8 Times more CPU consumption on Aida-X Neural Models
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Here's a self-trained in AIDA_X_Model_Trainer.ipynb. Ugly as hell, but it loads and compiles.
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I can confirm the same findings anecdotally. I haven't stuck it up on a performance tester, but I have to raise my buffer size in a HISE implementation where I could run multiple models at 32 samples in AIDA-X.
Here's a bunch of models for you to test, there's a couple different sizes here.
keras-models.zip -
@Christoph-Hart NAM models, at least the standard ones, are just heavier than the average LSTM model used. A lot of the LSTM implementations have been optimised to run on a MOD Dward or Rpi. So I think 10% more CPU for NAM models sounds about right. Though there are actually smaller NAM models around that might compare more favorably. The "Lite", "Feather", and "Nano" ones. If you want any of those for testing let me know, I'll train some up for you.
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@ccbl Is it possible the overhead is from defining the model in the script instead of loading it from a file?
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@orange said in 8 Times more CPU consumption on Aida-X Neural Models:
I can load the Aida-X neural model in HISE
Can you pleas make a guide video or post how to use neural network or model in HISE ? i am very excited to learn the new technology but didn't get proper resources. I created a thread on this forum but no luck.
Bollywood Music Producer and Trance Producer.
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I've done it with tensorflow using google Colab. I can help you get started if you just need to get your foot in the door.
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@griffinboy said in 8 Times more CPU consumption on Aida-X Neural Models:
google Colab
Oh.. nice. I am at the door. Ting Tong.
Let me know what are the required steps. because I never tried google Colab and all that you have written. -
@DabDab I haven't trained any model yet, but loading models is done like this:
- Open the Neural Sine synth example in the Snippet Browser. Delete the synth.
- Then replace the sine example with one of the models above (the models are in json format, just copy/paste).
- Then open the neural node in FX as Scriptnode, select the model then you're good to go.
When you replace the neural network, you might need to restart HISE after saving, sometimes it doesn't update.
develop Branch / XCode 13.1
macOS Monterey / M1 Max -
Something else coming back to the performance difference. I vaguely remember saying that there were optimisations pre-compiled when it came to inferencing certain architecture sizes. Given that Aida for example has a pretty specific pipeline that people use on collab, maybe they optimised that specific number of Layers and Hidden Size?
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@ccbl yes but none of that should cause an 8x performance boost (more like 20% or so). I just need to profile it and find out where it's spending its time.
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@Christoph-Hart cool. Well like I said, if you want any kind of models for testing, let me know. I can do either NAM-wavenet or LSTM, of any size.
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@Christoph-Hart One thing, in the Neural Example in the docs it says this "It requires HISE to be built with the RTNeural framework to enable real time inferencing..."
I don't remember doing this explicitly when I built HISE, I just built it the standard way and it all works. I'm assuming this is just no longer a requirement? Otherwise could it explain the performance penalty?
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@orange said in 8 Times more CPU consumption on Aida-X Neural Models:
@DabDab I haven't trained any model yet, but loading models is done like this:
- Open the Neural Sine synth example in the Snippet Browser. Delete the synth.
- Then replace the sine example with one of the models above (the models are in json format, just copy/paste).
- Then open the neural node in FX as Scriptnode, select the model then you're good to go.
When you replace the neural network, you might need to restart HISE after saving, sometimes it doesn't update.
Wow... i will give it a try. Later I will need @griffinboy help.
