{"id": "282e1ade-ca47-45c9-bc0a-a67d4fb97296", "dataset": "founders-1", "job_name": "embed", "command": "ls-embed \"founders-1\" \"points\" \"\ud83e\udd17-nvidia___NV-Embed-v2\" --prefix=\"\" --batch_size=100 --max_seq_length=512", "status": "error", "last_update": "2025-01-06 21:53:15.075111", "progress": ["Loading environment variables from: /Users/jimmyzhang/Development/latent-scope/.env", "RUNNING: embedding-003", "MODEL ID \ud83e\udd17-nvidia___NV-Embed-v2", "MODEL <latentscope.models.providers.transformers.TransformersEmbedProvider object at 0x12737c230>", "loading nvidia/NV-Embed-v2", "A new version of the following files was downloaded from https://huggingface.co./nvidia/NV-Embed-v2:", "- configuration_nvembed.py", ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.", "A new version of the following files was downloaded from https://huggingface.co./nvidia/NV-Embed-v2:", "- modeling_nvembed.py", ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.", "", "Downloading shards:   0%|          | 0/4 [00:00<?, ?it/s]", "Downloading shards:  25%|\u2588\u2588\u258c       | 1/4 [02:30<07:30, 150.10s/it]", "Downloading shards:  50%|\u2588\u2588\u2588\u2588\u2588     | 2/4 [04:55<04:54, 147.39s/it]", "Downloading shards:  75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c  | 3/4 [07:24<02:28, 148.08s/it]", "Downloading shards: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [07:47<00:00, 98.88s/it]", "Downloading shards: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [07:47<00:00, 116.99s/it]", "", "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]", "Loading checkpoint shards:  25%|\u2588\u2588\u258c       | 1/4 [00:08<00:26,  8.73s/it]", "Loading checkpoint shards:  50%|\u2588\u2588\u2588\u2588\u2588     | 2/4 [00:20<00:21, 10.58s/it]", "Loading checkpoint shards:  75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c  | 3/4 [00:31<00:10, 10.92s/it]", "Loading checkpoint shards: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [00:33<00:00,  7.26s/it]", "Loading checkpoint shards: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [00:33<00:00,  8.39s/it]", "Traceback (most recent call last):", "File \"/Users/jimmyzhang/Development/latent-scope/testenv/bin/ls-embed\", line 8, in <module>", "sys.exit(main())", "^^^^^^", "File \"/Users/jimmyzhang/Development/latent-scope/latentscope/scripts/embed.py\", line 63, in main", "embed(args.dataset_id, args.text_column, args.model_id, args.prefix, args.rerun, args.dimensions, args.batch_size, args.max_seq_length)", "File \"/Users/jimmyzhang/Development/latent-scope/latentscope/scripts/embed.py\", line 97, in embed", "model.load_model()", "File \"/Users/jimmyzhang/Development/latent-scope/latentscope/models/providers/transformers.py\", line 13, in load_model", "self.model = SentenceTransformer(self.name, trust_remote_code=True, device=self.device)#, backend=\"onnx\")", "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^", "File \"/Users/jimmyzhang/Development/latent-scope/testenv/lib/python3.12/site-packages/sentence_transformers/SentenceTransformer.py\", line 345, in __init__", "self.to(device)", "File \"/Users/jimmyzhang/Development/latent-scope/testenv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1173, in to", "return self._apply(convert)", "^^^^^^^^^^^^^^^^^^^^", "File \"/Users/jimmyzhang/Development/latent-scope/testenv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 779, in _apply", "module._apply(fn)", "File \"/Users/jimmyzhang/Development/latent-scope/testenv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 779, in _apply", "module._apply(fn)", "File \"/Users/jimmyzhang/Development/latent-scope/testenv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 779, in _apply", "module._apply(fn)", "[Previous line repeated 4 more times]", "File \"/Users/jimmyzhang/Development/latent-scope/testenv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 804, in _apply", "param_applied = fn(param)", "^^^^^^^^^", "File \"/Users/jimmyzhang/Development/latent-scope/testenv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1159, in convert", "return t.to(", "^^^^^", "RuntimeError: MPS backend out of memory (MPS allocated: 27.20 GB, other allocations: 464.00 KB, max allowed: 27.20 GB). Tried to allocate 224.00 MB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure)."], "times": ["2025-01-06 21:42:52.500542", "2025-01-06 21:42:52.533346", "2025-01-06 21:42:52.533490", "2025-01-06 21:42:53.106446", "2025-01-06 21:42:53.106605", "2025-01-06 21:42:55.497647", "2025-01-06 21:42:55.498266", "2025-01-06 21:42:55.498907", "2025-01-06 21:42:55.788729", "2025-01-06 21:42:55.788933", "2025-01-06 21:42:55.789185", "2025-01-06 21:42:55.978448", "2025-01-06 21:45:26.078342", "2025-01-06 21:47:51.570396", "2025-01-06 21:50:20.473407", "2025-01-06 21:50:43.936441", "2025-01-06 21:50:43.937347", "2025-01-06 21:50:43.937959", "2025-01-06 21:50:44.899902", "2025-01-06 21:50:53.632738", "2025-01-06 21:51:05.502163", "2025-01-06 21:51:16.829781", "2025-01-06 21:51:18.465309", "2025-01-06 21:51:18.465572", "2025-01-06 21:51:18.465688", "2025-01-06 21:53:15.030701", "2025-01-06 21:53:15.031782", "2025-01-06 21:53:15.033243", "2025-01-06 21:53:15.033342", "2025-01-06 21:53:15.033634", "2025-01-06 21:53:15.034006", "2025-01-06 21:53:15.035730", "2025-01-06 21:53:15.035814", "2025-01-06 21:53:15.036266", "2025-01-06 21:53:15.036621", "2025-01-06 21:53:15.037055", "2025-01-06 21:53:15.037381", "2025-01-06 21:53:15.038363", "2025-01-06 21:53:15.038731", "2025-01-06 21:53:15.039500", "2025-01-06 21:53:15.039675", "2025-01-06 21:53:15.039819", "2025-01-06 21:53:15.039958", "2025-01-06 21:53:15.040267", "2025-01-06 21:53:15.040400", "2025-01-06 21:53:15.040533", "2025-01-06 21:53:15.040671", "2025-01-06 21:53:15.040800", "2025-01-06 21:53:15.040929", "2025-01-06 21:53:15.073978", "2025-01-06 21:53:15.074217", "2025-01-06 21:53:15.074369", "2025-01-06 21:53:15.074514", "2025-01-06 21:53:15.074822", "2025-01-06 21:53:15.075107"], "run_id": "embedding-003"}