ProcessPoolExecutor

3. **executor.map**: The `executor.map` function applies the `worker` function to each document in the list concurrently. This is more efficient than manually starting and joining threads. 4. **Latency Calculation**: The code measures the …

- Process multiple texts in a single batch rather than one at a time. Batching can significantly reduce the overhead associated with individual inference requests. - Use the `batch_size` parameter when calling the model. 5. **Optimiz…

Here's an optimized version of your code using parallel processing and batch processing: ```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from concurrent.future…

model = MyModel().to(device) optimizer = optim.Adam(model.parameters(), lr=0.001) # Define the update logic def update_model(model, optimizer, data_loader): model.train() for data, _ in data_loader: data = data.to(device) …

- Use `ProcessPoolExecutor` to handle multiple text chunks in parallel. - Adjust `max_workers` based on your system's capabilities to balance between CPU usage and performance. 3. **Batch Processing**: - The `process_text_chunks` …

with ProcessPoolExecutor(max_workers=max_workers) as executor: for token_freq in executor.map(tokenize_text, text_chunks): results.append(token_freq) return results # Example usage text_chunks = ["This is an exa…