# 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 lightning.pytorch.loggers.logger import Logger, rank_zero_experiment
from lightning.pytorch.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"]
[docs]class CometLogger(Logger):
r"""Track your parameters, metrics, source code and more using `Comet
<https://www.comet.com/?utm_source=lightning.pytorch&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 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"), # 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 lightning.pytorch.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:`~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 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:`~lightning.pytorch.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
[docs] @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",
)
[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"
# 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,
)
[docs] @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
[docs] @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,
)
