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

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looking at few examples, for training in sagemaker . are there some guidelines based on the model size, data to be trained , what type of instance cpu/gpu to use? also, can one use spot instances ( may be with multiple gpu cores)?

2 Answers
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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

answered 6 months ago
1

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

answered 5 months ago

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