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# $ twine upload dist/*
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-
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from __future__ import absolute_import
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import os
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from datetime import date
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# We don't declare our dependency on transformers here because we build with
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# different packages for different variants
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- VERSION = "1.0.1 "
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+ VERSION = "1.2.0 "
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install_requires = [
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"sagemaker-inference>=1.5.5" ,
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setup (
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name = "sagemaker-huggingface-inference-toolkit" ,
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- version = VERSION if os .getenv ("SM_HF_TOOLKIT_RELEASE" ) is not None else VERSION + 'b' + str (date .today ()).replace ('-' , '' ),
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+ version = VERSION
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+ if os .getenv ("SM_HF_TOOLKIT_RELEASE" ) is not None
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+ else VERSION + "b" + str (date .today ()).replace ("-" , "" ),
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author = "HuggingFace and Amazon Web Services" ,
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description = "Open source library for running inference workload with Hugging Face Deep Learning Containers on "
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- "Amazon SageMaker." ,
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+ "Amazon SageMaker." ,
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long_description = open ("README.md" , "r" , encoding = "utf-8" ).read (),
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long_description_content_type = "text/markdown" ,
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keywords = "NLP deep-learning transformer pytorch tensorflow BERT GPT GPT-2 AWS Amazon SageMaker Cloud" ,
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