tutku iç giyim Türkiye'nin önde gelen iç giyim markalarından birisi olmasının yanı sıra en çok satan markalardan birisidir. Ürünleri hem çok kalitelidir hem de pamuk kullanımı daha fazladır.
https://www.toptantutkuicgiyim.com
PyTorch 1.8, with AMD ROCm support
21–30 of 147 posts
Re: PyTorch 1.8, with AMD ROCm support
#22nbb sütyen hem kaliteli hem de uygun fiyatlı sütyenler üretmektedir. Sütyene ek olarak sütyen takımı ve jartiyer gibi ürünleri de mevcuttur. Özellikle Avrupa ve Orta Doğu'da çokça tercih edilmektedir.
https://nbbsutyen.com
Re: PyTorch 1.8, with AMD ROCm support
#23yeni inci sütyen kaliteyi ucuz olarak sizlere ulaştırmaktadır. Çok çeşitli sütyen varyantları mevcuttur. iç giyime damga vuran markalardan biridir ve genellikle Avrupa'da ismi sıklıkla duyulur.
https://yeniincisutyen.com
Re: PyTorch 1.8, with AMD ROCm support
#24Earlier quoted context omitted.
I am getting some conflicting messages about support. There is a small group of people working on ROCm for Julia (in AMDGPU.jl) and while that work is still alpha quality, they seem to expect even devices as old as RX 580 to work. Are all of these support issues something that is on the ROCm/AMD/driver side, or are they on the side of libraries like pytorch?
RX580 had a pro version which got ROCm support. It's the newer cards and older cards which aren't supported.
RX580 is out of support for ROCm nowadays, which only supports Vega and Instinct now.
https://github.com/RadeonOpenCompute/ROCm/issues/1353#issuec...
Re: PyTorch 1.8, with AMD ROCm support
#25Now that major frameworks finally started supporting ROCm, AMD has half-abandoned it (IIRC the last consumer cards supported were the Vega ones, cards from 2 generations ago). I hope this will change.
Oh oof. Thanks for saving me time not having to look up ROCm benchmarks. I find it really surprising they they don't wanna compete on performance/$ at all by not supporting consumer cards
I think this is more of an issue that they have Compute optimised and Graphics optimised cards and Vega is their last compute optimised card.
It would be very nice for them to refresh their compute cards as well.
Re: PyTorch 1.8, with AMD ROCm support
#26Now that major frameworks finally started supporting ROCm, AMD has half-abandoned it (IIRC the last consumer cards supported were the Vega ones, cards from 2 generations ago). I hope this will change.
I work for AMD, but this comment contains exclusively my personal opinions and information that is publicly available.
ROCm has not been abandoned. PyTorch is built on top of rocBLAS, rocFFT, and Tensile (among other libraries) which are all under active development. You can watch the commits roll in day-by-day on their public GitHub repositories.
I can't speak about hardware support beyond what's written in the docs, but there are more senior folks at AMD who do comment on future plans (like John Bridgman).
Re: PyTorch 1.8, with AMD ROCm support
#27Now that major frameworks finally started supporting ROCm, AMD has half-abandoned it (IIRC the last consumer cards supported were the Vega ones, cards from 2 generations ago). I hope this will change.
I work for AMD, but this comment contains exclusively my personal opinions and information that is publicly available. ROCm has not been abandoned. PyTorch is built on top of rocBLAS, rocFFT, and Tensile (among other libraries) which are all under active development. You can watch the commits roll in day-by-day on their public GitHub repositories. I can't speak about hardware support beyond what's written in the docs…
Can you comment perhaps on what you guys have compared to nVidia’s DGX? I’d rather buy a workhorse with open drivers.
Re: PyTorch 1.8, with AMD ROCm support
#28It also looks like they are in the progress of adding Apple metal support, possibly for the M1: Part of 1.8 is this issue: https://github.com/pytorch/pytorch/pull/47635
What's the point? If you have enough money to buy a brand new Apple M1 laptop, you can afford a training rig or cloud credits. Any modern discrete GPU will blow away any M1 laptop for training.
Is anyone training ML models on their ultra-thin laptop?
Re: PyTorch 1.8, with AMD ROCm support
#29Now that major frameworks finally started supporting ROCm, AMD has half-abandoned it (IIRC the last consumer cards supported were the Vega ones, cards from 2 generations ago). I hope this will change.
It’s worse....
It’s Linux only so no Mac, Windows or WSL.
No support what so ever for APUs which means if you have a laptop without a dedicated GPU you’re out of luck (tho discrete mobile GPUs aren’t officially supported either and often do not work).
They’ve not only haven’t been supporting any of their consumer based R“we promise it’s not GCN this time”DNA GPUs, but since December last year (2020) they’ve dropped support for all pre-Vega GCN cards which means that Polaris (400/500 series) which is not only the most prolific of AMD GPUs that have been released in the past 5 years or so but also the most affordable ones are no longer supported.
That’s on top of all the technical and architectural software issues that plague the framework.
Re: PyTorch 1.8, with AMD ROCm support
#30It also looks like they are in the progress of adding Apple metal support, possibly for the M1: Part of 1.8 is this issue: https://github.com/pytorch/pytorch/pull/47635
What's the point? If you have enough money to buy a brand new Apple M1 laptop, you can afford a training rig or cloud credits. Any modern discrete GPU will blow away any M1 laptop for training. Is anyone training ML models on their ultra-thin laptop?
I don’t understand this logic. If someone has $1000 for an entry level m1 machine then they also have enough money for a separate rig with a GPU that’s probably another $600-1000 for something decent? Cloud GPUs are also pretty expensive.
I don’t think anyone is seriously training their ML models on their ultra thin laptop but I think the ability to do so would make it easier for lots of people to get started with what they have. It might be that they don’t have lots of extra cash to throw around or it’s hard to justify on something they are just starting.
Cloud training is also just less convenient and an extra hassle compared to doing things locally, even if it’s something like google collab which is probably the best option right now for training on thin laptops