Update pep8 (#78)

* simplify requirements file

* style

* apply linter

appendix-A/02_installing-python-libraries/python_environment_check.py

@@ -25,9 +25,10 @@ def get_packages(pkgs):
                 except AttributeError:
                     try:
                         versions.append(imported.version_info)
-                    except:
+                    except AttributeError:
                         try:
-                            import importlib, importlib_metadata
+                            import importlib
+                            import importlib_metadata
                             imported = importlib.import_module(p)
                             version = importlib_metadata.version(p)
                             versions.append(version)

appendix-A/03_main-chapter-code/DDP-script.py

@@ -91,11 +91,11 @@ def prepare_dataset():
     train_loader = DataLoader(
         dataset=train_ds,
         batch_size=2,
-        shuffle=False, # NEW: False because of DistributedSampler below
+        shuffle=False,  # NEW: False because of DistributedSampler below
         pin_memory=True,
         drop_last=True,
         # NEW: chunk batches across GPUs without overlapping samples:
-        sampler=DistributedSampler(train_ds) # NEW
+        sampler=DistributedSampler(train_ds)  # NEW
     )
     test_loader = DataLoader(
         dataset=test_ds,
@@ -108,14 +108,14 @@ def prepare_dataset():
 # NEW: wrapper
 def main(rank, world_size, num_epochs):
 
-    ddp_setup(rank, world_size) # NEW: initialize process groups
+    ddp_setup(rank, world_size)  # NEW: initialize process groups
 
     train_loader, test_loader = prepare_dataset()
     model = NeuralNetwork(num_inputs=2, num_outputs=2)
     model.to(rank)
     optimizer = torch.optim.SGD(model.parameters(), lr=0.5)
 
-    model = DDP(model, device_ids=[rank]) # NEW: wrap model with DDP
+    model = DDP(model, device_ids=[rank])  # NEW: wrap model with DDP
     # the core model is now accessible as model.module
 
     for epoch in range(num_epochs):
@@ -123,15 +123,15 @@ def main(rank, world_size, num_epochs):
         model.train()
         for features, labels in train_loader:
 
-            features, labels = features.to(rank), labels.to(rank) # New: use rank
+            features, labels = features.to(rank), labels.to(rank)  # New: use rank
             logits = model(features)
-            loss = F.cross_entropy(logits, labels) # Loss function
+            loss = F.cross_entropy(logits, labels)  # Loss function
 
             optimizer.zero_grad()
             loss.backward()
             optimizer.step()
 
-            ### LOGGING
+            # LOGGING
             print(f"[GPU{rank}] Epoch: {epoch+1:03d}/{num_epochs:03d}"
                   f" | Batchsize {labels.shape[0]:03d}"
                   f" | Train/Val Loss: {loss:.2f}")
@@ -142,7 +142,7 @@ def main(rank, world_size, num_epochs):
     test_acc = compute_accuracy(model, test_loader, device=rank)
     print(f"[GPU{rank}] Test accuracy", test_acc)
 
-    destroy_process_group() # NEW: cleanly exit distributed mode
+    destroy_process_group()  # NEW: cleanly exit distributed mode
 
 
 def compute_accuracy(model, dataloader, device):