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':'microsoft/phi-1_5',
'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
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
)
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':'microsoft/phi-1_5',
'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
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()
starting the train job
huggingface_estimator.fit()
