import time
import json
from sagemaker.huggingface import HuggingFace
define Training Job Name
job_name = f’huggingface-donut-{time.strftime(“%Y-%m-%d-%H-%M-%S”, time.localtime())}’
stingify special tokens
special_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 job
hyperparameters = {
‘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 Estimator
huggingface_estimator = HuggingFace(
entry_point = ‘train.py’, # train script
source_dir = ‘./scripts’, # directory which includes all the files needed for training
instance_type = ‘ml.p3.2xlarge’, # instances type used for the training job
output_path = “s3://processed/donut-sagemaker/path/”,
code_location = “s3://processed/donut-sagemaker/code/”,
instance_count = 1, # the number of instances used for training
base_job_name = job_name, # the name of the training job
role = role, # Iam role used in training job to access AWS ressources, e.g. S3
volume_size = 100, # the size of the EBS volume in GB
transformers_version = ‘4.26’, # the transformers version used in the training job
pytorch_version = ‘1.13’, # the pytorch_version version used in the training job
py_version = ‘py39’, # the python version used in the training job
hyperparameters = hyperparameters
)
define a data input dictonary with our uploaded s3 uris
data = {‘training’: training_input_path}
starting the train job with our uploaded datasets as input
huggingface_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?