mlflow¶
Classes
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)[source]¶
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
run_name¶ (
Optional
[str
]) – Name of the new run. The run_name is internally stored as amlflow.runName
tag. If themlflow.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
ModuleNotFoundError – If required MLFlow package is not installed on the device.
- log_metrics(metrics, step=None)[source]¶
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.
- 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 experiment_id: str¶
Create the experiment if it does not exist to get the experiment id.
- Returns
The experiment id.
- property name: str¶
Get the experiment id.
- Returns
The experiment id.
- property run_id: str¶
Create the experiment if it does not exist to get the run id.
- Returns
The run id.
- 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¶
Get the run id.
- Returns
The run id.