Endpoint Deployment
Hello everyone,I deployed my BERT classification model for batch jobs on Sagemaker withcreate Hugging Face Model Class
huggingface_model = HuggingFaceModel(
   model_data=model_uri, # configuration for loading model from Hub
   role=role, # iam role with permissions to create an Endpoint
   transformers_version="4.6", # transformers version used
   pytorch_version="1.7", # pytorch version used
   py_version='py36', # python version used
)

# create Transformer to run our batch job
batch_job = huggingface_model.transformer(
    instance_count=1,
    instance_type='ml.g4dn.xlarge',
    output_path=output_s3_path, # we are using the same s3 path to save the output with the input
    strategy='SingleRecord')


# starts batch transform job and uses s3 data as input
batch_job.transform(
    data=s3_file_uri,
    content_type='application/json',    
    split_type='Line')

utput_file = f"{dataset_jsonl_file}.out"
output_path = s3_path_join(output_s3_path,output_file)

# download file
S3Downloader.download(output_path,'.')

batch_transform_result = []
with open(output_file) as f:
    for line in f:
        # converts jsonline array to normal array
        line = "[" + line.replace("[","").replace("]",",") + "]"
        batch_transform_result = literal_eval(line)Anyways, when I want to predict a new batch it feels like I always have to start my Notebook on Sagemaker Studio etc. Is there a way to create an API which I can feed with my data to predict from the outside? Is there any cool tutorial or anything? Thanks in advance
2021-09-20T07:13:03Z
Hey@marlon89,I sadly don’t have yet a tutorial or a sample for it. I am looking into creating something like that in the next weeks, withcdksupport.I created an architecture of how this API can look like.SageMaker-batch-cycle.drawio1522×1602 231 KBYou can basically leverage AWS Lambda with S3 Trigger to start an create you batch transform jobs after a file Is uploaded to a prefix on an s3 bucket.