mlcommons / mlcommons/inference
v4.0 Inference auditing procedure postmortem
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- Dominant language
- Python
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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
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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