Open miaekim opened 4 years ago
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Currently Python SageMaker SDK supports local mode.
import numpy from sagemaker.mxnet import MXNetModel model_location = 's3://mybucket/my_model.tar.gz' code_location = 's3://mybucket/sourcedir.tar.gz' image_url = get_image_uri(sess.boto_region_name, 'image', repo_version="latest") s3_model = MXNetModel(model_data=model_location, role='SageMakerRole', image=image_url, entry_point='mnist.py', source_dir=code_location) predictor = s3_model.deploy(initial_instance_count=1, instance_type='local') data = numpy.zeros(shape=(1, 1, 28, 28)) predictor.predict(data) # Tear down the endpoint container and delete the corresponding endpoint configuration predictor.delete_endpoint() # Deletes the model predictor.delete_model()
And right now this SDK forces us to create endpoint when we define model.
Is there any plan to support local mode for inferencing?
Hi @miaekim,
sagemaker-spark does not provide hosting/inference in local mode or other SageMaker services in local mode like Python SDK.
Got it. Please update this issue when you plan to implement it :)
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System Information
Describe the problem
Currently Python SageMaker SDK supports local mode.
And right now this SDK forces us to create endpoint when we define model.
Is there any plan to support local mode for inferencing?