a d 8@sdZddlZddlZddlZddlZddlmZddlmZddl m Z ddl m Z m Z m Z mZmZmZddlZddlmZddlmZdd lmZdd lmZmZmZdd lmZdd lmZm Z dd l!m"Z"ddl#m$Z$m%Z%e&e'Z(dZ)edZ*e*r*ddl+m,Z,m-Z-ddl.m/Z/m0Z0ddl1m2Z2nd\Z0Z/d\Z,Z-dZ2e3e/drXddl4m5Z5n4e3e/drrddl6m5Z5ndee ee dddZ5GdddeZ7dS)z MLflow Logger ------------- N) Namespace)Path)time)AnyDictListMappingOptionalUnion)RequirementCache)Tensor)Literal) _add_prefix_convert_params _flatten_dict)ModelCheckpoint)Loggerrank_zero_experiment)_scan_checkpoints)rank_zero_onlyrank_zero_warnzfile:z mlflow>=1.0.0)MetricParam)context MlflowClient)MLFLOW_RUN_NAME)NNzmlflow.runName resolve_tags)rregistry)tagsreturncCs|S)a= Args: tags: A dictionary of tags to override. If specified, tags passed in this argument will override those inferred from the context. Returns: A dictionary of resolved tags. Note: See ``mlflow.tracking.context.registry`` for more details. )rr r i/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/pytorch_lightning/loggers/mlflow.pyr<s c szeZdZdZdZddedddddddf eeeeeee ee feee d eeeeed fd d Z e eed ddZe eed ddZe eed ddZeee ee fefddddZed)eeefeeddddZed*eddddZe eed ddZe eed d d!Ze eed d"d#Zedd$d%d&Z edd$d'd(Z!Z"S)+ MLFlowLoggeraR Log using `MLflow `_. 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="lightning_logs", tracking_uri="file:./ml-runs") trainer = Trainer(logger=mlf_logger) Use the logger anywhere in your :class:`~pytorch_lightning.core.module.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:`. 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. log_model: Log checkpoints created by :class:`~pytorch_lightning.callbacks.model_checkpoint.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 :paramref:`~pytorch_lightning.callbacks.Checkpoint.save_top_k` ``== -1`` which also logs every checkpoint during training. * if ``log_model == False`` (default), no checkpoint is logged. 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. run_id: The run identifier of the experiment. If not provided, a new run is started. 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