Repository: Lightning-AI/pytorch-lightning

Issue:
CometLogger does not implement name() and version() class methods
Explicitly creating a CometLogger instance and passing it to Trainer using trainer(logger=my_comet_logger) raises a NotImplementedError because CometLogger does not implement the name() and version() class methods.

Below is the traceback:
`
Traceback (most recent call last):
  File "main.py", line 126, in <module>
    trainer.fit(model)
  File "/home/ryan/miniconda3/envs/compling/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 351, in fit
    self.single_gpu_train(model)
  File "/home/ryan/miniconda3/envs/compling/lib/python3.7/site-packages/pytorch_lightning/trainer/dp_mixin.py", line 77, in single_gpu_train
    self.run_pretrain_routine(model)
  File "/home/ryan/miniconda3/envs/compling/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 471, in run_pretrain_routine
    self.train()
  File "/home/ryan/miniconda3/envs/compling/lib/python3.7/site-packages/pytorch_lightning/trainer/train_loop_mixin.py", line 60, in train
    self.run_training_epoch()
  File "/home/ryan/miniconda3/envs/compling/lib/python3.7/site-packages/pytorch_lightning/trainer/train_loop_mixin.py", line 99, in run_training_epoch
    output = self.run_training_batch(batch, batch_nb)
  File "/home/ryan/miniconda3/envs/compling/lib/python3.7/site-packages/pytorch_lightning/trainer/train_loop_mixin.py", line 255, in run_training_batch
    self.main_progress_bar.set_postfix(**self.training_tqdm_dict)
  File "/home/ryan/miniconda3/envs/compling/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 309, in training_tqdm_dict
    if self.logger is not None and self.logger.version is not None:
  File "/home/ryan/miniconda3/envs/compling/lib/python3.7/site-packages/pytorch_lightning/logging/base.py", line 76, in version
    raise NotImplementedError("Sub-classes must provide a version property")
`




Discussion:

Resolving patch:
diff --git a/pytorch_lightning/logging/comet_logger.py b/pytorch_lightning/logging/comet_logger.py
--- a/pytorch_lightning/logging/comet_logger.py
+++ b/pytorch_lightning/logging/comet_logger.py
@@ -1,25 +1,126 @@
+from logging import getLogger
+
 try:
     from comet_ml import Experiment as CometExperiment
+    from comet_ml import OfflineExperiment as CometOfflineExperiment
+    from comet_ml.papi import API
 except ImportError:
     raise ImportError('Missing comet_ml package.')
 
+from torch import is_tensor
+
 from .base import LightningLoggerBase, rank_zero_only
+from ..utilities.debugging import MisconfigurationException
+
+logger = getLogger(__name__)
 
 
 class CometLogger(LightningLoggerBase):
-    def __init__(self, *args, **kwargs):
-        super(CometLogger, self).__init__()
-        self.experiment = CometExperiment(*args, **kwargs)
+    def __init__(self, api_key=None, save_dir=None, workspace=None,
+                 rest_api_key=None, project_name=None, experiment_name=None, **kwargs):
+        """
+        Initialize a Comet.ml logger. Requires either an API Key (online mode) or a local directory path (offline mode)
+
+        :param str api_key: Required in online mode. API key, found on Comet.ml
+        :param str save_dir: Required in offline mode. The path for the directory to save local comet logs
+        :param str workspace: Optional. Name of workspace for this user
+        :param str project_name: Optional. Send your experiment to a specific project.
+        Otherwise will be sent to Uncategorized Experiments.
+        If project name does not already exists Comet.ml will create a new project.
+        :param str rest_api_key: Optional. Rest API key found in Comet.ml settings.
+        This is used to determine version number
+        :param str experiment_name: Optional. String representing the name for this particular experiment on Comet.ml
+        """
+        super().__init__()
+        self._experiment = None
+
+        # Determine online or offline mode based on which arguments were passed to CometLogger
+        if save_dir is not None and api_key is not None:
+            # If arguments are passed for both save_dir and api_key, preference is given to online mode
+            self.mode = "online"
+            self.api_key = api_key
+        elif api_key is not None:
+            self.mode = "online"
+            self.api_key = api_key
+        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.")
+
+        logger.info(f"CometLogger will be initialized in {self.mode} mode")
+
+        self.workspace = workspace
+        self.project_name = project_name
+        self._kwargs = kwargs
+
+        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
+
+        if experiment_name:
+            try:
+                self.name = experiment_name
+            except TypeError as e:
+                logger.exception("Failed to set experiment name for comet.ml logger")
+
+    @property
+    def experiment(self):
+        if self._experiment is not None:
+            return self._experiment
+
+        if self.mode == "online":
+            self._experiment = CometExperiment(
+                api_key=self.api_key,
+                workspace=self.workspace,
+                project_name=self.project_name,
+                **self._kwargs
+            )
+        else:
+            self._experiment = CometOfflineExperiment(
+                offline_directory=self.save_dir,
+                workspace=self.workspace,
+                project_name=self.project_name,
+                **self._kwargs
+            )
+
+        return self._experiment
 
     @rank_zero_only
     def log_hyperparams(self, params):
         self.experiment.log_parameters(vars(params))
 
     @rank_zero_only
-    def log_metrics(self, metrics, step_num):
-        # self.experiment.set_epoch(self, metrics.get('epoch', 0))
-        self.experiment.log_metrics(metrics)
+    def log_metrics(self, metrics, step_num=None):
+        # 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()
+
+        self.experiment.log_metrics(metrics, step=step_num)
 
     @rank_zero_only
     def finalize(self, status):
         self.experiment.end()
+
+    @property
+    def name(self):
+        return self.experiment.project_name
+
+    @name.setter
+    def name(self, value):
+        self.experiment.set_name(value)
+
+    @property
+    def version(self):
+        if self.project_name and self.rest_api_key:
+            # Determines the number of experiments in this project, and returns the next integer as the version number
+            nb_exps = len(self.comet_api.get_experiments(self.workspace, self.project_name))
+            return nb_exps + 1
+        else:
+            return None


Test patch:
diff --git a/.run_local_tests.sh b/.run_local_tests.sh
--- a/.run_local_tests.sh
+++ b/.run_local_tests.sh
@@ -2,6 +2,7 @@
 rm -rf _ckpt_*
 rm -rf tests/save_dir*
 rm -rf tests/mlruns_*
+rm -rf tests/cometruns*
 rm -rf tests/tests/*
 rm -rf lightning_logs
 coverage run --source pytorch_lightning -m py.test pytorch_lightning tests pl_examples -v --doctest-modules
diff --git a/tests/test_y_logging.py b/tests/test_y_logging.py
--- a/tests/test_y_logging.py
+++ b/tests/test_y_logging.py
@@ -137,8 +137,83 @@ def test_mlflow_pickle():
     testing_utils.clear_save_dir()
 
 
-def test_custom_logger(tmpdir):
+def test_comet_logger():
+    """
+    verify that basic functionality of Comet.ml logger works
+    """
+    reset_seed()
+
+    try:
+        from pytorch_lightning.logging import CometLogger
+    except ModuleNotFoundError:
+        return
+
+    hparams = testing_utils.get_hparams()
+    model = LightningTestModel(hparams)
+
+    root_dir = os.path.dirname(os.path.realpath(__file__))
+    comet_dir = os.path.join(root_dir, "cometruns")
+
+    # We test CometLogger in offline mode with local saves
+    logger = CometLogger(
+        save_dir=comet_dir,
+        project_name="general",
+        workspace="dummy-test",
+    )
+
+    trainer_options = dict(
+        max_nb_epochs=1,
+        train_percent_check=0.01,
+        logger=logger
+    )
+
+    trainer = Trainer(**trainer_options)
+    result = trainer.fit(model)
+
+    print('result finished')
+    assert result == 1, "Training failed"
+
+    testing_utils.clear_save_dir()
+
 
+def test_comet_pickle():
+    """
+    verify that pickling trainer with comet logger works
+    """
+    reset_seed()
+
+    try:
+        from pytorch_lightning.logging import CometLogger
+    except ModuleNotFoundError:
+        return
+
+    hparams = testing_utils.get_hparams()
+    model = LightningTestModel(hparams)
+
+    root_dir = os.path.dirname(os.path.realpath(__file__))
+    comet_dir = os.path.join(root_dir, "cometruns")
+
+    # We test CometLogger in offline mode with local saves
+    logger = CometLogger(
+        save_dir=comet_dir,
+        project_name="general",
+        workspace="dummy-test",
+    )
+
+    trainer_options = dict(
+        max_nb_epochs=1,
+        logger=logger
+    )
+
+    trainer = Trainer(**trainer_options)
+    pkl_bytes = pickle.dumps(trainer)
+    trainer2 = pickle.loads(pkl_bytes)
+    trainer2.logger.log_metrics({"acc": 1.0})
+
+    testing_utils.clear_save_dir()
+
+
+def test_custom_logger(tmpdir):
     class CustomLogger(LightningLoggerBase):
         def __init__(self):
             super().__init__()