rename variables nb -> num (#567)

* rename nb -> num

* flake8

* batch_nb, epoch_nb, gpu_nb, split_nb

* add _num deprecations

pytorch_lightning/logging/__init__.py

@@ -52,7 +52,7 @@ only the first process in DDP training logs data.
             pass
 
         @rank_zero_only
-        def log_metrics(self, metrics, step_num):
+        def log_metrics(self, metrics, step_idx):
             # metrics is a dictionary of metric names and values
             # your code to record metrics goes here
             pass

pytorch_lightning/logging/base.py

@@ -21,12 +21,11 @@ class LightningLoggerBase(object):
     def __init__(self):
         self._rank = 0
 
-    def log_metrics(self, metrics, step_num):
-        """Record metrics
+    def log_metrics(self, metrics, step_idx):
+        """Record metrics.
 
-        :param metric: Dictionary with metric names as keys and measured
-        quanties as values
-        :param step_num: Step number at which the metrics should be recorded
+        :param float metric: Dictionary with metric names as keys and measured quanties as values
+        :param int|None step_idx: Step number at which the metrics should be recorded
         """
         raise NotImplementedError()
 

pytorch_lightning/logging/comet.py

@@ -145,13 +145,13 @@ class CometLogger(LightningLoggerBase):
         self.experiment.log_parameters(vars(params))
 
     @rank_zero_only
-    def log_metrics(self, metrics, step_num=None):
+    def log_metrics(self, metrics, step_idx=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)
+        self.experiment.log_metrics(metrics, step=step_idx)
 
     @rank_zero_only
     def finalize(self, status):
@@ -169,7 +169,7 @@ class CometLogger(LightningLoggerBase):
     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
+            num_exps = len(self.comet_api.get_experiments(self.workspace, self.project_name))
+            return num_exps + 1
         else:
             return None

pytorch_lightning/logging/mlflow.py

@@ -68,7 +68,7 @@ class MLFlowLogger(LightningLoggerBase):
             self.experiment.log_param(self.run_id, k, v)
 
     @rank_zero_only
-    def log_metrics(self, metrics, step_num=None):
+    def log_metrics(self, metrics, step_idx=None):
         timestamp_ms = int(time() * 1000)
         for k, v in metrics.items():
             if isinstance(v, str):
@@ -76,7 +76,7 @@ class MLFlowLogger(LightningLoggerBase):
                     f"Discarding metric with string value {k}={v}"
                 )
                 continue
-            self.experiment.log_metric(self.run_id, k, v, timestamp_ms, step_num)
+            self.experiment.log_metric(self.run_id, k, v, timestamp_ms, step_idx)
 
     def save(self):
         pass

pytorch_lightning/logging/test_tube.py

@@ -76,10 +76,10 @@ class TestTubeLogger(LightningLoggerBase):
         self.experiment.argparse(params)
 
     @rank_zero_only
-    def log_metrics(self, metrics, step_num=None):
+    def log_metrics(self, metrics, step_idx=None):
         # TODO: HACK figure out where this is being set to true
         self.experiment.debug = self.debug
-        self.experiment.log(metrics, global_step=step_num)
+        self.experiment.log(metrics, global_step=step_idx)
 
     @rank_zero_only
     def save(self):