EOF occurred in violation of protocol (_ssl.c:2426)

Question

I am trying to get inference from a deployed pretrained model on Sagemaker notebook environment. While executing the below line of code, response = predictor.predict(serialized_data)

I am receiving an SSL error as below, SSLError: SSL validation failed for https://runtime.sagemaker.us-east-1.amazonaws.com/endpoints/pytorch-inference-2024-06-11-13-39-50-210/invocations EOF occurred in violation of protocol (_ssl.c:2426)

Expected Behavior: I should be receiving the response as I tested it manually in the notebook environment without deploying the model and just loading the weights from S3 bucket and using the same piece of code for inference.

Code: input_data = (interaction_data, mt_data) results = predict_fn(input_data, model)

Result: Iteration at 0: auc 0.964, map 0.439

Current Behavior: Assuming that Model gets executed properly as defined below, pytorch_model = PyTorchModel(model_data=f's3://{model_bucket}/{model_key}', role=role, entry_point='inference.py', framework_version='1.8.1', # Specify the PyTorch version py_version='py3', sagemaker_session=sagemaker_session) and the deployment takes place properly as, predictor = pytorch_model.deploy(instance_type='ml.m5.large', initial_instance_count=1)

While executing response = predictor.predict(serialized_data),

I receive the below error, SSLError: SSL validation failed for https://runtime.sagemaker.us-east-1.amazonaws.com/endpoints/pytorch-inference-2024-06-11-13-39-50-210/invocations EOF occurred in violation of protocol (_ssl.c:2426)

Reproduction Steps: Define your data path interaction_data = "s3://path_to_pkl/interaction.pkl" auxiliary_data = "s3://path_to_pkl/auxiliary.pkl" Define Model bucket model_bucket = 'path_to_model_bucket' model_key = 'Model-Structure/model.tar.gz'

Arrange your data properly, with open("interaction.pkl", 'rb') as f: data1 = CPU_Unpickler(f).load() with open("auxiliary.pkl", 'rb') as f: data2= CPU_Unpickler(f).load() serialized_data = pickle.dumps({ 'data1': data1, 'data2': data2 }) Define your model, pytorch_model = PyTorchModel(model_data=f's3://{model_bucket}/{model_key}', role=role, entry_point='inference.py', framework_version='1.8.1', # Specify the PyTorch version py_version='py3', sagemaker_session=sagemaker_session) Deploy the model predictor = pytorch_model.deploy(instance_type='ml.m5.large', initial_instance_count=1) Get the response response = predictor.predict(serialized_data)

I also have an inference.py which I am using for model evaluation and obtaining the response, a general practice for sagemaker environment.