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I am not quite sure if that works. Hi there, thanks for the study! Do you have any benchmark recommendation to test all those facts? Sorry I no longer have the code I think. I think I never uploaded it to github.
I used a Linux tool that downclocks the core clock rate and benchmarks performance in taht way. What about a laptop that is equipped with rtx super max p? Would it be sufficient for deep learning? If that is okay for you it might be a good option. Any thoughts on this? Unfortunately, this feature will be too slow to train neural networks. There are some similar techniques that you could use memory swapping but these need to be programmed or tuned for any neural network separately and do not work automatically.
So with such techniques, an 8 GB GPU memory will be enough even for the largest neural networks, but unified memory does not allow for this right now. Hello Tim, hope you are doing well. Hi Tim, Thank you for detailed description on all the essentials in setting up Deep Learning machine.
I am currently building my DL machine and would request your suggestion if the config i am building will work out well or not. The CPU is a bit overkill if you want to just do deep learning. If you want to also do other things with the computer it looks pretty good.
This would be a well-balanced build for Kaggle competitions for example. Thanks for your article. It helped me to select parts for my rig. I would appreciate some feedback on my selection. I think they should perform equally well on the motherboard. I am not sure why it would be otherwise.
Thanks for your post. Do you know if that is true? Thank you. If you start upgrading the PSU though it might be worth thinking if it is worth it or to build a new desktop entirely.
Both options can make sense depending on budget and other constraints. One or possibly both of them died, I cannot really troubleshoot without replacing them. It seems much more economical to move to AMD, even if it would complicate processor water cooling etc. What fo you think?
I am thinking about Intel Core iF 4 cores, 3. This does not come with an integrated graphic I will add the GPU. If yes, which CPU and Motherboard would be a good fit for my budget? You can also roll with a AMD CPU which are now pretty cost-efficient and powerful but it would only make a small difference.
Maybe you can also give me some guidance on the choice in GPU. I work in a hospital and want to start with deep learning projects on high resolution image data-sets from MRI and CT. I would would definitely go with the RTX Titan! The memory will be a life-safer if you work with medical images! I was wondering what the lower limit on RAM speeds is? I am looking at repurposing old server hardware and have 64gb of mhz DDR3 memory and was wondering if this would be a bottleneck?
Also I have committed to offsetting my carbon footprint, and wanted to thank you for encouraging others to do the same!! It can always be a bit tricky to re-purpose old hardware but if the computer boots with the RAM stick then it should not be the biggest bottleneck. The problem that the ram is only 6GB but I cannot afford anything more. It will be difficult but you can look up techniques to conserve memory. You will probably also need to accept running smaller datasets and models.
If I am to start out building supervised and unsupervised models, then do I really need a graphics heavy computer? Yes, usually even if you just want to get started a GPU is required. A CPU can be quite slow even for small problems.
Hi Tim! How significant would this bottleneck be in your opinion, and does it warrant an upgrade to a modern CPU? I believe PCIe 2. I would give it a try and upgrade your computer if it does not work out. Theoretically it should be fine. Thanks for your advice! I ended up getting a and it worked pretty decently. A little faster than colab.
To me this is a good signal that an upgrade is neccesary, but do you think its a significant bottleneck? The GPU utilization is also not the true utilization; it just means that all cores on the GPU are used but not by how much.
Then you can compare the performance with an underclocked CPU. If it is much lower, then the CPU is a bottleneck. If the performance is similar, the CPU is not a bottleneck. I have doubts in order to choose a CPU. Which CPU do you recommend? Ryzen 7 x? As far as I know, I9 and I7 only has 16 pcie lanes and that could be a problem.
I mostly do deep learnign stuff but I also want to use my pc to some kaggle competitions mostly tree-based models that runs on cpu in sklearn. MKL library issues are only for things like solvers, Fourier transform, eigendecomposition. I am not sure if that is really that common for Kaggle competitions and you would only be hit by a small penalty.
I think Ryzen processors are fine even in your case. Also, is that good enough for a deep learning student? I was wondering if you had any opinion on cube computer cases. Do you have any knowledge or opinions? Are 2x GPU for machine learning worth it? More GPUs are always better :. If you plan to go for 4 GPUs in the future, it makes sense to get the Threadripper and the right motherboard right away. Hey Tim, Please tell me what do you think of this pc: iK processor 3. BTW loved your article!
I think that is appropriate. I also like Ryzen CPUs if you want to save a bit of money. A lot thanks for the wonderful article and all the replies to our queries. Or maybe the 10th gen improvement will not make much difference for DL….
The CPU does not matter that much for deep learning. If you have some workloads which require a better CPU factorization, sklearn models, some big data stuff then it might well worth it to wait. However, if you just want to get started and do deep learning it might be better to just go ahead now — you will lose almost no deep learning performance if you use a k CPU.
I think it looks good. I guess you want to store the OS on the smaller one and have the rest for data? The better thing is to use a small partition for the OS and then use a virtual RAID 0 to create a single high-speed device — this can make a huge difference if you work with very large datasets! Otherwise, quite some few spinning disks, but if you need the space, then you need the space. Do you have a link? Is there any reason to not go with a single 1 TB drive for OS and datasets vs a drive for OS and another drive for datasets?
I think startup stuff and consulting is fair with 2 GPUs. Not sure about Windows though. All this results in a somewhat high-end motherboard. There a euros gap in Spain between the 6 cores Ryzen 5 and the 8 cores Ryzen 7 X. Should I go for a Ryzen 7 instead? It is totally fine if you spend more on a motherboard than a CPU. However, the Ryzen 7 can make sense if you are working with datasets that involve loading and preprocessing lots of data computer vision, for example ImageNet.
Note that at home you would have to pay for electricity yourself. No thoughts? What setup would you recommend for GPT-2 pre-trained language model latest release 1. I am intending to train this AI for my researches, but i am very unaware about the hardware needed. I have read that numerous users have issues with still powerfull setup.
Any idea? A minimum would be 4x RTX Ti. You might use very small batch sizes though which is computationally inefficient, thus I would not recommend RTX Tis. I would recommend instead 4x Titan RTX which should have enough memory so you can run GPT-2 and other transformers with a large enough batch size. You opened my eyes on an important point regarding the transformers. I am now studying the matter of batch sizes. Better to start first a collab notebook and run tests before investing big money….
Tim, your hardware guide was really useful in identifying a deep learning machine for me about 9 months ago. At that time the RTXs had started appearing in gaming machines. Based on your info about the great value of the RTXs and FP16 capability I saw that a gaming machine was a realistic cost-effective choice for a small deep learning machine 1 gpu. As a Linux newbie one gotcha I found out was using a Windows file system results in a performance bottleneck in Linux.
It has been working great for learning deep learning with pytorch and Kaggle competitions. I have found this local setup to be faster than Google Colab, Kaggle kernels, and Azure notebooks and long runs are more reliable.
The colorful case lights are an added bonus! Thanks for your feedback! I think cases like this are pretty common. Some setups will fall a bit short here and there but with a bit adjustments you can quickly get a great system that fulfills most of your needs. Thanks for the excellent material. Some of the new GAN training work really requires 8xTi. Have you given any thought to a 8xGPU machine that can live comfortably in a home environment?
Any thoughts appreciated. I do not think you will find a 8 GPU machine which you can comfortable house in a home. Or do batch aggregation to simulate 8 GPU training. Batch aggregation will just double the training time for you, which should be alright and is doable in a home environment. Hello, I am going to do a UG project which going to do deep learning of Medical imaging.
And I am finding a perfect laptop can let me do the research. Do you have laptop recommendation? I would not recommend laptops for medical imaging deep learning projects. You can buy a desktop and a small laptop with which you login to the desktop when you are on the go.
This would be the best solution. I am going to use it for training CNNs Kaggle, not large projects. Is this build sufficient for my purposes? Would you recommend any different processor? I love your blog. This all provided knowledge are unique than other Deep learning blog. Good explain, keep updating. Do you know if on an x mobo there are ways other than watercooling and two blower fans that would keep the case cool enough?
For example, how much would hybrid cooling AIOs help? Or, if I get an air cooled GPU now, would adding a blower fan to that allow for sufficient cooling? Hoping you can offer any advice on this issue. If you just want to run two cards you can get a motherboard with at least 3 PCIe slots and use non-blower fans.
Because you have a single PCIe slot which is empty between cards cooling is usually sufficient and you can run a bit more silent non-blower fans. Otherwise, AIOs can help. People have mixed reviews about them, some reporting very low temperatures, others report similar temperatures to regular fans. Am I overlooking something? If you have the right case you can install a GPU on the bottom slot.
It only has a 1-slot-width, but in some computer cases, the GPU just extends beyond the motherboard. If you look for cases that optimized for GPU airflow you can probably find a usable case. Dear Tim Dettmers, Thank you very much for this blog. This information is really useful for upcoming deep learning project.
In my work place we are developing a server kind on of system to run three deep learning projects. To maintain this GPU what type of additional resources do I need. I need this GPU only for inference not for training.
My question may look a little broad sorry for that. If you need any other information please let me know. If you require a large amount of memory to hold different kind of models? For inference, in general, the software will be far more important than the hardware.
I bought a rtx super. And I have a system with i5 I can upgrade to a , but would it make a lot of difference? You can also try this if you are using TensorFlow but I am not sure if it will help. The difference is mainly determined by your scripts. If they load a lot of data you can benefit quite a bit from a good CPU.
You can also tweak the data loader threads and see how big the difference is for that. Sometimes you can squeeze a bit more out of your CPU if you tune that parameter. Hi, I am considering buying a GPU for deep learning. If I understand this article right there are different models of RTX card. Could you tell me which parameter in the specification should I pay attention to? Sorry for the confusion, but all RTX have bit capability.
You can pick any card. For especially Kaggle -beginner- competitions Does it look good? Hey Tim, really appreciate your post here. Has been a huge help. The extra PCIe lanes are not worth it. It helps alot! My question is, should I keep everything same but just replace Threadripper X with Ryzen 7 X? Thank you! If it does it is a great and cheap option! First of all thank you very much for your wonderful article with a great insight about Deep Learning.
It helped me to certain extent to understand the hardware requirements needed for any DL machine. But I have an existing system with the following Config:.
Tahnk you! This is extremely helpful. Several of your comments relate to smaller systems, so what are the key caveats for larger systems like the one I need to buy?
Any killer argument for Tesla? Reach out to some hardware vendors that offer these systems. It might be that for such a machine the budged you need is slightly higher 32k euro. RTX Ti has too small memory for your application. The V is too pricey and not good! Thank you very much indeed for your advice.
AMP allows you to train deeper models or larger training batch faster training with limited memory footprint. If you are planning 3D data driven models or multi-channel informations from different sequences I would definitely chose V 32 Gb cards. If we follow this advice we could only start with V GPUs and buy more at a later point in time.
Do you have a comment and could you elaborate what you meant when stating V is …and not good. Thank you once again! It supports all the features of V including AMP. If you think memory is a problem I suggest going with Quadro cards with 48 GB memory instead.
Hi Tim, Thanks for your great article! I have questions, please can you answer them?! What about using multi Titan rtx instead of multi quadro ? Which one will be faster? Also, I found that Lambda uses quadro and Tesla instead of titan rtx for DL server, what is the point? Is that just for double precision? Both are about the same.
Lambda uses quadro because they make more profit. So this could also be a reason. I am looking to do some entry level DL stuff and then build my way upto kaggle. I would appreciate any feedback on the following machine. In this scenario, would it be better to get just one GPU if a Intend to use parallelism? Therefore, if the m. Yes, you are right. I got it wrong the first time around. Hello Tim, this is a great article!
Thanks for all the info. They are supposed to be more powerful than their processors, its said that the RTX super is almost as good as the RTX Can you shed some light on this?
I have not analyzed the data of the GPUs yet. What you say seems accurate from my first impression though. So RTX and Super are good. RTX Super not so much. Thank you for sharing! What are your opinions on Intel i5 k vs i7 k?
Or do you recommend something else? Also can you recommend a good compatible motherboard? I will be using one RTX for now but would like to be future proof for up to 4 one day. One GPU builds usually have no cooling issue. Airflow is not that critical. It is more about what kind of cooling system you have on the GPU.
It seems to have a max of 3 GPUs, vs. Hello Tim, Thanks so much for the blog and replies to comments. I am sorry if this is a reposting, but my comment seemed to have disappeared, so thought I would post again… It would be so helpful to have your insights.
I am attempting to put together a desktop with what I have available online and locally, that is both DL-now ready and future proof. These are the components with some questions:. NVMe 5. Power supply — Corsair smps cx we have occasional power cuts, so thought this is a worthy investment 7.
I think this looks reasonable. You could go with a cheaper AMD processor Ryzen to save some money. Looks good otherwise! Unfortunately most of posts on internet on this are from gaming perspective and do not look too relevant…. You should be fine with an iK for most tasks. If you compare this to getting a full new system sticking with your iK looks like a quite cost-efficient solution.
I would give it a go! I was trying to train a network and came across some problems, and hope you could help me out. The setup I am currently using might be a little unusual.
I went through some PyTorch tutorials and had seemingly no problems with this setup. I have been trying to find out what the problem is. For reference purposes, the price of R is valid , and while stocks last, may be valid until IMG file onto the flash drive. No success either.
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