mavrick102000
Shredder
- Messages
- 2,278
You'd have to configure the settings/tone you like in Amplitube and then capture it. Now it's an Amp Tone Model with the pedal baked in at those settings.
You have trim levels to set in Global Settings on the pedal. If you are there, while playing, it will tell you "Low" or "Ok" or "High". Good chance that's it.Hi folks! Has anyone experienced this? I am loading the same tone model ( Hi quality @amalgam Fender clean/edge one) into both the plugin and the hardware pedal, same settings,.. etc..
While the plugin sounds great, with the right amount of cleanish smooth crunchiness, the pedal on the other hand sounds thinner, much more clean with no crunch at all .. Am I missing something? Thanks so much in avance
Advanced training is about 14-15minutes on my machine. I've got a 3060ti GPU which gets hammered when doing the training, so I know it is defo using the CUDA cores.
Okay, so this is kind of mental. I recently upgraded from a 3060ti to a 4090. An extremely more powerful graphics card.
Now here's what I said before:
But I did a capture of my Dual Recto using the 4090, advanced training, amp only.... so the exact same type of session as before... and it took..... 27minutes!!!
That is really really disappointing. Something is wrong with ToneX and how it interfaces with the CUDA cores on the 4090. It's definitely using some of them, because the card is at 99% utilisation. But there is something wrong here.
For CUDA rendering reference, a scene like this:
That would take me over 12 hours to render on the 3060ti. The 4090 does it in about 2 hours and 25 minutes. So I have categorical proof that the 4090 should absolutely trounce and murder the 3060ti in its sleep. But with ToneX, it simply isn't giving the results you would expect.
ToneX has some issues. @IK Multimedia you may want to get your developers to look into this.
I'm thinking the underlying Tensorflow or Pytorch libraries that NAM and ToneX are using, has some fundamental bug in the way it allocates jobs to the CUDA cores. I'm not seeing a time boost with NAM either.F.w.i.w .... Karlis .... who does the Amalgamaudio Captures upgraded to [I'm pretty certain] a 4090 also, and his Advanced Captures still take 14 - 15 minutes .... weird ?? ....I remember him also writing he was surprised at the distinct lack of time-reduction.
Maybe there's an A.I processing point of no return with the IK process ? or maybe the software needs decent optimizations ?
Ben
Okay, so this is kind of mental. I recently upgraded from a 3060ti to a 4090. An extremely more powerful graphics card.
Now here's what I said before:
But I did a capture of my Dual Recto using the 4090, advanced training, amp only.... so the exact same type of session as before... and it took..... 27minutes!!!
That is really really disappointing. Something is wrong with ToneX and how it interfaces with the CUDA cores on the 4090. It's definitely using some of them, because the card is at 99% utilisation. But there is something wrong here.
For CUDA rendering reference, a scene like this:
That would take me over 12 hours to render on the 3060ti. The 4090 does it in about 2 hours and 25 minutes. So I have categorical proof that the 4090 should absolutely trounce and murder the 3060ti in its sleep. But with ToneX, it simply isn't giving the results you would expect.
ToneX has some issues. @IK Multimedia you may want to get your developers to look into this.
I’ve got a Dual Rectifier Rev G, any sounds in particular you’re missing?Interesting.
I’m still getting my feet under me with my handful of captures so far, but everything has used the advanced mode and takes a little over an hour per capture on my m1 Mac mini.
Just curious - which version recto are you working with? Direct or full rig captures? I’ve had lost puppy syndrome over my old rev G - would love to check out what you’ve got.
I posted up a couple Mark III direct captures recently but they aren’t dialed in for the brutal stuff yet - more middle of the road high gain.
Okay, so this is kind of mental. I recently upgraded from a 3060ti to a 4090. An extremely more powerful graphics card.
Now here's what I said before:
But I did a capture of my Dual Recto using the 4090, advanced training, amp only.... so the exact same type of session as before... and it took..... 27minutes!!!
That is really really disappointing. Something is wrong with ToneX and how it interfaces with the CUDA cores on the 4090. It's definitely using some of them, because the card is at 99% utilisation. But there is something wrong here.
For CUDA rendering reference, a scene like this:
That would take me over 12 hours to render on the 3060ti. The 4090 does it in about 2 hours and 25 minutes. So I have categorical proof that the 4090 should absolutely trounce and murder the 3060ti in its sleep. But with ToneX, it simply isn't giving the results you would expect.
ToneX has some issues. @IK Multimedia you may want to get your developers to look into this.
What do you mean?Whoa why do you know how to do that stuff?
What circumstances led you to learning animation (e.g. Blender)?What do you mean?
That's a bit of a can of worms. But suffice to say, for the last 3 years I've been heavily getting into 3D, Blender, and particularly building my own custom addons using Python and the Blender API.What circumstances led you to learning animation (e.g. Blender)?
I’ve got a Dual Rectifier Rev G, any sounds in particular you’re missing?
Okay, so this is kind of mental. I recently upgraded from a 3060ti to a 4090. An extremely more powerful graphics card.
Now here's what I said before:
But I did a capture of my Dual Recto using the 4090, advanced training, amp only.... so the exact same type of session as before... and it took..... 27minutes!!!
That is really really disappointing. Something is wrong with ToneX and how it interfaces with the CUDA cores on the 4090. It's definitely using some of them, because the card is at 99% utilisation. But there is something wrong here.
For CUDA rendering reference, a scene like this:
That would take me over 12 hours to render on the 3060ti. The 4090 does it in about 2 hours and 25 minutes. So I have categorical proof that the 4090 should absolutely trounce and murder the 3060ti in its sleep. But with ToneX, it simply isn't giving the results you would expect.
ToneX has some issues. @IK Multimedia you may want to get your developers to look into this.
Sorry, missed this the first time around. I've got a 2018 multi-watt Dual Recto. And honestly?? It is one of the best amps I've ever played. It is right up there with my Satch JVM and my Diezel VH4 as a proper "lifer" amp.Just curious - which version recto are you working with? Direct or full rig captures? I’ve had lost puppy syndrome over my old rev G - would love to check out what you’ve got.
Let me know your results. Always good to confirm with other people. Also try turning off hardware scheduling for your graphics, @Deadpan advised me to do that, and it did seem to speed up capturing with NAM. But I've not tried ToneX since. Even so... I only saved around 7-8minutes with NAM, which is decent... but given how powerful this card is, is still a bit disappointing overall.Dude that is disappointing af. I just literally pulled my new rig out of a box today, i9 13900KF, 64gigs 6000mhz ram and a RTX 4090 founders edition and was gonna get into ripping my amp for Nam and Tonex. What a bummer.
With Tonex relying on PyTorch, I expect that its developers would have to update it to support the latest version to get better gains on Nvidia 40 series GPUs. It seems PyTorch 2.0+ supports newer CUDA libraries that have improvements for the 40 series architecture.Let me know your results. Always good to confirm with other people. Also try turning off hardware scheduling for your graphics, @Deadpan advised me to do that, and it did seem to speed up capturing with NAM. But I've not tried ToneX since. Even so... I only saved around 7-8minutes with NAM, which is decent... but given how powerful this card is, is still a bit disappointing overall.
At least the benefit with NAM is, you can set it up to train a bunch of stuff in one run - so do all your capturing in one fell swoop, and then train everything overnight.
I suspect you are correct./With Tonex relying on PyTorch, I expect that its developers would have to update it to support the latest version to get better gains on Nvidia 40 series GPUs. It seems PyTorch 2.0+ supports newer CUDA libraries that have improvements for the 40 series architecture.
Might be a similar situation for NAM.