Replies: 13 comments
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Hello @UniSabina, Apologies on the slow experience. I'll reach out to the team that handles the hosting platform and let them comment. |
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I'm also interested in this |
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Any update on this? |
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Hii @ChoiByungWook, |
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Is there any update on this? I'm experiencing a similar issue. |
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Any update about this issue? I am still having the same problem, the model deployment take too long. |
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It is too slow for training and deploying model via SageMaker Studio. I just tested with Iris dataset. |
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really slow |
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Still no update? I can build and push a custom PyTorch training image, and then train a model in a shorter time than deploying. |
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Takes over 90 minutes for me. |
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How much should I wait before killing the process?! |
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so slow still |
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terribly slow... tring to deploy an ml.m4.xlarge, since the max health_check_timeout is topped at 3600 and my deployment currently takes over an hour, I am not able to deploy llama3 70B. What to do? |
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System Information
Describe the problem
I'm quite new to the SageMaker algorithms and estimators so please bear with me.
I'm running a script very similar to this example script for DeepAR
https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/deepar_electricity/DeepAR-Electricity.ipynb
And want to start more than hundred of such training + prediction jobs.
The cell
takes up 70% (~8.5min) of the time of the overall training and predicting job (~12min). Is there a possibility to reduce that time? What is the reason for this deploy job taking so long?
Thanks!
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