profiling results output

stats.print_stats() end_time = datetime.datetime.now() latency = calculate_latency(start_time, end_time) print(f"Latency: {latency} hours") if __name__ == "__main__": main() ``` ### Steps to Follow 1. **Run the Scrip…

1 0.000 0.000 10.001 0.000 <stdin>:1(critical_assignment_code) 1 0.000 0.000 0.000 0.000 <string>:1(<module>) ``` In this example, the `critical_assignment_code` function is taking the most time. You …

[Turn 3243] Assistant: Great! Running the script with `cProfile` will help you pinpoint the areas that are taking the most time. Once you have the profiling output, you can focus on optimizing those specific parts. Here's a quick recap of w…

1 0.000 0.000 10.001 0.000 <stdin>:1(main) 1 0.000 0.000 10.001 0.000 <stdin>:1(critical_assignment_code) 1 .000 0.000 0.000 0.000 <string>:1(<module>) ``` In this example, the `cri…

with profiler.profile(record_shapes=True, use_cuda=True) as prof: with profiler.record_function("model_training"): for i, (batch_inputs, batch_targets) in enumerate(dataloader): with autocast(): # Us…

# Backward pass scaler.scale(loss).backward() # Update weights if (i + 1) % accumulation_steps == 0: scaler.step(optimizer) …

### Example Usage When you run the code, you should see output similar to the following: ```plaintext Processed 1500 queries in 1.50 seconds ``` This indicates that the system is capable of processing 1,500 queries per minute efficiently…

futures = [executor.submit(reformulate_query, query) for query in queries] for future in as_completed(futures): results.append(future.result()) return results # Define a function to tokenize queries def toke…

Test the reformulation function with a subset of your queries to identify and fix specific issues. Gradually increase the test set size until you are confident in the performance. ```python import pandas as pd # Load the query data querie…