Models is not saved in S3 bucket location
import timeimport jsonfrom sagemaker.huggingface import HuggingFacedefine Training Job Namejob_name = f’huggingface-donut-{time.strftime(“%Y-%m-%d-%H-%M-%S”, time.localtime())}’stingify special tokensspecial_tokens = ‘,’.join(map(str, processor.tokenizer.special_tokens_map_extended[“additional_special_tokens”]))training_input_path = f’s3://processed/donut-sagemaker/train’hyperparameters, which are passed into the training jobhyperparameters = {‘model_id’: model_id,                                # pre-trained model‘special_tokens’: json.dumps(special_tokens),        # special tokens which will be added to the tokenizer‘dataset_path’: training_input_path,                 # path where sagemaker will save training dataset‘epochs’: 1,                                         # number of training epochs‘per_device_train_batch_size’: 8,                    # batch size for training‘gradient_checkpointing’: True,                      # batch size for training‘lr’: 4e-5,                                          # learning rate used during training}create the Estimatorhuggingface_estimator = HuggingFace(entry_point          = ‘train.py’,        # train scriptsource_dir           = ‘./scripts’,       # directory which includes all the files needed for traininginstance_type        = ‘ml.p3.2xlarge’,   # instances type used for the training joboutput_path          = “s3://processed/donut-sagemaker/path/”,code_location        = “s3://processed/donut-sagemaker/code/”,instance_count       = 1,                 # the number of instances used for trainingbase_job_name        = job_name,          # the name of the training jobrole                 = role,              # Iam role used in training job to access AWS ressources, e.g. S3volume_size          = 100,               # the size of the EBS volume in GBtransformers_version = ‘4.26’,            # the transformers version used in the training jobpytorch_version      = ‘1.13’,            # the pytorch_version version used in the training jobpy_version           = ‘py39’,            # the python version used in the training jobhyperparameters      =  hyperparameters)define a data input dictonary with our uploaded s3 urisdata = {‘training’: training_input_path}starting the train job with our uploaded datasets as inputhuggingface_estimator.fit(data, wait=True)model.save_pretrained("s3://processed/donut-sagemaker/model/")
processor.save_pretrained("s3://processed/donut-sagemaker/model/")I am trying run this training on sagemaker pipeline , the training is success but my models is not saved in that location can anyone help me to resolve it?