| import sagemaker | |
| import boto3 | |
| from sagemaker.huggingface import HuggingFace | |
| try: | |
| role = sagemaker.get_execution_role() | |
| except ValueError: | |
| iam = boto3.client('iam') | |
| role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn'] | |
| hyperparameters = { | |
| 'model_name_or_path':'Open-Orca/OpenOrca-Preview1-13B', | |
| 'output_dir':'/opt/ml/model' | |
| # add your remaining hyperparameters | |
| # more info here https://github.com/huggingface/transformers/tree/v4.26.0/examples/pytorch/language-modeling | |
| } | |
| # git configuration to download our fine-tuning script | |
| git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.26.0'} | |
| # creates Hugging Face estimator | |
| huggingface_estimator = HuggingFace( | |
| entry_point='run_clm.py', | |
| source_dir='./examples/pytorch/language-modeling', | |
| instance_type='ml.p3.2xlarge', | |
| instance_count=1, | |
| role=role, | |
| git_config=git_config, | |
| transformers_version='4.26.0', | |
| pytorch_version='1.13.1', | |
| py_version='py39', | |
| hyperparameters = hyperparameters | |
| ) | |
| # starting the train job | |
| huggingface_estimator.fit() |