mlcommons / mlcommons/inference

v4.0 Inference auditing procedure postmortem

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Description

Specifically for the nominated auditing selection procedure. To prevent misleading information during the nominated auditing selection procedure, it's essential to prioritize transparency and accuracy in the data provided. An issue arose where a significant performance difference of over 20% for L40S was mentioned without specifying the comparison context. This lack of specificity resulted in a mistake during the auditing process, where the comparison inadvertently paired top results with outlier low results. Consequently, this led to voters making decisions based on incorrect information.

To address this, we propose using a table format similar to the one below to provide relevant data along with the nomination:

        resnet    
        Server Offline  
ID Submitter System Availability Queries/s Samples/s Reason for nomination
4.0-0028 Dell Dell PowerEdge R760xa (4x L40S, TensorRT) available 179,615.00 175,746.00 Pre-GPU performance is XYZ faster than 4.0-0029
4.0-0029 Dell Dell PowerEdge R7615 (2x L40S, TensorRT) available 90,571.10 88,893.10  
           

This will help avoid mistakes, which can result in misleading voters. It will also contribute to making the correct auditing selection, ensuring that resources are allocated appropriately.

Thanks,
Frank

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Research direction

The issue describes a nominated auditing selection procedure and proposes presenting nomination data in a comparison table, but it identifies no repository file, test, or entry point. Start by locating where this procedure and nomination information are maintained; done means the comparison context is explicit and voters are not led to compare top results with outliers.

Written by the indexing model from the issue text.

Assessment

Domain
documentation, machine-learning
Issue type
Documentation
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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