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  1. train +36 -0
train ADDED
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+ import sagemaker
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+ import boto3
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+ from sagemaker.huggingface import HuggingFace
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+
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+ try:
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+ role = sagemaker.get_execution_role()
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+ except ValueError:
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+ iam = boto3.client('iam')
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+ role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']
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+
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+ hyperparameters = {
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+ 'model_name_or_path':'liuhaotian/llava-v1.5-13b',
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+ 'output_dir':'/opt/ml/model'
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+ # add your remaining hyperparameters
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+ # more info here https://github.com/huggingface/transformers/tree/v4.26.0/examples/pytorch/language-modeling
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+ }
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+
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+ # git configuration to download our fine-tuning script
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+ git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.26.0'}
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+
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+ # creates Hugging Face estimator
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+ huggingface_estimator = HuggingFace(
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+ entry_point='run_mlm.py',
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+ source_dir='./examples/pytorch/language-modeling',
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+ instance_type='ml.p3.2xlarge',
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+ instance_count=1,
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+ role=role,
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+ git_config=git_config,
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+ transformers_version='4.26.0',
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+ pytorch_version='1.13.1',
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+ py_version='py39',
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+ hyperparameters = hyperparameters
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+ )
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+
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+ # starting the train job
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+ huggingface_estimator.fit()