To handle very large datasets that don't fit into memory, use chunked processing and streaming techniques. Break the dataset into smaller batches, process them sequentially, and avoid loading all data at once. For example, read files line-by-line, use memory-mapped formats like Parquet, or employ generators in Python to lazily load data. During training, leverage techniques like gradient accumulation (processing small batches but updating weights less frequently) or distributed training across multiple GPUs. For inference/embedding, process data in fixed-size batches and write results incrementally to disk instead of storing all embeddings in memory.
The Sentence Transformers library directly supports chunked processing for embedding generation. Its encode() method accepts generators or iterators, allowing you to stream data. For example, you can read a large text file in chunks, feed each chunk to model.encode(), and save embeddings to a file or database without holding all data in RAM. Here’s a simplified example:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
def chunk_generator(file_path, batch_size=1000):
with open(file_path, 'r') as f:
while True:
batch = [line.strip() for _, line in zip(range(batch_size), f)]
if not batch:
break
yield batch
for embeddings in model.encode(chunk_generator('large_data.txt'), batch_size=512):
# Save embeddings to disk/database here
For training large datasets, Sentence Transformers integrates with PyTorch’s DataLoader, which supports custom datasets that load data on-demand. Use a dataset class that reads from disk incrementally or uses memory mapping. For instance, you could create a Dataset that loads individual records or small shards from storage when requested. Combine this with PyTorch’s DataLoader and num_workers for parallel data loading to avoid memory bottlenecks.
Key considerations:
- Use file formats like JSONL or Parquet for chunk-friendly storage.
- Disable in-memory caching in datasets (e.g., Hugging Face
datasetslibrary’sload_from_diskwithstreaming=True). - For reproducibility, implement deterministic chunking or seed-controlled shuffling at the batch level.
- Monitor disk I/O performance – compressed files or SSDs may be necessary for very large datasets.
Sentence Transformers doesn’t include built-in streaming for training data, but its compatibility with standard PyTorch data-loading tools allows you to implement memory-efficient pipelines using established patterns.