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Performance issue regarding Sagemaker Estimator.deploy() #1006
Description
System Information
- Python Version: 3.6
- CPU or GPU: CPU
- Python SDK Version: Anaconda
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
predictor = estimator.deploy(
initial_instance_count=1,
instance_type='ml.m4.xlarge',
predictor_cls=DeepARPredictor)
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!
Activity
ChoiByungWook commentedon Aug 30, 2019
Hello @UniSabina,
Apologies on the slow experience.
I'll reach out to the team that handles the hosting platform and let them comment.
vedal commentedon Dec 16, 2019
I'm also interested in this
lexxx233 commentedon Sep 9, 2020
Any update on this?
rishabh1212 commentedon Nov 12, 2020
Hii @ChoiByungWook,
Any update on this?
nrapanos commentedon Jan 5, 2021
Is there any update on this? I'm experiencing a similar issue.
dsow86 commentedon Feb 23, 2021
Any update about this issue? I am still having the same problem, the model deployment take too long.
zlufy90 commentedon Apr 22, 2021
It is too slow for training and deploying model via SageMaker Studio. I just tested with Iris dataset.
Especially when debugging. It makes us give up easily.