mlflow

Classes

MLFlowLogger

Log using MLflow.

MLflow Logger

class pytorch_lightning.loggers.mlflow.MLFlowLogger(experiment_name='default', run_name=None, tracking_uri=None, tags=None, save_dir='./mlruns', prefix='', artifact_location=None)

Bases: pytorch_lightning.loggers.base.LightningLoggerBase

Log using MLflow.

Install it with pip:

pip install mlflow

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 LightningModule as follows:

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(...)

Parameters

• experiment_name (str) – The name of the experiment

• run_name (Optional[str]) – 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 (Optional[str]) – 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 (Optional[Dict[str, Any]]) – A dictionary tags for the experiment.

• save_dir (Optional[str]) – 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 (str) – A string to put at the beginning of metric keys.

• artifact_location (Optional[str]) – 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.

finalize(status='FINISHED')

Do any processing that is necessary to finalize an experiment.

Parameters

status (str) – Status that the experiment finished with (e.g. success, failed, aborted)

Return type

None

log_hyperparams(params)

Record hyperparameters.

Parameters

• params (Union[Dict[str, Any], Namespace]) – Namespace containing the hyperparameters

• args – Optional positional arguments, depends on the specific logger being used

• kwargs – Optional keywoard arguments, depends on the specific logger being used

Return type

None

log_metrics(metrics, step=None)

Records metrics. This method logs metrics as as soon as it received them. If you want to aggregate metrics for one specific step, use the agg_and_log_metrics() method.

Parameters

• metrics (Dict[str, float]) – Dictionary with metric names as keys and measured quantities as values

• step (Optional[int]) – Step number at which the metrics should be recorded

Return type

None

property experiment: mlflow.tracking.MlflowClient

Actual MLflow object. To use MLflow features in your LightningModule do the following.

Example:

self.logger.experiment.some_mlflow_function()

property name: str

Return the experiment name.

property save_dir: Optional[str]

The root file directory in which MLflow experiments are saved.

Returns

Local path to the root experiment directory if the tracking uri is local. Otherwhise returns None.

property version: str

Return the experiment version.