What if the memory usage keeps growing when encoding a large number of sentences — could there be a memory leak, and how do I manage memory in this scenario?

When encoding a large number of sentences, increasing memory usage could indicate a memory leak, but it might also stem from inefficient resource management. A memory leak occurs when objects are not properly released after use, causing gradual accumulation. For example, if your code retains references to processed data (like sentence embeddings) in unintended places—such as global variables, caches, or unclosed iterators—memory can grow uncontrollably. However, even without a leak, high memory use might result from loading all data at once instead of processing it incrementally. For instance, loading 1 million sentences into a list before encoding can exhaust available RAM, especially with large models like BERT.

To manage memory, first verify if objects are being unnecessarily retained. Use tools like Python’s tracemalloc or memory-profiler to track allocations. For example, if embeddings are stored in a list that isn’t cleared between batches, memory usage will spike. Instead, process data in smaller batches and explicitly delete variables (e.g., del embeddings followed by gc.collect()). If using frameworks like PyTorch, ensure tensors are moved to the CPU or deleted after GPU operations. Additionally, consider streaming data instead of loading it all at once. For instance, read sentences from a file line by line, encode them incrementally, and write results to disk immediately to avoid holding all data in memory.

Optimize your code structure to minimize long-lived objects. For example, use generators instead of lists to process sentences on-the-fly. If using a machine learning library like Hugging Face Transformers, disable caching (e.g., model.config.use_cache = False) and avoid retaining intermediate outputs. For large workloads, consider model quantization or using smaller models (e.g., DistilBERT) to reduce memory per inference. If the issue persists, inspect third-party libraries for known leaks—some versions of TensorFlow or PyTorch may have unpatched issues. Finally, monitor memory usage systematically: tools like psutil can log memory trends, helping you pinpoint when and where allocations occur. Addressing these factors often resolves growth issues without requiring major architectural changes.