Source code for pytorch_lightning.loggers.comet
# Copyright The PyTorch Lightning 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 typing import Any, Dict, Optional, Union
import torch
from torch import is_tensor
import pytorch_lightning as pl
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
from pytorch_lightning.utilities import _module_available, rank_zero_only
from pytorch_lightning.utilities.exceptions import MisconfigurationException
log = logging.getLogger(__name__)
_COMET_AVAILABLE = _module_available("comet_ml")
if _COMET_AVAILABLE:
import comet_ml
from comet_ml import ExistingExperiment as CometExistingExperiment
from comet_ml import Experiment as CometExperiment
from comet_ml import OfflineExperiment as CometOfflineExperiment
try:
from comet_ml.api import API
except ImportError: # pragma: no-cover
# For more information, see: https://www.comet.ml/docs/python-sdk/releases/#release-300
from comet_ml.papi import API # pragma: no-cover
else:
# needed for test mocks, these tests shall be updated
comet_ml = None
CometExperiment, CometExistingExperiment, CometOfflineExperiment = None, None, None
API = None
[docs]class CometLogger(LightningLoggerBase):
r"""
Log using `Comet.ml <https://www.comet.ml>`_.
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"),
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="default", # 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(
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="default", # Optional
)
trainer = Trainer(logger=comet_logger)
Args:
api_key: Required in online mode. API key, found on Comet.ml. 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, Comet.ml will create a new project.
rest_api_key: Optional. Rest API key found in Comet.ml settings.
This is used to determine version number
experiment_name: Optional. String representing the name for this particular experiment on Comet.ml.
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:
ImportError:
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,
):
if comet_ml is None:
raise ImportError(
"You want to use `comet_ml` logger which is not installed yet,"
" install it with `pip install comet-ml`."
)
super().__init__()
self._experiment = None
# 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.")
log.info(f"CometLogger will be initialized in {self.mode} mode")
self._project_name = project_name
self._experiment_key = experiment_key
self._experiment_name = experiment_name
self._prefix = prefix
self._kwargs = kwargs
self._future_experiment_key = None
if rest_api_key is not None:
# Comet.ml 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
self._kwargs = kwargs
@property
@rank_zero_experiment
def experiment(self):
r"""
Actual Comet object. To use Comet features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
Example::
self.logger.experiment.some_comet_function()
"""
if self._experiment is not None:
return self._experiment
if self._future_experiment_key is not None:
os.environ["COMET_EXPERIMENT_KEY"] = self._future_experiment_key
try:
if self.mode == "online":
if self._experiment_key is None:
self._experiment = CometExperiment(
api_key=self.api_key, project_name=self._project_name, **self._kwargs
)
self._experiment_key = self._experiment.get_key()
else:
self._experiment = CometExistingExperiment(
api_key=self.api_key,
project_name=self._project_name,
previous_experiment=self._experiment_key,
**self._kwargs,
)
else:
self._experiment = CometOfflineExperiment(
offline_directory=self.save_dir, project_name=self._project_name, **self._kwargs
)
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] @rank_zero_only
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
params = self._convert_params(params)
params = self._flatten_dict(params)
self.experiment.log_parameters(params)
[docs] @rank_zero_only
def log_metrics(self, metrics: Dict[str, Union[torch.Tensor, float]], step: Optional[int] = None) -> None:
assert rank_zero_only.rank == 0, "experiment tried to log from global_rank != 0"
# Comet.ml 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 is_tensor(val):
metrics_without_epoch[key] = val.cpu().detach()
epoch = metrics_without_epoch.pop("epoch", None)
metrics_without_epoch = self._add_prefix(metrics_without_epoch)
self.experiment.log_metrics(metrics_without_epoch, step=step, epoch=epoch)
def reset_experiment(self):
self._experiment = None
[docs] @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()``.
"""
self.experiment.end()
self.reset_experiment()
@property
def save_dir(self) -> Optional[str]:
return self._save_dir
@property
def name(self) -> str:
# 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
def version(self) -> str:
# Don't create an experiment if we don't have one
if self._experiment is not None:
return self._experiment.id
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
# Pre-generate an experiment key
self._future_experiment_key = comet_ml.generate_guid()
return self._future_experiment_key
def __getstate__(self):
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.id 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] def log_graph(self, model: "pl.LightningModule", input_array=None) -> None:
if self._experiment is not None:
self._experiment.set_model_graph(model)
