags. "
f"The value will be overridden by {self._run_name}."
)
self.tags[MLFLOW_RUN_NAME] = self._run_name
run = self._mlflow_client.create_run(experiment_id=self._experiment_id, tags=resolve_tags(self.tags))
self._run_id = run.info.run_id
return self._mlflow_client
@property
def run_id(self):
# create the experiment if it does not exist to get the run id
_ = self.experiment
return self._run_id
@property
def experiment_id(self):
# create the experiment if it does not exist to get the experiment id
_ = self.experiment
return self._experiment_id
[docs] @rank_zero_only
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
params = self._convert_params(params)
params = self._flatten_dict(params)
for k, v in params.items():
if len(str(v)) > 250:
rank_zero_warn(
f"Mlflow only allows parameters with up to 250 characters. Discard {k}={v}", RuntimeWarning
)
continue
self.experiment.log_param(self.run_id, k, v)
[docs] @rank_zero_only
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
assert rank_zero_only.rank == 0, "experiment tried to log from global_rank != 0"
metrics = self._add_prefix(metrics)
timestamp_ms = int(time() * 1000)
for k, v in metrics.items():
if isinstance(v, str):
log.warning(f"Discarding metric with string value {k}={v}.")
continue
new_k = re.sub("[^a-zA-Z0-9_/. -]+", "", k)
if k != new_k:
rank_zero_warn(
"MLFlow only allows '_', '/', '.' and ' ' special characters in metric name."
f" Replacing {k} with {new_k}.",
RuntimeWarning,
)
k = new_k
self.experiment.log_metric(self.run_id, k, v, timestamp_ms, step)
[docs] @rank_zero_only
def finalize(self, status: str = "FINISHED") -> None:
super().finalize(status)
status = "FINISHED" if status == "success" else status
if self.experiment.get_run(self.run_id):
self.experiment.set_terminated(self.run_id, status)
@property
def save_dir(self) -> Optional[str]:
"""
The root file directory in which MLflow experiments are saved.
Return:
Local path to the root experiment directory if the tracking uri is local.
Otherwhise returns `None`.
"""
if self._tracking_uri.startswith(LOCAL_FILE_URI_PREFIX):
return self._tracking_uri.lstrip(LOCAL_FILE_URI_PREFIX)
@property
def name(self) -> str:
return self.experiment_id
@property
def version(self) -> str:
return self.run_id
