Error in lab 3 - PDS course 3

This cell will take approximately 5-10 minutes to run.

pytorch_endpoint_name = ‘endpoint-{}’.format(timestamp)

predictor = model.deploy(initial_instance_count=1,

AttributeError Traceback (most recent call last)

/opt/conda/lib/python3.7/site-packages/sagemaker/ in deploy(self, initial_instance_count, instance_type, serializer, deserializer, accelerator_type, endpoint_name, tags, kms_key, wait, data_capture_config, async_inference_config, serverless_inference_config, **kwargs)
985 The following call
→ 986
987 >>> Model(entry_point=‘’,
988 … dependencies=[‘my/libs/common’, ‘virtual-env’])

/opt/conda/lib/python3.7/site-packages/sagemaker/ in _create_sagemaker_model(self, instance_type, accelerator_type, tags)
516 s3_kms_key (str): the kms key to encrypt the output with
517 tags (list[dict]): List of tags for labeling an edge packaging job. For
→ 518 more, see
519 Tag - Amazon SageMaker.

/opt/conda/lib/python3.7/site-packages/sagemaker/pytorch/ in prepare_container_def(self, instance_type, accelerator_type)
239 self._upload_code(deploy_key_prefix, repack=self._is_mms_version())
240 deploy_env = dict(self.env)
→ 241 deploy_env.update(self._framework_env_vars())
243 if self.model_server_workers:

AttributeError: ‘PyTorchModel’ object has no attribute ‘_framework_env_vars’
You can review the endpoint in the AWS console and check its status.

Hi @Ravi4biz thanks for reaching out, I think the error might be due to skipping one of the cells in between can you please run again and let us know the status, if its still the same we will look into it