Logger consistency (#397)

* added comet logger

* bug fix in cases where comet was not imported before torch

* fixed mlflow logger to be consistent with docs, updated cometLogger and cometLoggers docs + flake 8 compliance

docs/Trainer/Logging.md

@@ -72,7 +72,6 @@ mlf_logger = MLFlowLogger(
 )
 trainer = Trainer(logger=mlf_logger)
 ```
-
 Use the logger anywhere in you LightningModule as follows:
 ```python
 def train_step(...):
@@ -83,6 +82,30 @@ def any_lightning_module_function_or_hook(...):
     self.logger.experiment.whatever_ml_flow_supports(...)
 ```
 
+---
+#### Comet.ml
+
+Log using [comet](https://www.comet.ml)
+
+```{.python}
+from pytorch_lightning.logging import CometLogger
+# arguments made to CometLogger are passed on to the comet_ml.Experiment class
+comet_logger = CometLogger(
+    api_key=os.environ["COMET_KEY"],
+    workspace=os.environ["COMET_KEY"],
+)
+trainer = Trainer(logger=comet_logger)
+```
+Use the logger anywhere in you LightningModule as follows:
+```python
+def train_step(...):
+    # example
+    self.logger.experiment.whatever_comet_ml_supports(...)
+
+def any_lightning_module_function_or_hook(...):
+    self.logger.experiment.whatever_comet_ml_supports(...)
+```
+
 ---
 #### Custom logger
 

pytorch_lightning/logging/__init__.py

@@ -8,3 +8,7 @@ try:
     from .mlflow_logger import MLFlowLogger
 except ModuleNotFoundError:
     pass
+try:
+    from .comet_logger import CometLogger
+except ModuleNotFoundError:
+    pass

pytorch_lightning/logging/comet_logger.py

@@ -0,0 +1,27 @@
+from time import time
+from logging import getLogger
+from os import environ
+from comet_ml import Experiment as CometExperiment
+from .base import LightningLoggerBase, rank_zero_only
+
+# needed to prevent ImportError and duplicated logs.
+environ["COMET_DISABLE_AUTO_LOGGING"] = "1"
+
+
+class CometLogger(LightningLoggerBase):
+    def __init__(self, *args, **kwargs):
+        super(CometLogger, self).__init__()
+        self.experiment = CometExperiment(*args, **kwargs)
+
+    @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)
+
+    @rank_zero_only
+    def finalize(self, status):
+        self.experiment.end()

pytorch_lightning/logging/mlflow_logger.py

@@ -11,7 +11,7 @@ logger = getLogger(__name__)
 class MLFlowLogger(LightningLoggerBase):
     def __init__(self, experiment_name, tracking_uri=None, tags=None):
         super().__init__()
-        self.client = mlflow.tracking.MlflowClient(tracking_uri)
+        self.experiment = mlflow.tracking.MlflowClient(tracking_uri)
         self.experiment_name = experiment_name
         self._run_id = None
         self.tags = tags
@@ -21,22 +21,22 @@ class MLFlowLogger(LightningLoggerBase):
         if self._run_id is not None:
             return self._run_id
 
-        experiment = self.client.get_experiment_by_name(self.experiment_name)
+        experiment = self.experiment.get_experiment_by_name(self.experiment_name)
         if experiment is None:
             logger.warning(
                 f"Experiment with name f{self.experiment_name} not found. Creating it."
             )
-            self.client.create_experiment(self.experiment_name)
-            experiment = self.client.get_experiment_by_name(self.experiment_name)
+            self.experiment.create_experiment(self.experiment_name)
+            experiment = self.experiment.get_experiment_by_name(self.experiment_name)
 
-        run = self.client.create_run(experiment.experiment_id, tags=self.tags)
+        run = self.experiment.create_run(experiment.experiment_id, tags=self.tags)
         self._run_id = run.info.run_id
         return self._run_id
 
     @rank_zero_only
     def log_hyperparams(self, params):
         for k, v in vars(params).items():
-            self.client.log_param(self.run_id, k, v)
+            self.experiment.log_param(self.run_id, k, v)
 
     @rank_zero_only
     def log_metrics(self, metrics, step_num=None):
@@ -47,7 +47,7 @@ class MLFlowLogger(LightningLoggerBase):
                     f"Discarding metric with string value {k}={v}"
                 )
                 continue
-            self.client.log_metric(self.run_id, k, v, timestamp_ms, step_num)
+            self.experiment.log_metric(self.run_id, k, v, timestamp_ms, step_num)
 
     def save(self):
         pass
@@ -56,4 +56,4 @@ class MLFlowLogger(LightningLoggerBase):
     def finalize(self, status="FINISHED"):
         if status == 'success':
             status = 'FINISHED'
-        self.client.set_terminated(self.run_id, status)
+        self.experiment.set_terminated(self.run_id, status)