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Hi,
I have been working with ML Studio (classic) and facing a problem with "Execute Python" scripts. I have noticed that it takes time to perform some internal tasks after which it starts executing the actual Python code in ML Studio. This delay has caused an increased time of 40-60 seconds per module which is aggregating and causing a delay of 400-500 seconds per execution when consumed through Batch Execution System or on running the experiments manually. (I've multiple Modules of "Execute Python" scripts)
Can you please help understand the reason behind this or any optimization that can be done?
Regards,
Anant
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Hi, thanks for reaching out. The reason may be capacity constraints due to growing demand from customers (as a result of the pandemic). A demand grows, we are prioritizing first responders, health and emergency management services, critical government infrastructure organizational use, and ensuring remote workers stay up and running with the core functionality of Teams. Note that we will also consider adjusting free offers, as necessary, to ensure support of existing customers. If you fall into any of the categories above, let us know so we can work towards a resolution. Furthermore, I encourage you to explore Azure ML Designer which provides similar drag and drop ML modules plus scalability and wide range of customizable compute targets.
Hi, I've been facing this issue even prior to the pandemic and it seems ML Studio (classic) creates a Python environment for each "Execute Python" module (as per logs) which actually is causing additional time. Please let me know if this is correct and Is there any way to optimize this time?
I completely understand, sorry for the inconvenience, however, this is a known limitation as optimizing performance in Azure ML Studio (Classic) isn't supported. We recommend that customers use Designer for advanced capabilities and active updates/improvements for the service. Thanks.