wehub-resource-sync
68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
# flake8: noqa
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# __import_start__
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from starlette.requests import Request
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import ray
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from ray import serve
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# __import_end__
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# __model_start__
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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@serve.deployment(num_replicas=2, ray_actor_options={"num_cpus": 0.2, "num_gpus": 0})
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class Translator:
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def __init__(self):
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# Load model
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self.tokenizer = AutoTokenizer.from_pretrained("t5-small")
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self.model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
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def translate(self, text: str) -> str:
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# Run inference
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input_ids = self.tokenizer(
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f"translate English to French: {text}", return_tensors="pt"
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).input_ids
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output_ids = self.model.generate(
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input_ids, num_beams=4, early_stopping=True, max_length=300
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)
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# Post-process output to return only the translation text
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translation = self.tokenizer.decode(
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output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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return translation
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async def __call__(self, http_request: Request) -> str:
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english_text: str = await http_request.json()
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return self.translate(english_text)
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# __model_end__
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# __model_deploy_start__
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translator_app = Translator.bind()
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# __model_deploy_end__
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translator_app = Translator.options(ray_actor_options={}).bind()
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serve.run(translator_app)
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# __client_function_start__
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# File name: model_client.py
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import requests
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english_text = "Hello world!"
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response = requests.post("http://127.0.0.1:8000/", json=english_text)
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french_text = response.text
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print(french_text)
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# __client_function_end__
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assert french_text == "Bonjour monde!"
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serve.shutdown()
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ray.shutdown()
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