import sagemaker from sagemaker.huggingface import HuggingFace

gets role for executing training job

role = sagemaker.get_execution_role() hyperparameters = { 'model_name_or_path':'Grossmend/rudialogpt3_medium_based_on_gpt2', 'output_dir':'/opt/ml/model' # add your remaining hyperparameters # more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/language-modeling }

git configuration to download our fine-tuning script

git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.17.0'}

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='4.17.0', pytorch_version='1.10.2', py_version='py38', hyperparameters = hyperparameters )

starting the train job

huggingface_estimator.fit()