To scale Sentence Transformer inference for large datasets or high-throughput scenarios, you can leverage parallel processing across multiple GPUs and optimize data handling. Here’s how to approach this:
1. Data Parallelism with Batch Processing
The simplest method is to split the dataset into smaller batches and distribute them across GPUs. For example, using PyTorch’s DataParallel or DistributedDataParallel, each GPU processes a subset of the input batch simultaneously. Sentence Transformers can handle batched text inputs, so you maximize GPU utilization by increasing the batch size until memory limits are reached. To avoid bottlenecks, preprocess data into a format that allows rapid loading (e.g., memory-mapped arrays) and use a DataLoader with multiple workers. For variable-length texts, dynamically pad batches to the longest sequence in the batch to minimize wasted computation. Tools like NVIDIA’s DALI can further accelerate data loading and preprocessing.
2. Model Parallelism and Optimization
While data parallelism is often sufficient, very large models might require splitting the transformer layers across GPUs (model parallelism). However, Sentence Transformers like all-mpnet-base-v2 are typically small enough to fit on a single GPU, making this unnecessary. Instead, focus on optimizing the model itself: convert it to ONNX or TensorRT for faster inference, apply quantization (e.g., 16-bit or 8-bit precision), and enable kernel fusion. Libraries like Hugging Face’s optimum or NVIDIA’s Triton Inference Server can automate these optimizations and manage parallel execution, reducing latency and increasing throughput.
3. Distributed Inference with Horizontal Scaling For extremely large datasets, distribute the workload across multiple machines. For example, use Apache Spark or Ray to partition the dataset, process chunks on separate GPU nodes, and aggregate results. In cloud environments, auto-scaling GPU clusters (e.g., AWS SageMaker or Kubernetes with GPU nodes) can dynamically adjust resources based on demand. Asynchronous processing with a task queue (e.g., Celery or Redis) helps decouple ingestion from inference, ensuring GPUs stay saturated. For real-time scenarios, Triton Inference Server’s dynamic batching combines multiple requests into a single batch, improving throughput without increasing latency.
Example Workflow
- Preprocess the dataset into shards.
- Use a cluster of GPU nodes, each running Triton with an optimized ONNX model.
- Distribute shards via Spark, letting each node process its assigned data.
- Store embeddings in a distributed database like FAISS for efficient retrieval.
This approach balances computational efficiency, hardware utilization, and scalability, ensuring low latency even for billions of texts.