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I think a better comparison would have been some of the smaller discrete GPU wielding laptop. You can get some of these for similar prices to the M1 macs. Also
by jefft255 6y ago
I think a better comparison would have been some of the smaller discrete GPU wielding laptop. You can get some of these for similar prices to the M1 macs.
Also, for me, the answer is no: they're not any good. I use PyTorch. I use custom CUDA code. The reality is brutal but simple: if it doesn't run CUDA, no serious ML research will use it for training anything else than toy models.
- hu3 6y agoIs there a roadmap for CUDA support? (downvoted for asking a honest question. Ahhh the things you see on Apple related posts... Emotion driven bunch)
- coayer 6y agoCUDA is proprietary and exclusive to Nvidia, so probably not! (Apple and Nvidia haven’t been cooperative for some time)
- whywhywhywhy 6y agoIt's even more absurd because Apple discontinued OpenCL and replaced it with their own proprietary solution too.
- jefft255 6y agoOfficially I don't see that coming, I think it's iffy in terms of patents etc...
- cnasc 6y agoCUDA is Nvidia exclusive, no? I don’t think support for third party graphics hardware will be forthcoming
- russli1993 6y agocuda is designed by nvidia, to lock software and applications to use Nvidia's hardware. Cuda is the core of Nvidia's business model. Cuda is the reason why Nvidia has the guts to charge $5000 for a data center or Quadro card when that card uses nearly the same silicon as the consumer versions and customers will still have to buy their cards. Its like Apple sell you the iPhone, but also the iOS APIs and application model so you can run iOS apps. Once you run iOS apps, you are in Apple's ecosystem, both the ISVs and the user are hard to leave. Its like saying when will Apple officially make iOS APIs and libraries run on Android phones. Both will never happen.
- sorenjan 6y agoA better question would be when will serious ML researchers stop using proprietary frameworks? The open source projects should work towards using something more open than Cuda, probably Vulkan. It's a real shame that Nvidia has a whole industry in their grip, and nobody seems to mind.
- russli1993 6y agocuda is basically equivalent to the combination of the instruction set and the C/C++ language, API and standard library, and all bundled together. In the CPU's case, C/C++, API and standard library are designed to be hardware platform agnostic, instruction sets is separate and open, and x86 have two players. Cuda became this way because it is designed by the hardware company and its business need. We really should be thankful that C/C++, open compilers, API and library exists in the CPU world back then. And hardware vendors like Intel published raw instruction sets and allow any software to target the instruction sets. I can't imagine in a world where people uses the programming language and libraries designed by a hardware CPU company and all that software only runs on hardware from such company.
- sorenjan 6y agoSome things were better back in the day. E-mail could not be invented today, it would be a mix of incompatible mail system like Facebook mail, Google mail, Apple mail, Slack mail, some distributed open source mail nobody used, etc.
- meepmorp 6y agoGo read the SMTP and related RFCs and see if you still think that email back in the day wasn’t already a mix of incompatible systems. The parts about address formats alone was quite an eye opener for me.
- brian_cunnie 6y agoYes, I remember IBM had its own email system back in the early 80's when I worked there, AT&T had its own in the later 80's. Even Prodigy/Compuserve/AOL had proprietary email systems (IIRC) with internet email bolted on. And getting email to the internet at large was no mean feat: I remember having to do a slew of UUCP addressing to get an email from AT&T to the internet (something along the lines of "astevens@redhill3!ihnp4@mit.edu). It was the wild west.
- eyelidlessness 6y agoHey don't jump to conclusions, downvotes all around here! (I upvoted your perfectly reasonable and honest question)
- deleted 6y ago[deleted]
- make3 6y agoAside from the fact that CUDA is proprietary & very obviously made to lock people into using NVidia products, the thing is also that it's not just about CUDA. Indeed, deep learning primitives use CUDNN which have professionally written code hand tuned by PhDs in internal only assembly, getting performance not available to the public even if you did end up getting a CUDA compiler somehow.
- teruakohatu 6y ago> I think a better comparison would have been some of the smaller discrete GPU wielding laptop. I agree, but from what I have seen they also perform poorly against collab. A 1660 Ti 4gb RAM laptop gpu, runs about as fast as Collab CPU does. [1] I have not been able to find how the RTX 2080 Super (Laptop) and upcoming RTX 3080 (Laptop) compares to Collab. The 3080 has over 3x as many cores as the 1660 Ti. [1] https://towardsdatascience.com/google-colab-how-does-it-compare-to-a-gpu-enabled-laptop-851c1e0a2ca9\ https://towardsdatascience.com/google-colab-how-does-it-comp...
- joshocar 6y agoAre the Quadro cards any good for it?
- teruakohatu 6y agoThey are, other than being priced more expensively compared with consumer cards. When doing things like RNN's, RAM becomes an issue regardless of how powerful the card is in other ways.
- apohn 6y ago>A 1660 Ti 4gb RAM laptop gpu, runs about as fast as Collab CPU does According to the chart on that page you linked to the Lenovo Legion (with the 1660ti) is about 30% faster than the Colab GPU. The Lenovo 480s (CPU only, no GPU) is about the same speed as the Colab CPU. So if you can train your model on the on 4GB of VRAM, a laptop with a discrete GPU (if you exclude the really basic business laptop GPUs) may be useful.
- winter_blue 6y agoIn this particular use case, I would heartily recommend the Zephyrus G14. It can come with a RTX 2060 (Max Q / low voltage) which supports CUDA; as well as Ryzen 9 4900HS, which was the fastest mobile CPU until the 5000 series mobile Ryzen came out. The 2060 + 4900HS has gone on sale at Best Buy for $1199 multiple times late last year (just check slickdeals). I think it’s absolute steal of a machine for that price. Not to mention that it’s beautifully built, is lightweight, has an amazing matte color accurate screen, etc. There’s also a fan sub for this particular laptop with over 13k subscribers (as of today) on Reddit: https://www.reddit.com/r/ZephyrusG14/ https://www.reddit.com/r/ZephyrusG14/
- sgillen 6y agoNo doubt it's a good laptop. However I would recommend anyone looking at laptops with machine learning in mind seriously consider a cheaper / less powerful laptop, and to do their training remotely on either on a workstation or in someones cloud.
- deleted 6y ago[deleted]
- adgjlsfhk1 6y agoOr just build a cheap desktop with a gpu. rtx 2060 + nvme SSD + any cpu is pretty good for most ML, and will only cost $800 or so.
- rileymat2 6y agoThat is easy to say, but good luck finding a RTX 2060 at a reasonable price. I have been trying for a month and a half, and almost all graphics cards are not available.
- mehrdada 6y agoIf you are in the US, you can easily buy a prebuilt machine that comes with RTX2060 for $1100-1200. Check Dell/Lenovo or Costco. Buying the card independently is pretty much impossible/uneconomical. If you don't want the rest of it, simply list the remains of the system with as a cheap PC with integrated GPU.
- visarga 6y ago> no serious ML research will use it for training anything else than toy models They will use it to edit code, deploy and remotely run jobs. Serious deep learning on laptops is a non-starter, on any laptop, on account of heat dissipation.
- ChuckNorris89 6y agoDepends what do you mean by 'serious'? Training Tesla's FSD neural net on a laptop? A student training some models for courses or self study?
- WanderPanda 6y agoExactly! I would guess that maybe 80% of monthly active deep learning users would benefit from a small accelerator like that. In contrast to that, probably less than 1% of the deep learning flops will benefit
- 908B64B197 6y ago> A student training some models for courses or self study? Serious schools have computing clusters. ML researchers might be interested to know which undergrad is actually training models complex enough to benefit from better hardware.
- EveYoung 6y agoWith WFH nowadays, I wouldn't even want a GPU workstation running at home considering the noise and power consumption. And with the high costs of a capable DS team, runing these workloads in the cloud doesn't make much of a difference either.
- 3np 6y agoSounds like you haven't seen the cloud costs for processing units handling these workloads, then... They're severely overpriced compared to normal CPU loads.
- chungus_khan 6y ago
- virgilp 6y agoThey even buried it in the conclusion, after a few thousand words & charts designed to give the opposite impression: > these still aren’t machines made for deep learning. Don’t get me wrong, you can use the MBP for any basic deep learning tasks, but there are better machines in the same price range if you’ll do deep learning daily.
- amitport 6y ago"buried it in the conclusion" The article has 4 short sentences in the 'conclusion' section which can be found, as expected, at the end of the article. it really isn't "buried"
- virgilp 6y agoBut it is. A regular conclusion summarizes the article, doesn't contradict it.
- slowhand09 6y agoThat's not buried. This is buried. “But the plans were on display…” “On display? I eventually had to go down to the cellar to find them.” “That’s the display department.” “With a flashlight.” “Ah, well, the lights had probably gone.” “So had the stairs.” “But look, you found the notice, didn’t you?” “Yes,” said Arthur, “yes I did. It was on display in the bottom of a locked filing cabinet stuck in a disused lavatory with a sign on the door saying ‘Beware of the Leopard.”
- p1esk 6y agoCorrection: if it doesn’t run Pytorch. No one really runs custom cuda code anymore. If you do, you’re an outlier.
- fxtentacle 6y agoI work in computer vision and pretty much all recent SOTA models had custom CUDA code. So I'd argue the opposite that in my field, lack of custom CUDA kernels will effectively exclude you from research.
- p1esk 6y agoCan you give an example?
- fxtentacle 6y agohttps://github.com/princeton-vl/RAFT#optional-efficent-implementation https://github.com/princeton-vl/RAFT#optional-efficent-imple... It's "optional" in the sense that things still calculate correctly on CPU without it, but at a 1000x performance penalty. Or you could skip it if you had 64GB of GPU RAM, which you cannot buy (yet). So if you actually want to work with this on GPUs that are commercially available, you need it.
- p1esk 6y agoOk, so in this example the cuda code is only used when you're short on GPU memory. This means if Apple makes a chip with enough memory cuda compatibility won't be necessary, right? Are there any examples where custom cuda code implements some op that can't be written in Pytorch/TF/Jax/etc? That would have provided a better support to your claim that M1 needs to be able to run cuda.
- jefft255 6y agoPointNet++ https://github.com/charlesq34/pointnet2 https://github.com/charlesq34/pointnet2
- pjmlp 6y agoI am CUDA fan, but it all depends how much adoption SYSCL manages to actually achieve.
- sillysaurusx 6y agoIf you say so. I haven't used cuda in over a year, other than deploying to production. TPUs are probably the future (or something like a TPU, whenever Microsoft launches theirs).