Comments (9)
Please ask on NVIDIA support page.
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I wonder how to measure and compare power consumption in your paper.
For public confidence, your paper is needed to reveal how to measure and compare energy fairly.
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What do you mean by public confidence?
NVIDIA TX2 is manufactured by NVIDIA and "not me". So, you need to ask them how to measure these metrics. They can provide you much better support than me.
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@sacmehta Hi,
Did NVIDIA directly measure the results in your paper?
I just want to know how "you" measure power consumption.
For examples, In SqueezeDet paper, the author said that they measure power consumption by nvidia-smi with 100ms interval.
I just want to know your measuring method in order to reproduce your results on my TX2 board.
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You can measure power on desktop GPUs using nvidia-smi and we also used the same on Desktop GPu. However, nvidia-smi doesn’t work on TX2. Please post on nvidia forums for more help.
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If you have a laptop with mobile GPUs, you can use powertop. Note that this work only on battery mode.
https://wiki.archlinux.org/index.php/powertop
You need to do calibration etc. see above page for more help.
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@sacmehta many thanks !!
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You need to very much familiar with Tx2 architecture, including register specification. Look for such details on NVIDIA’s page. It is not pretty straightforward for measuring power on TX2. If you think you are comfortable with computer architecture, please follow below thread to measure power on TX2:
Sorry, but I won’t be able to provide more help. Good luck
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Also, while measuring power, make sure you restart your machine after every run and let it stay in idle position for about 5 minutes . This time window allows OS to finish startup related activities. To get a good estimate of power, Don’t launch any other process such as web browser while measuring power. Also, for each datapoint, repeat your experiments atleast 5 times to ensure that there is no variation in results.
For battery driven devices such as laptop and TX2, make sure that battery is fully charged (atleast 95%) before every run.
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Related Issues (20)
- About cyclic learning rate HOT 1
- Downsampling HOT 1
- Training issues (from pretrained weights) HOT 1
- a strange question
- a strange question
- A strange problem encountered during training
- A strange problem encountered during training HOT 15
- Turning ESPNet into c++ cudastream problem HOT 2
- train.txt create HOT 2
- compared with ShuffleNet V2 HOT 3
- train/eval for another dataset HOT 1
- Question about predict on espnetv2 HOT 3
- Espnet .pth with another scale factor HOT 1
- You have following values as class labels: [ 0 1 2 4 5 6 7 8 10 11 13 255] Some problem with labels. Please check image file: ./city/gtFine/train/cologne/cologne_000000_000019_gtFine_labelTrainIds.png Exiting!! HOT 2
- Network parameters of segmentation model in the paper HOT 5
- Have you used ImageNet pretrained weight to train epsnetv2 for segmentation? HOT 1
- Can anyone upload a pretrained model? HOT 1
- The tool to calculate FLOPs
- Can this model be used for regression tasks?
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