wehub-resource-sync
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
"""
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Documentation example and test for embedding model batch inference.
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"""
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import subprocess
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import sys
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subprocess.check_call([sys.executable, "-m", "pip", "install", "--upgrade", "ray[llm]"])
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subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy==1.26.4"])
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def run_embedding_example():
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# __embedding_example_start__
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import ray
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from ray.data.llm import vLLMEngineProcessorConfig, build_processor
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embedding_config = vLLMEngineProcessorConfig(
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model_source="sentence-transformers/all-MiniLM-L6-v2",
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task_type="embed",
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engine_kwargs=dict(
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enable_prefix_caching=False,
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enable_chunked_prefill=False,
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max_model_len=256,
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enforce_eager=True,
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),
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batch_size=32,
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concurrency=1,
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chat_template_stage=False, # Skip chat templating for embeddings
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detokenize_stage=False, # Skip detokenization for embeddings
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)
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embedding_processor = build_processor(
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embedding_config,
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preprocess=lambda row: dict(prompt=row["text"]),
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postprocess=lambda row: {
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"text": row["prompt"],
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"embedding": row["embeddings"],
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},
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)
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texts = [
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"Hello world",
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"This is a test sentence",
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"Embedding models convert text to vectors",
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]
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ds = ray.data.from_items([{"text": text} for text in texts])
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embedded_ds = embedding_processor(ds)
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embedded_ds.show(limit=1)
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# __embedding_example_end__
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if __name__ == "__main__":
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try:
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import torch
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if torch.cuda.is_available():
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run_embedding_example()
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else:
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print("Skipping embedding example (no GPU available)")
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except Exception as e:
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print(f"Skipping embedding example: {e}")
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