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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 for key, val in metrics.items(): if is_tensor(val): metrics[key] = val.cpu().detach() metrics_without_epoch = metrics.copy() 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)

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