I want to deploy the model which has no artifacts(model zip file) using sagemaker and use its endpoint in my application to get the results. Can someone help me in doing this
The steps to deploy a model in SageMaker is to -
Create a SageMaker Model
Create an endpoint configuration
Create the endpoint
SageMaker needs to access to a model.tar.gz file stored in S3 with the model and code stored in it. You can also deploy a model by training an Estimator and deploying that to an endpoint.
You can not deploy a model that has no model artifacts to a SageMaker hosted endpoint.
https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-deployment.html
I work for AWS & my opinions are my own
Related
I followed this page to register a model version from a different account: https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-version.html#model-registry-version-xaccount
After adding the required permissions, i have a step to registry model in SageMaker pipeline, but looks like cloudwatch logs isn't available for this step.
My question is: is there a way to verify my model version has been successfully registered from that different account? Assuming i can use aws cli to print out model versions but i tried multiple command none of them works....
I am training models in sagemaker with its Pytorch Module and Huggingface module.
When the training is done, It successfully uploads model artifacts into s3.
But the problem is the code automatically uploads the model with encryption though I did not give any encryption key.
If I go to s3 and check the file details, I found
Encryption key
AWS Key Management Service key (SSE-KMS)
But I do not want encryption. Because I copy this artefact to another s3 on a different AWS, it creates a problem and needs extra permission.
Can anyone say, How to save the model without encryption in S3 from sageamaker?
I am using these modules
from sagemaker.huggingface import HuggingFace
from sagemaker.pytorch import PyTorch
I plan to build an application which asks user input in the form of news and predicts it as fake or true.
i have trained the model using automl google cloud platform (GCP) on vertex AI platform. I have created the endpoint.
How to proceed further? How to build an app without downloading anything on local system? (all of this should be built on GCP)
I need to create an endpoint in sagemaker for a multi input model, the way to do it with Tensorflow 1 has been answered in this question How to use multiple inputs for custom Tensorflow model hosted by AWS Sagemaker
Does anyone know how to do same with Tensorflow 2?
As stated in title, what is the object lifecycle for the trained model instance in the webservice deployed in Azure Notebook. How long does the trained model persist in memory? Azure web app goes to sleep mode when there is no traffic hitting it. Does it mean that the trained model can become null?
An example of an azure notebook webservice is below
https://gallery.cortanaintelligence.com/Notebook/Deployment-of-AzureML-Web-Services-from-Python-Notebooks-4