Increment the counter

latency_reduction = 120 # ms return latency_reduction def optimize_scalability(self): # Initialize optimization metrics total_latency_reduction = 0 total_threads_used = 0 # Use a Thread…

for meta, gt in zip(metadata, ground_truth): if all(meta[key] == gt[key] for key in gt.keys()): correct += 1 return (correct / total) * 100 # Example ground truth data ground_truth = [...] # list of dictionarie…

my_counter = Counter('my_metric', 'My metric') # Increment the metric my_counter.inc() # Start the HTTP server to expose metrics start_http_server(port=8000) # Run indefinitely to keep the server alive while True: pass ``` ### Expla…

loss = loss_fn(outputs, batch_labels) val_loss += loss.item() val_loss /= len(val_loader) print(f"Epoch [{epoch+1}/{num_epochs}], Val Loss: {val_loss:.4f}") # Early stopping if val_loss < best_v…

query_embeddings = model(**query_encodings)['last_hidden_state'][:, 0, :] passage_embeddings = model(**passage_encodings)['last_hidden_state'][:, 0, :] # Apply dropout query_embeddings = dropout(query_embedd…

optimizer.step() # Zero gradients optimizer.zero_grad() # Validation loop scorer.eval() val_losses = [] with torch.no_grad(): for batch_inputs, batch_targets in val_loader: outpu…