Delete app.py

app.py

@@ -1,36 +0,0 @@
-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':'HuggingFaceTB/SmolVLM2-256M-Video-Instruct',
-	'output_dir':'/opt/ml/model'
-	# add your remaining hyperparameters
-	# more info here https://github.com/huggingface/transformers/tree/v4.49.0/path/to/script
-}
-
-# git configuration to download our fine-tuning script
-git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.49.0'}
-
-# creates Hugging Face estimator
-huggingface_estimator = HuggingFace(
-	entry_point='train.py',
-	source_dir='./path/to/script',
-	instance_type='ml.p3.2xlarge',
-	instance_count=1,
-	role=role,
-	git_config=git_config,
-	transformers_version='4.49.0',
-	pytorch_version='2.5.1',
-	py_version='py311',
-	hyperparameters = hyperparameters
-)
-print("congrats")
-# starting the train job
-huggingface_estimator.fit()