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import torch |
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from typing import Dict, List, Any |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline |
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device = 0 if torch.cuda.is_available() else -1 |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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tokenizer = AutoTokenizer.from_pretrained(path) |
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model = AutoModelForSeq2SeqLM.from_pretrained(path ,low_cpu_mem_usage=True) |
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self.pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer,device=device) |
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def __call__(self, data: Any) -> List[List[Dict[str, float]]]: |
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inputs = data.pop("inputs", data) |
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parameters = data.pop("parameters", None) |
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if parameters is not None: |
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prediction = self.pipeline(inputs, **parameters) |
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else: |
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prediction = self.pipeline(inputs) |
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return prediction |
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