rename variables nb -> num (#567)

* rename nb -> num

* flake8

* batch_nb, epoch_nb, gpu_nb, split_nb

* add _num deprecations

pl_examples/domain_templates/gan.py

@@ -90,12 +90,12 @@ class GAN(pl.LightningModule):
     def adversarial_loss(self, y_hat, y):
         return F.binary_cross_entropy(y_hat, y)
 
-    def training_step(self, batch, batch_nb, optimizer_i):
+    def training_step(self, batch, batch_idx, optimizer_idx):
         imgs, _ = batch
         self.last_imgs = imgs
 
         # train generator
-        if optimizer_i == 0:
+        if optimizer_idx == 0:
             # sample noise
             z = torch.randn(imgs.shape[0], self.hparams.latent_dim)
 
@@ -125,7 +125,7 @@ class GAN(pl.LightningModule):
             return output
 
         # train discriminator
-        if optimizer_i == 1:
+        if optimizer_idx == 1:
             # Measure discriminator's ability to classify real from generated samples
 
             # how well can it label as real?

pl_examples/full_examples/imagenet/imagenet_example.py

@@ -234,7 +234,7 @@ def main(hparams):
     trainer = pl.Trainer(
         default_save_path=hparams.save_path,
         gpus=hparams.gpus,
-        max_nb_epochs=hparams.epochs,
+        max_num_epochs=hparams.epochs,
         distributed_backend=hparams.distributed_backend,
         use_amp=hparams.use_16bit
     )

pl_examples/multi_node_examples/multi_node_ddp2_demo.py

@@ -31,7 +31,7 @@ def main(hparams):
     # ------------------------
     trainer = Trainer(
         gpus=2,
-        nb_gpu_nodes=2,
+        num_nodes=2,
         distributed_backend='ddp2'
     )
 

pl_examples/multi_node_examples/multi_node_ddp_demo.py

@@ -31,7 +31,7 @@ def main(hparams):
     # ------------------------
     trainer = Trainer(
         gpus=2,
-        nb_gpu_nodes=2,
+        num_nodes=2,
         distributed_backend='ddp'
     )