lukas

I don't think MobileNetV2 is designed to train on GPUs - according to this https://azure.microsoft.com/en-us/blog/gpus-vs-cpus-for-depl... MobileNetV2 gets bigger gains from GPUs vs several CPUs than ResNet. You could argue the batch size doesn't fully use the V100 but these comparisons are tricky and this looks like fairly normal training to me.

It's pretty surprising to me that an M1 performs anywhere near a V100 on model training and I guess the most striking thing is the energy efficiency of the M1.

Do you really train more than one model at the same time on a single GPU? In my experience that's pretty unusual.

I completely agree with your conclusion here.

For sure! I'm emailing you now.

I totally agree with this and I built wandb (wandb.com) to solve this problem. We try to do this in as lightweight a way as possible - for example we can do keras tracking with a single line (https://www.wandb.com/articles/visualize-keras-models-with-o...) and pytorch with just a couple lines (https://www.wandb.com/articles/monitor-your-pytorch-models-w...). Would love any feedback on it.

This stuff looks super cool. I’m a fan of pytorch and can’t help but add a shameless plug for the ML experiment management tools I built - https://www.wandb.com/blog/monitor-your-pytorch-models-with-...

That’s pretty awesome - I hadn’t seen it before. Thanks for the link!

Could you say a little more? I think I understand the "scaler" and it's how I learned the scales and how I practice, but I'm curious what the pentanizer is suggesting to do. Picking a root and an interval and finding it all over the fretboard?

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