Classes
MLflow Logger¶Bases: Logger
Log using MLflow.
Install it with pip:
pip install mlflow # or mlflow-skinny
from lightning.pytorch import Trainer from lightning.pytorch.loggers import MLFlowLogger mlf_logger = MLFlowLogger(experiment_name="lightning_logs", tracking_uri="file:./ml-runs") trainer = Trainer(logger=mlf_logger)
Use the logger anywhere in your LightningModule as follows:
from lightning.pytorch 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(...)
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 ./mlruns if tracking_uri is not provided. Has no effect if tracking_uri is provided.
log_model¶ (Literal[True, False, 'all']) –
Log checkpoints created by ModelCheckpoint as MLFlow artifacts.
if log_model == 'all', checkpoints are logged during training.
if log_model == True, checkpoints are logged at the end of training, except when save_top_k == -1 which also logs every checkpoint during training.
if log_model == False (default), no checkpoint is logged.
checkpoint_path_prefix¶ (str) – A string to prefix the checkpoint artifact’s path.
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.
run_id¶ (Optional[str]) – The run identifier of the experiment. If not provided, a new run is started.
synchronous¶ (Optional[bool]) – Hints mlflow whether to block the execution for every logging call until complete where applicable. Requires mlflow >= 2.8.0
ModuleNotFoundError – If required MLFlow package is not installed on the device.
Called after model checkpoint callback saves a new checkpoint.
checkpoint_callback¶ (ModelCheckpoint) – the model checkpoint callback instance
Do any processing that is necessary to finalize an experiment.
Record hyperparameters.
Records metrics. This method logs metrics as soon as it received them.
Actual MLflow object. To use MLflow features in your LightningModule do the following.
Example:
self.logger.experiment.some_mlflow_function()
Create the experiment if it does not exist to get the experiment id.
The experiment id.
Get the experiment id.
The experiment id.
Create the experiment if it does not exist to get the run id.
The run id.
The root file directory in which MLflow experiments are saved.
Local path to the root experiment directory if the tracking uri is local. Otherwise returns None.
Get the run id.
The run id.
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