Hi! Thanks for the feedback.

Actually, Progress Table is not tied to Keras or any other Deep Learning framework. You can use Progress Table to track any long-running process that produces data. The source code is not neural network specific :)

To help you start out, I've created a markdown file with PyTorch integration example. Check this out: integrations.md. Let me know if it's clear!

Looks very nice, kudos! Can we expect a similar tool for Pytorch?

Oh nice set up. I have thermaltake 850w 80+ gold. Judging by your answer i think my desktop will be okay.

Looks like, GTX980 4GB = 165 W & RTX2080 6GB = 160 W, which would be 325 W. I haven't used Intel K CPUs, so I am not that familiar with power usage of that. But I think 850 W would be more than enough, if it is a proper 850 W PSU, even considering power usage by other components like motherboard, RAM, SSD etc.

You can use this to calculate power requirement https://outervision.com/power-supply-calculator

My power usage is AMD Ryzen 5600x (75 W) + RTX3060 (170W) + RTX3090 (350W) = 595 W at max, I think with other components total was 750 W ( System power budget : https://outervision.com/b/8XoZwf ).

I have a 850 W, Tier A - Deepcool PQ850M which is a Seasonic based 80+ Gold. I have power stress tested with OCCT and it was fine.

It can also be pixel unshuffle.Here you can find pytorch reference: https://pytorch.org/docs/stable/generated/torch.nn.PixelUnshuffle.html

This is spaceToDepth effectively? https://www.researchgate.net/figure/Intuitive-examples-of-space-to-depth-During-the-space-to-depth-method-top-we-aim-to_fig3_347156516

To accomplish the specific op here, spaceToDepth with block_size=2.

https://discuss.pytorch.org/t/is-there-any-layer-like-tensorflows-space-to-depth-function/3487/12

Looks great, defo like to have a go with this.Perhaps epoch n/total epochs too though?

Use convnet to transform to the right shape. May need to use dilution.

Use convnet to transform to the right shape

Nice use of violet in this spaces mate

Wow, that's a nice extension/replacement. Very cool work!

Sometimes that beautiful one-liner just isn't worth it compared to something like

torch.cat((
    img[:N, :N],
    img[N:, :N],
    img[:N, N:],
    img[N:, N:],
    ), dim=-1)

Skimage view_as_windows would do this.

I don't have the time to figure it out, but I'm pretty sure you can do it through some combination of permutations and reshapes. Play around with an NxN numpy array (e.g. np.arange(8**2).reshape(8,8)) and perform various transposes and reshapes and see what comes out. You might have to add and remove an axis at some point too.

Depending on my set up, do you think 850 watt PSU will be good enough ? My CPU is i7 6700k.

How would a 2x A5000 system differ from a single A6000 in actual use? Are your cards treated as a single card by the software?

Here is a thread where we talked about this with GeForce cards. It's not treated as a single GPU and apparently you still need to parallelize. At least that's what I was told in that thread.

I will have to check that out, thank you!

This is only the code license for the open source portion, but the SDK license of the general, proprietary software that TensorRT is, is also something you have to agree on: https://docs.nvidia.com/deeplearning/tensorrt/sla/index.html

In there, ownership is phrased in such an ambiguous way the legal team of a company would probably never greenlight using it.

Can I ask you what you mean by "implicitly prevents"?
https://github.com/NVIDIA/TensorRT/blob/main/LICENSE seems to permit commercial use, do you refer to trademarks?

>Now if I connect my 2060 along with the gtx 980, and connect my display to the 980 , will pytorch be use the whole vram of 2060 ?

Yes, I have a similar setup, RTX3090 - No display (full VRAM for training), RTX3060 - 2 Monitors

When I play games, I connect 1 monitor to RTX3090 and play on that, other monitor on RTX3060

Cool might try that with my 2080s then. Thnx.

Works fine with my A5000s

Is that confirmed to work? I’ve read mixed on same GPU connected by NVLINK.

Unless he gets nvlink