When using the Sentence Transformers library for embedding generation, there are several concurrency and multi-threading considerations to keep in mind. First, the library relies on PyTorch or TensorFlow under the hood, which have their own threading behaviors. While the library itself doesn’t explicitly prevent multi-threaded usage, PyTorch’s default configuration (especially with GPU acceleration) can introduce bottlenecks. For example, PyTorch uses a global interpreter lock (GIL)-like mechanism for GPU operations, which serializes GPU access across threads. This means that even if you parallelize embedding generation across threads, GPU-bound workloads may not scale linearly due to contention for GPU resources. CPU-bound tasks might see better threading performance, but this depends on the model size and batch processing strategy.
A key limitation is thread safety during model inference. While Sentence Transformers models are generally stateless after initialization, parallel threads invoking the encode() method could encounter issues if they modify shared resources (e.g., tokenizers or model parameters). For example, some tokenizers in Hugging Face’s transformers library (a dependency of Sentence Transformers) are not thread-safe when using padding or truncation. To avoid race conditions, you may need to implement thread-local storage for models or use a mutex to synchronize access. Additionally, batching inputs within a single thread often outperforms parallelizing small batches across threads, as GPUs process batched data more efficiently. For instance, encoding 100 texts in one batch on a single thread is typically faster than splitting them across 10 threads each handling 10 texts.
Hardware and framework constraints also play a role. GPU memory limits the number of concurrent threads or processes that can operate without causing out-of-memory errors. If you use multi-processing instead of threading (e.g., via Python’s multiprocessing), each process will load a separate copy of the model into memory, which can quickly exhaust GPU RAM. A workaround is to use a single process with asynchronous batching or a worker pool that shares the model instance. For example, frameworks like FastAPI can handle concurrent requests by offloading embedding tasks to a background thread pool while avoiding redundant model copies. Always test throughput with realistic workloads: overloading threads/processes can lead to diminishing returns or instability, especially with large models like all-mpnet-base-v2.