Comments (15)
Hi @NTuan-Nguyen, can you please help Sara to fix this issue ? Thanks! Dinesh
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Hello Sara,
I think this issue might sometime occur on a system with PyTorch and Triton backend on multiple GPU systems. I was able to replicate the issue on Colab using the default Colab PyTorch 2.3 with cuda 12. A workaround for this issue is to revert back to an earlier PyTorch build using cuda 11.8. This can be done by adding the following code to the notebook during installation step:
!pip install torch==2.0.1+cu118 torchvision torchaudio torchinfo --extra-index-url https://download.pytorch.org/whl/cu118
I have tested this on Colab environment, but please let me know if the issue persist or if you're unable to apply the fix in your workspace.
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Thanks, this worked in resolving the issue with the Triton library.
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The YAML file has a section for MSP processing criteria used to filter out MSP blocks that fall outside the model training space. If an MSP block does not meet these criteria, it will not be streamlined in the prediction step. You can find a log file in the output folder, which records any issues with MSP block processing. An example of an MSP block for Aspirin is provided on the main GitHub page. The necessary row entries for an MSP block are Name, PrecursorMZ, and Num Peaks.
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Names do not have to be unique values, but Name row entries must be there. You should standardize your msp blocks before feeding them into MINT.
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WARNING!!! Removed MSP block ID 1
related to A_M8_negPFP_03
! Names don't need to be unique, but in this case the MSP block ID 1 should be specifically investigated.
The m/z thresholds refer to the mass values, not their intensities. Could you please also share your YAML file?
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Your precursor mass is out of the mass range specified in the YAML file.
Minimum m/z: 100
Maximum m/z: 900
There is a 10% tolerance in number of peaks for fragmentation mass to be outside of the training space, but the precursor mass must be within this range.
Additionally, keep in mind:
Minimum number of peaks: 5
counted after noise removal threshold: 0.01
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By this are you referring to the fact that the minimum and maximum m/z should be investigated over the mass of all samples (train/validation and test) then the yaml file can include those numbers? Could this be why the algorithm bypasses some samples? I will also look into the names for a more accurate representation.
…
On Jul 11, 2024, at 4:38 PM, Sadjad Fakouri Baygi @.***> wrote: WARNING!!! Removed MSP block ID 1 related to A_M8_negPFP_03! Names don't need to be unique, but in this case the MSP block ID 1 should be specifically investigated. The m/z thresholds refer to the mass values, not their intensities. Could you please also share your YAML file? — Reply to this email directly, view it on GitHub <#1 (comment)>, or unsubscribe https://github.com/notifications/unsubscribe-auth/AKSAYJHQJCHGS2YYPNIE2STZL3UMFAVCNFSM6AAAAABJ2M2FQCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEMRTHA4TSOBTHE. You are receiving this because you authored the thread.
Yes, each m/z value is represented by a specific embedded token. If that token is not in the training space, the model cannot represent your chemical space.
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you're welcome!
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Related Issues (2)
- Error training on GPU HOT 2
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