System Information (please complete the following information):
- OS & Version: Windows Server 2019
- ML.NET Version: ML.NET 1.6.
- .NET Version: .NET 7.0
Describe the bug
I run prediction in Parallel.ForEach loop on 16 core machine. The CPU consumption usually stabilizes near 10-12% even is max parallelism would be 16.
I would like to understand what is might be the bottleneck. Are there some parts of Microsoft.ML that may be limiting, such as locks(?) or parallel data structures that do it internally?
Memory hard Faults usually <10, max < 100. I am caching models and dataset, about 90% RAM used. Disk 0-20% Highest active time.
System Information (please complete the following information):
Describe the bug
I run prediction in Parallel.ForEach loop on 16 core machine. The CPU consumption usually stabilizes near 10-12% even is max parallelism would be 16.
I would like to understand what is might be the bottleneck. Are there some parts of Microsoft.ML that may be limiting, such as locks(?) or parallel data structures that do it internally?
Memory hard Faults usually <10, max < 100. I am caching models and dataset, about 90% RAM used. Disk 0-20% Highest active time.