venv/lib/python3.11/site-packages/pytorch_lightning/loggers/comet.py

# 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
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Comet Logger
------------
"""

import logging
import os
from argparse import Namespace
from collections.abc import Mapping
from typing import TYPE_CHECKING, Any, Literal, 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 _convert_params
from lightning_fabric.utilities.rank_zero import _get_rank
from pytorch_lightning.loggers.logger import Logger, rank_zero_experiment
from pytorch_lightning.utilities.rank_zero import rank_zero_only

if TYPE_CHECKING:
    from comet_ml import ExistingExperiment, Experiment, OfflineExperiment

log = logging.getLogger(__name__)
_COMET_AVAILABLE = RequirementCache("comet-ml>=3.44.4", module="comet_ml")

FRAMEWORK_NAME = "pytorch-lightning"
comet_experiment = Union["Experiment", "ExistingExperiment", "OfflineExperiment"]


class CometLogger(Logger):
    r"""Track your parameters, metrics, source code and more using `Comet
    <https://www.comet.com/?utm_source=pytorch_lightning&utm_medium=referral>`_.

    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 pytorch_lightning import Trainer
        from pytorch_lightning.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"),  # Optional
            workspace=os.environ.get("COMET_WORKSPACE"),  # Optional
            project="default_project",  # Optional
            experiment_key=os.environ.get("COMET_EXPERIMENT_KEY"),  # Optional
            name="lightning_logs",  # Optional
        )
        trainer = Trainer(logger=comet_logger)

    **OFFLINE MODE**

    .. code-block:: python

        from pytorch_lightning.loggers import CometLogger

        # arguments made to CometLogger are passed on to the comet_ml.Experiment class
        comet_logger = CometLogger(
            workspace=os.environ.get("COMET_WORKSPACE"),  # Optional
            project="default_project",  # Optional
            name="lightning_logs",  # Optional
            online=False
        )
        trainer = Trainer(logger=comet_logger)

    **Log Hyperparameters:**

    Log parameters used to initialize a :class:`~pytorch_lightning.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 nested parameters
        logger.log_hyperparams({"specific": {'param': {'subparam': "value"}}})

    **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})

        # add nested metrics
        logger.log_metrics({"specific": {'metric': {'submetric': "value"}}})

    **Access the Comet Experiment object:**

    You can gain access to the underlying Comet
    `Experiment <https://www.comet.com/docs/v2/api-and-sdk/python-sdk/reference/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 = PIL.Image.open("<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 <https://tinyurl.com/22phzw5s>`__
        - `Comet Documentation <https://www.comet.com/docs/v2/integrations/ml-frameworks/pytorch-lightning/>`__

    Args:
        api_key: Comet API key. It's recommended to configure the API Key with `comet login`.
        workspace: Comet workspace name. If not provided, uses the default workspace.
        project: Comet project name. Defaults to `Uncategorized`.
        experiment_key: The Experiment identifier to be used for logging. This is used either to append
            data to an Existing Experiment or to control the key of new experiments (for example to match another
            identifier). Must be an alphanumeric string whose length is between 32 and 50 characters.
        mode: Control how the Comet experiment is started.
            * ``"get_or_create"``: Starts a fresh experiment if required, or persists logging to an existing one.
            * ``"get"``: Continue logging to an existing experiment identified by the ``experiment_key`` value.
            * ``"create"``: Always creates of a new experiment, useful for HPO sweeps.
        online: If True, the data will be logged to Comet server, otherwise it will be stored
            locally in an offline experiment. Default is ``True``.
        prefix: The prefix to add to names of the logged metrics.
            example: prefix=`exp1`, then metric name will be logged as `exp1_metric_name`
        **kwargs: Additional arguments like `name`, `log_code`, `offline_directory` 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.

    """

    def __init__(
        self,
        *,
        api_key: Optional[str] = None,
        workspace: Optional[str] = None,
        project: Optional[str] = None,
        experiment_key: Optional[str] = None,
        mode: Optional[Literal["get_or_create", "get", "create"]] = None,
        online: Optional[bool] = None,
        prefix: Optional[str] = None,
        **kwargs: Any,
    ):
        if not _COMET_AVAILABLE:
            raise ModuleNotFoundError(str(_COMET_AVAILABLE))

        super().__init__()

        ##################################################
        # HANDLE PASSED OLD TYPE PARAMS

        # handle old "experiment_name" param
        if "experiment_name" in kwargs:
            log.warning("The parameter `experiment_name` is deprecated, please use `name` instead.")
            experiment_name = kwargs.pop("experiment_name")

            if "name" not in kwargs:
                kwargs["name"] = experiment_name
            else:
                log.warning("You specified both `experiment_name` and `name` parameters, please use `name` only")

        # handle old "project_name" param
        if "project_name" in kwargs:
            log.warning("The parameter `project_name` is deprecated, please use `project` instead.")
            if project is None:
                project = kwargs.pop("project_name")
            else:
                log.warning("You specified both `project_name` and `project` parameters, please use `project` only")

        # handle old "offline" experiment flag
        if "offline" in kwargs:
            log.warning("The parameter `offline is deprecated, please use `online` instead.")
            if online is None:
                online = kwargs.pop("offline")
            else:
                log.warning("You specified both `offline` and `online` parameters, please use `online` only")

        # handle old "save_dir" param
        if "save_dir" in kwargs:
            log.warning("The parameter `save_dir` is deprecated, please use `offline_directory` instead.")
            if "offline_directory" not in kwargs:
                kwargs["offline_directory"] = kwargs.pop("save_dir")
            else:
                log.warning(
                    "You specified both `save_dir` and `offline_directory` parameters, "
                    "please use `offline_directory` only"
                )
        ##################################################

        self._api_key: Optional[str] = api_key
        self._experiment: Optional[comet_experiment] = None
        self._workspace: Optional[str] = workspace
        self._mode: Optional[Literal["get_or_create", "get", "create"]] = mode
        self._online: Optional[bool] = online
        self._project_name: Optional[str] = project
        self._experiment_key: Optional[str] = experiment_key
        self._prefix: Optional[str] = prefix
        self._kwargs: dict[str, Any] = kwargs

        # needs to be set before the first `comet_ml` import
        # because comet_ml imported after another machine learning libraries (Torch)
        os.environ["COMET_DISABLE_AUTO_LOGGING"] = "1"

        import comet_ml

        config_kwargs = self._kwargs.copy()
        if online is False:
            config_kwargs["disabled"] = True
        self._comet_config = comet_ml.ExperimentConfig(**config_kwargs)

        # create real experiment only on main node/process (when strategy=auto/ddp)
        if _get_rank() is not None and _get_rank() != 0:
            return

        self._create_experiment()

    def _create_experiment(self) -> None:
        import comet_ml

        self._experiment = comet_ml.start(
            api_key=self._api_key,
            workspace=self._workspace,
            project=self._project_name,
            experiment_key=self._experiment_key,
            mode=self._mode,
            online=self._online,
            experiment_config=self._comet_config,
        )

        if self._experiment is None:
            raise comet_ml.exceptions.ExperimentNotFound("Failed to create Comet experiment.")

        self._experiment_key = self._experiment.get_key()
        self._project_name = self._experiment.project_name
        self._experiment.log_other("Created from", FRAMEWORK_NAME)

    @property
    @rank_zero_experiment
    def experiment(self) -> comet_experiment:
        r"""Actual Comet object. To use Comet features in your :class:`~pytorch_lightning.core.LightningModule` do the
        following.

        Example::

            self.logger.experiment.some_comet_function()

        """

        # if by some chance there is no experiment created yet (for example, when strategy=ddp_spawn)
        # then we will create a new one
        if not self._experiment:
            self._create_experiment()

        return self._experiment

    @override
    @rank_zero_only
    def log_hyperparams(self, params: Union[dict[str, Any], Namespace]) -> None:
        params = _convert_params(params)
        self.experiment.__internal_api__log_parameters__(
            parameters=params,
            framework=FRAMEWORK_NAME,
            flatten_nested=True,
            source="manual",
        )

    @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"
        # Comet.com 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)
        self.experiment.__internal_api__log_metrics__(
            metrics_without_epoch,
            step=step,
            epoch=epoch,
            prefix=self._prefix,
            framework=FRAMEWORK_NAME,
        )

    @override
    @rank_zero_only
    def finalize(self, status: str) -> None:
        """We will not end experiment (will not call self._experiment.end()) here to have an ability to continue using
        it after training is complete but instead of ending we will upload/save all the data."""
        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

        # just save the data
        self.experiment.flush()

    @property
    @override
    def save_dir(self) -> Optional[str]:
        """Gets the save directory.

        Returns:
            The path to the save directory.

        """
        return self._comet_config.offline_directory

    @property
    @override
    def name(self) -> Optional[str]:
        """Gets the project name.

        Returns:
            The project name if it is specified.

        """
        return self._project_name

    @property
    @override
    def version(self) -> Optional[str]:
        """Gets the version.

        Returns:
            The experiment key if present

        """
        # Don't create an experiment if we don't have one
        if self._experiment is not None:
            return self._experiment.get_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"] = self._experiment.get_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

    @override
    def log_graph(self, model: Module, input_array: Optional[Tensor] = None) -> None:
        if self._experiment is not None:
            self._experiment.__internal_api__set_model_graph__(
                graph=model,
                framework=FRAMEWORK_NAME,
            )