-
Notifications
You must be signed in to change notification settings - Fork 173
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Adding support for CUDA_VISIBLE_DEVICES #567
Comments
Hello @lipengfeizju! Thanks for using In the meantime, in EmissionsTracker(
...
gpu_ids ="0,3,4"
) Is this what you need? |
Thanks! That's exactly what I need. |
Sorry to reopen the issue again, is it possible to measure the power of several specific CPU cores? (Maybe just like we do for the GPU ids) |
Maybe we could initialize For CPU node could you open another issue and provide the codecarbon debug logs ? Because it's not possible yet but maybe we could imagine a way to do it. |
Thanks! |
Description
In our university's cluster, our goal is to measure the energy consumption of a deep learning model. The server uses SLURM system and we only get 1 A100 ( out of 8 GPUs). The GPU power measurement from codecarbon is about all 8 GPUs, instead of the GPU we have been allocated.
Techinically speaking, I guess in line 184 of
codecarbon/core/gpu.py
, it queries all GPUs from nvml instead of focusing on the GPU we are actually using. To get a more accurate measurement, it would be better to only look up the power consumption related toCUDA_VISIBLE_DEVICES
.Similar discussion about this topic can also be found here in pynvml.
So is it possible to add a new feature to support measurements focusing on
CUDA_VISIBLE_DEVICES
? I think this is important for deep learning applications, since the other non-visiable devices are usually unrelated to the power consumption of the DL applications.Thank you again for providing the code base for carbon measurement.
The text was updated successfully, but these errors were encountered: