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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()