how to debug invocation timeout error in sagemaker batch transform?

Question

I am experimenting with sagemaker, using a container from list here , https://github.com/aws/deep-learning-containers/blob/master/available_images.md to run my model and overwriting model_fn and predict_fn functions in inference.py file for loading model and prediction as shown in link here (https://github.com/PacktPublishing/Learn-Amazon-SageMaker-second-edition/blob/main/Chapter%2007/huggingface/src/torchserve-predictor.py) . I keep getting invocations timeout error => "Model server did not respond to /invocations request within 3600 seconds". am i missing anything in my inference.py code , as to adding something to response to the ping/healthcheck?

file : inference.py

import json
import torch
from transformers import AutoConfig, AutoTokenizer, DistilBertForSequenceClassification

JSON_CONTENT_TYPE = 'application/json'

def model_fn(model_dir):
    config_path = '{}/config.json'.format(model_dir)
    model_path =  '{}/pytorch_model.bin'.format(model_dir)
    config = AutoConfig.from_pretrained(config_path)
   ...

def predict_fn(input_data, model):
    //return predictions
...

Answer 1

The issue is not with the health checks. It is with the container not responding to the /invocations request and this is can be due to model taking longer time than expected to get predictions from the input data.