how to choose an instance type for a sagemaker testing/inference?

Yes, you can use spot instances. I recommend it, and always run training on spot instances. If you are using the Python SDK, add the following parameters to your Estimator:

       use_spot_instances=True,
       max_run={maximum runtime here},
       max_wait={maximum wait time},
       checkpoint_s3_uri={URI of your bucket and folder },

See the documentation for more details here: https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html

As far as instance types are concerned, the individual algorithms contain some initial recommendations for instances types: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

For example, see the EC2 Instance Recommendation for the Image Classification Algorithm: https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html

There was a presentation at re:Invent 2020 - How to choose the right instance type for ML inference: https://www.youtube.com/watch?v=0DSgXTN7ehg

Hope this helps

And for the selection of instance type for inference, you might want to look at Amazon SageMaker Inference Recommender:

https://docs.aws.amazon.com/sagemaker/latest/dg/inference-recommender.html

Hi I understood was spot instances are for model training and not model inference. I hav echeckpoints available for a model which I want to run in sagemaer using spot instances. It is possible. Can you guide?