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](https://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.
+## Contents
+- `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
+```python
+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.

sample_embeddings.npy ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:c2488e6cc90eb143a9799ba547e9db1fe7fd1fa5f8e8354a26e982be033cbda0
+size 307328

sample_ids.txt ADDED Viewed

	@@ -0,0 +1,200 @@

+item_00000000
+item_00000001
+item_00000002
+item_00000003
+item_00000004
+item_00000005
+item_00000006
+item_00000007
+item_00000008
+item_00000009
+item_00000010
+item_00000011
+item_00000012
+item_00000013
+item_00000014
+item_00000015
+item_00000016
+item_00000017
+item_00000018
+item_00000019
+item_00000020
+item_00000021
+item_00000022
+item_00000023
+item_00000024
+item_00000025
+item_00000026
+item_00000027
+item_00000028
+item_00000029
+item_00000030
+item_00000031
+item_00000032
+item_00000033
+item_00000034
+item_00000035
+item_00000036
+item_00000037
+item_00000038
+item_00000039
+item_00000040
+item_00000041
+item_00000042
+item_00000043
+item_00000044
+item_00000045
+item_00000046
+item_00000047
+item_00000048
+item_00000049
+item_00000050
+item_00000051
+item_00000052
+item_00000053
+item_00000054
+item_00000055
+item_00000056
+item_00000057
+item_00000058
+item_00000059
+item_00000060
+item_00000061
+item_00000062
+item_00000063
+item_00000064
+item_00000065
+item_00000066
+item_00000067
+item_00000068
+item_00000069
+item_00000070
+item_00000071
+item_00000072
+item_00000073
+item_00000074
+item_00000075
+item_00000076
+item_00000077
+item_00000078
+item_00000079
+item_00000080
+item_00000081
+item_00000082
+item_00000083
+item_00000084
+item_00000085
+item_00000086
+item_00000087
+item_00000088
+item_00000089
+item_00000090
+item_00000091
+item_00000092
+item_00000093
+item_00000094
+item_00000095
+item_00000096
+item_00000097
+item_00000098
+item_00000099
+item_00000100
+item_00000101
+item_00000102
+item_00000103
+item_00000104
+item_00000105
+item_00000106
+item_00000107
+item_00000108
+item_00000109
+item_00000110
+item_00000111
+item_00000112
+item_00000113
+item_00000114
+item_00000115
+item_00000116
+item_00000117
+item_00000118
+item_00000119
+item_00000120
+item_00000121
+item_00000122
+item_00000123
+item_00000124
+item_00000125
+item_00000126
+item_00000127
+item_00000128
+item_00000129
+item_00000130
+item_00000131
+item_00000132
+item_00000133
+item_00000134
+item_00000135
+item_00000136
+item_00000137
+item_00000138
+item_00000139
+item_00000140
+item_00000141
+item_00000142
+item_00000143
+item_00000144
+item_00000145
+item_00000146
+item_00000147
+item_00000148
+item_00000149
+item_00000150
+item_00000151
+item_00000152
+item_00000153
+item_00000154
+item_00000155
+item_00000156
+item_00000157
+item_00000158
+item_00000159
+item_00000160
+item_00000161
+item_00000162
+item_00000163
+item_00000164
+item_00000165
+item_00000166
+item_00000167
+item_00000168
+item_00000169
+item_00000170
+item_00000171
+item_00000172
+item_00000173
+item_00000174
+item_00000175
+item_00000176
+item_00000177
+item_00000178
+item_00000179
+item_00000180
+item_00000181
+item_00000182
+item_00000183
+item_00000184
+item_00000185
+item_00000186
+item_00000187
+item_00000188
+item_00000189
+item_00000190
+item_00000191
+item_00000192
+item_00000193
+item_00000194
+item_00000195
+item_00000196
+item_00000197
+item_00000198
+item_00000199

throughput.json ADDED Viewed

	@@ -0,0 +1,33 @@

+{
+  "modality": "text",
+  "model": "sentence-transformers/all-MiniLM-L6-v2",
+  "device": "cuda",
+  "gpu": "NVIDIA GeForce RTX 5090",
+  "embedding_dim": 384,
+  "autobatch": {
+    "min_batch": 8,
+    "max_batch": 512,
+    "start_batch": 32,
+    "grow_free_fraction": 0.5,
+    "shrink_free_fraction": 0.2,
+    "grow_factor": 1.5,
+    "shrink_factor": 0.5,
+    "oom_factor": 0.5
+  },
+  "runs": [
+    {
+      "num_workers": 1,
+      "pre_run_counts": {
+        "pending": 500,
+        "in_progress": 0,
+        "done": 0,
+        "failed": 0,
+        "requeued": 0
+      },
+      "total_processed": 500,
+      "wall_seconds": 14.76,
+      "throughput": 33.9,
+      "note": "small-scale single-worker RTX 5090 run"
+    }
+  ]
+}