Datasets:

narinzar
/

distributed-embedding-generation-queue

license: mit
task_categories:
  - feature-extraction
language:
  - en
tags:
  - embeddings
  - sentence-transformers
  - all-MiniLM-L6-v2
  - feature-extraction
pretty_name: Distributed Embedding Generation Queue Sample
size_categories:
  - n<1K

Distributed Embedding Generation Queue - Sample Embeddings

Sample text embeddings produced by a durable producer/consumer GPU queue with resume-on-crash support. Source code: github.com/narinzar/distributed-embedding-generation-queue.

Generation method

Model: sentence-transformers/all-MiniLM-L6-v2 (384-dimensional vectors).
Pipeline: a durable SQLite task queue (WAL mode) feeds a GPU worker pool. Items are claimed atomically, embedded in batches, written as .npy files, and marked done. Orphaned in_progress items are re-queued on restart, so a run resumes without re-embedding finished items. Batch size is tuned to GPU headroom by an autobatcher (grows with free VRAM, shrinks on a caught OOM).
Run: 500 text items embedded on an RTX 5090 at 33.9 items/s (wall 14.76s, single worker). This was a small-scale single-worker run; the architecture supports scaling the worker count, and multi-worker throughput scaling is reproducible on Linux.

sample_embeddings.npy - a 200 x 384 float32 array, the first 200 vectors of the 500-item run.
sample_ids.txt - the item ids for those 200 vectors, one per line, aligned by row order.
throughput.json - the run summary: model, device, autobatch configuration, queue transition counts, and measured throughput.

Usage

import numpy as np
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="narinzar/distributed-embedding-generation-queue",
    filename="sample_embeddings.npy",
    repo_type="dataset",
)
vecs = np.load(path)  # (200, 384) float32

License

MIT.

Distributed Embedding Generation Queue - Sample Embeddings

Generation method

Contents

Usage

License