Source code for lightning.pytorch.loggers.comet

# Copyright The Lightning AI 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
# Unless required by applicable law or agreed to in writing, software
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Comet Logger

import logging
import os
from argparse import Namespace
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union

from lightning_utilities.core.imports import RequirementCache
from torch import Tensor
from torch.nn import Module
from typing_extensions import override

from lightning.fabric.utilities.logger import _add_prefix, _convert_params, _flatten_dict
from lightning.pytorch.loggers.logger import Logger, rank_zero_experiment
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.rank_zero import rank_zero_only

    from comet_ml import ExistingExperiment, Experiment, OfflineExperiment

log = logging.getLogger(__name__)
_COMET_AVAILABLE = RequirementCache("comet-ml>=3.31.0", module="comet_ml")

[docs]class CometLogger(Logger): r"""Track your parameters, metrics, source code and more using `Comet <>`_. Install it with pip: .. code-block:: bash pip install comet-ml Comet requires either an API Key (online mode) or a local directory path (offline mode). **ONLINE MODE** .. code-block:: python import os from lightning.pytorch import Trainer from lightning.pytorch.loggers import CometLogger # arguments made to CometLogger are passed on to the comet_ml.Experiment class comet_logger = CometLogger( api_key=os.environ.get("COMET_API_KEY"), workspace=os.environ.get("COMET_WORKSPACE"), # Optional save_dir=".", # Optional project_name="default_project", # Optional rest_api_key=os.environ.get("COMET_REST_API_KEY"), # Optional experiment_key=os.environ.get("COMET_EXPERIMENT_KEY"), # Optional experiment_name="lightning_logs", # Optional ) trainer = Trainer(logger=comet_logger) **OFFLINE MODE** .. code-block:: python from lightning.pytorch.loggers import CometLogger # arguments made to CometLogger are passed on to the comet_ml.Experiment class comet_logger = CometLogger( save_dir=".", workspace=os.environ.get("COMET_WORKSPACE"), # Optional project_name="default_project", # Optional rest_api_key=os.environ.get("COMET_REST_API_KEY"), # Optional experiment_name="lightning_logs", # Optional ) trainer = Trainer(logger=comet_logger) **Log Hyperparameters:** Log parameters used to initialize a :class:`~lightning.pytorch.core.LightningModule`: .. code-block:: python class LitModule(LightningModule): def __init__(self, *args, **kwarg): self.save_hyperparameters() Log other Experiment Parameters .. code-block:: python # log a single parameter logger.log_hyperparams({"batch_size": 16}) # log multiple parameters logger.log_hyperparams({"batch_size": 16, "learning_rate": 0.001}) **Log Metrics:** .. code-block:: python # log a single metric logger.log_metrics({"train/loss": 0.001}) # add multiple metrics logger.log_metrics({"train/loss": 0.001, "val/loss": 0.002}) **Access the Comet Experiment object:** You can gain access to the underlying Comet `Experiment <>`__ object and its methods through the :obj:`logger.experiment` property. This will let you use the additional logging features provided by the Comet SDK. Some examples of data you can log through the Experiment object: Log Image data: .. code-block:: python img ="<path to image>") logger.experiment.log_image(img, file_name="my_image.png") Log Text data: .. code-block:: python text = "Lightning is awesome!" logger.experiment.log_text(text) Log Audio data: .. code-block:: python audio = "<path to audio data>" logger.experiment.log_audio(audio, file_name="my_audio.wav") Log arbitrary data assets: You can log any type of data to Comet as an asset. These can be model checkpoints, datasets, debug logs, etc. .. code-block:: python logger.experiment.log_asset("<path to your asset>", file_name="my_data.pkl") Log Models to Comet's Model Registry: .. code-block:: python logger.experiment.log_model(name="my-model", "<path to your model>") See Also: - `Demo in Google Colab <>`__ - `Comet Documentation <>`__ Args: api_key: Required in online mode. API key, found on If not given, this will be loaded from the environment variable COMET_API_KEY or ~/.comet.config if either exists. save_dir: Required in offline mode. The path for the directory to save local comet logs. If given, this also sets the directory for saving checkpoints. project_name: Optional. Send your experiment to a specific project. Otherwise will be sent to Uncategorized Experiments. If the project name does not already exist, will create a new project. rest_api_key: Optional. Rest API key found in settings. This is used to determine version number experiment_name: Optional. String representing the name for this particular experiment on experiment_key: Optional. If set, restores from existing experiment. offline: If api_key and save_dir are both given, this determines whether the experiment will be in online or offline mode. This is useful if you use save_dir to control the checkpoints directory and have a ~/.comet.config file but still want to run offline experiments. prefix: A string to put at the beginning of metric keys. \**kwargs: Additional arguments like `workspace`, `log_code`, etc. used by :class:`CometExperiment` can be passed as keyword arguments in this logger. Raises: ModuleNotFoundError: If required Comet package is not installed on the device. MisconfigurationException: If neither ``api_key`` nor ``save_dir`` are passed as arguments. """ LOGGER_JOIN_CHAR = "-" def __init__( self, api_key: Optional[str] = None, save_dir: Optional[str] = None, project_name: Optional[str] = None, rest_api_key: Optional[str] = None, experiment_name: Optional[str] = None, experiment_key: Optional[str] = None, offline: bool = False, prefix: str = "", **kwargs: Any, ): if not _COMET_AVAILABLE: raise ModuleNotFoundError(str(_COMET_AVAILABLE)) super().__init__() self._experiment = None self._save_dir: Optional[str] self.rest_api_key: Optional[str] # needs to be set before the first `comet_ml` import os.environ["COMET_DISABLE_AUTO_LOGGING"] = "1" import comet_ml # Determine online or offline mode based on which arguments were passed to CometLogger api_key = api_key or comet_ml.config.get_api_key(None, comet_ml.config.get_config()) if api_key is not None and save_dir is not None: self.mode = "offline" if offline else "online" self.api_key = api_key self._save_dir = save_dir elif api_key is not None: self.mode = "online" self.api_key = api_key self._save_dir = None elif save_dir is not None: self.mode = "offline" self._save_dir = save_dir else: # If neither api_key nor save_dir are passed as arguments, raise an exception raise MisconfigurationException("CometLogger requires either api_key or save_dir during initialization.")"CometLogger will be initialized in {self.mode} mode") self._project_name: Optional[str] = project_name self._experiment_key: Optional[str] = experiment_key self._experiment_name: Optional[str] = experiment_name self._prefix: str = prefix self._kwargs: Any = kwargs self._future_experiment_key: Optional[str] = None if rest_api_key is not None: from comet_ml.api import API # rest API, used to determine version number self.rest_api_key = rest_api_key self.comet_api = API(self.rest_api_key) else: self.rest_api_key = None self.comet_api = None @property @rank_zero_experiment def experiment(self) -> Union["Experiment", "ExistingExperiment", "OfflineExperiment"]: r"""Actual Comet object. To use Comet features in your :class:`~lightning.pytorch.core.LightningModule` do the following. Example:: self.logger.experiment.some_comet_function() """ if self._experiment is not None and self._experiment.alive: return self._experiment if self._future_experiment_key is not None: os.environ["COMET_EXPERIMENT_KEY"] = self._future_experiment_key from comet_ml import ExistingExperiment, Experiment, OfflineExperiment try: if self.mode == "online": if self._experiment_key is None: self._experiment = Experiment(api_key=self.api_key, project_name=self._project_name, **self._kwargs) self._experiment_key = self._experiment.get_key() else: self._experiment = ExistingExperiment( api_key=self.api_key, project_name=self._project_name, previous_experiment=self._experiment_key, **self._kwargs, ) else: self._experiment = OfflineExperiment( offline_directory=self.save_dir, project_name=self._project_name, **self._kwargs ) self._experiment.log_other("Created from", "pytorch-lightning") finally: if self._future_experiment_key is not None: os.environ.pop("COMET_EXPERIMENT_KEY") self._future_experiment_key = None if self._experiment_name: self._experiment.set_name(self._experiment_name) return self._experiment
[docs] @override @rank_zero_only def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None: params = _convert_params(params) params = _flatten_dict(params) self.experiment.log_parameters(params)
[docs] @override @rank_zero_only def log_metrics(self, metrics: Mapping[str, Union[Tensor, float]], step: Optional[int] = None) -> None: assert rank_zero_only.rank == 0, "experiment tried to log from global_rank != 0" # expects metrics to be a dictionary of detached tensors on CPU metrics_without_epoch = metrics.copy() for key, val in metrics_without_epoch.items(): if isinstance(val, Tensor): metrics_without_epoch[key] = val.cpu().detach() epoch = metrics_without_epoch.pop("epoch", None) metrics_without_epoch = _add_prefix(metrics_without_epoch, self._prefix, self.LOGGER_JOIN_CHAR) self.experiment.log_metrics(metrics_without_epoch, step=step, epoch=epoch)
def reset_experiment(self) -> None: self._experiment = None
[docs] @override @rank_zero_only def finalize(self, status: str) -> None: r"""When calling ``self.experiment.end()``, that experiment won't log any more data to Comet. That's why, if you need to log any more data, you need to create an ExistingCometExperiment. For example, to log data when testing your model after training, because when training is finalized :meth:`CometLogger.finalize` is called. This happens automatically in the :meth:`~CometLogger.experiment` property, when ``self._experiment`` is set to ``None``, i.e. ``self.reset_experiment()``. """ if self._experiment is None: # When using multiprocessing, finalize() should be a no-op on the main process, as no experiment has been # initialized there return self.experiment.end() self.reset_experiment()
@property @override def save_dir(self) -> Optional[str]: """Gets the save directory. Returns: The path to the save directory. """ return self._save_dir @property @override def name(self) -> str: """Gets the project name. Returns: The project name if it is specified, else "comet-default". """ # Don't create an experiment if we don't have one if self._experiment is not None and self._experiment.project_name is not None: return self._experiment.project_name if self._project_name is not None: return self._project_name return "comet-default" @property @override def version(self) -> str: """Gets the version. Returns: The first one of the following that is set in the following order 1. experiment id. 2. experiment key. 3. "COMET_EXPERIMENT_KEY" environment variable. 4. future experiment key. If none are present generates a new guid. """ # Don't create an experiment if we don't have one if self._experiment is not None: return if self._experiment_key is not None: return self._experiment_key if "COMET_EXPERIMENT_KEY" in os.environ: return os.environ["COMET_EXPERIMENT_KEY"] if self._future_experiment_key is not None: return self._future_experiment_key import comet_ml # Pre-generate an experiment key self._future_experiment_key = comet_ml.generate_guid() return self._future_experiment_key def __getstate__(self) -> Dict[str, Any]: state = self.__dict__.copy() # Save the experiment id in case an experiment object already exists, # this way we could create an ExistingExperiment pointing to the same # experiment state["_experiment_key"] = if self._experiment is not None else None # Remove the experiment object as it contains hard to pickle objects # (like network connections), the experiment object will be recreated if # needed later state["_experiment"] = None return state
[docs] @override def log_graph(self, model: Module, input_array: Optional[Tensor] = None) -> None: if self._experiment is not None: self._experiment.set_model_graph(model)