import sagemaker
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
	'model_name_or_path':'etmckinley/BERFALTER',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.17.0/examples/pytorch/question-answering
}

# 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_qa.py',
	source_dir='./examples/pytorch/question-answering',
	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()
region:us
{}
TAGS
#region-us 

import sagemaker
from sagemaker.huggingface import HuggingFace

# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
	'model_name_or_path':'etmckinley/BERFALTER',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here URL
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'URL 'v4.17.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_qa.py',
	source_dir='./examples/pytorch/question-answering',
	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()
# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
	'model_name_or_path':'etmckinley/BERFALTER',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here URL
} # git configuration to download our fine-tuning script
git_config = {'repo': 'URL 'v4.17.0'} # creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_qa.py',
	source_dir='./examples/pytorch/question-answering',
	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()
TAGS
#region-us 
 # gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
	'model_name_or_path':'etmckinley/BERFALTER',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here URL
} # git configuration to download our fine-tuning script
git_config = {'repo': 'URL 'v4.17.0'} # creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_qa.py',
	source_dir='./examples/pytorch/question-answering',
	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()
passage: TAGS
#region-us 
# gets role for executing training job
role = sagemaker.get_execution_role()
hyperparameters = {
	'model_name_or_path':'etmckinley/BERFALTER',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here URL
}# git configuration to download our fine-tuning script
git_config = {'repo': 'URL 'v4.17.0'}# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='run_qa.py',
	source_dir='./examples/pytorch/question-answering',
	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()