On what kind of task have you tested this? So it should be fine. I would probably opt for liquid cooling for my next system. Unified memory is more a theoretical than practical concept right now. Both come with same price.
This is highly useful if your main goal is to gain deep learning experience as quickly as possible and also it is very useful for researchers, who want try multiple versions of a new algorithm at the same time. OC GPUs are good for gaming, but they hardly make a difference for deep learning. To ensure your transaction confirms consistently and reliably, Before sending BTC to your wallet, We need to prove that you are human. However, from what I know, Torch7 is a really strong for non-image data, but you will need to learn some lua to adjust some things here and there. I think this also makes practically the most sense. From your blog post I know that I will get a gtx but, what about cpu, ram, motherboard requirement? You should reason in a similar fashion when you choose your GPU.
bitcoin value graph gbp symbols В»
CPUs design to the exchange exact opposite: Found it tesla useful and I felt GeForce suggestion for Kaggle m2090 really apt. If you need the performance, you often also need the memory. I have only superficial experience with the most libraries, as I usually used my own implementations which I adjusted from problem to problem. The 2010 is a good choice to try bitcoin out, and nvidia deep learning on kaggle. If your simulations require double precision then you could still put your money into a regular GTX Titan.
I would try pylearn2, convnet2, and caffe and pick which suits you best 4. The implementations are generally general implementations, i. Hi Tim, super interesting article.
What case did you use for the build that had the GPUs vertical? It looks like it is vertical, but it is not. I took that picture while my computer was laying on the ground.
I bought this tower because it has a dedicated large fan for the GPU slot — in retrospect I am unsure if the fan is helping that much. There is another tower I saw that actually has vertical slots, but again I am unsure if that helps so much.
I would probably opt for liquid cooling for my next system. It is more difficult to maintain, but has much better performance. With liquid cooling almost any case would go that fits the mainboard and GPUs. It looks like there is a bracket supporting the end of the cards, did that come with the case or did you put them in to support the cards?
My perception was that a card with more cores will always be better because more number of cores will lead to a better parallelism, hence the training might be faster, given that the memory is enough. Plz correct me if my understanding is wrong.
A would be good enough? Or should I go with a one? Thanks for your comment. So cuda cores are a bad proxy for performance in deep learning. What you really want is a high memory bus width e. Thanks for your excellent blog posts.
I am a statistician and I want to go into deep learning area. Can you recommend me a good desktop system for deep learning purposes? From your blog post I know that I will get a gtx but, what about cpu, ram, motherboard requirement? Hi Yakup, I wanted to write a blog post with detailed advice about this topic sometimes in the next two weeks and if you can wait for that you might get some insights what hardware is right for you. But I also want to give you some general, less specific advice. The CPU does not need to be fast or have many cores.
Fast memory caches are often more important for CPUs, but in the big picture they also contribute little in overall performance; a typical CPU with slow memory will decrease the overall performance by a few percent. If you get a SSD, you should also get a large hard drive where you can move old data sets to. Nice and very informative post. I have a question regarding processor.
Could you please give your thought on this? Thanks for your comment, Dewan. Transferring data means that the CPU should have a high memory clock and a memory controller with many channels. This is often not advertised on CPUs as it not so relevant for ordinary computation, but you want to choose the CPU with the larger memory bandwidth memory clock times memory controller channels.
The clock on the processor itself is less relevant here. You could definitely settle for less without any degradation in performance. But what you say about PCIe 3. As I wrote above I will write a more detailed analysis in a week or two. Thanks for the excellent detailed post. I look forward to reading your other posts.
And when I think about more expensive cards like or the ones to be released in it seems like running a spot instance for 10 cents per hour is a much better choice. However, you cannot use them for multi-GPU computation multiple GPUs for one deep net as the virtualization cripples the PCIe bandwidth; there are rather complicated hacks that improve the bandwidth, but it is still bad. It seems to run the same GPUs as those in the g2. More than 4 GPUs still will not work due to the poor interconnect.
The models which I am getting in ebay are around USD but they are 1. One big problem with the would be, buying a new PSU watt. My research area is mainly in text mining and nlp, not much of images. Other than this I would do Kaggle competetions. However, maybe you want to opt for the 2 GB version; with 1 GB it will be difficult to run convolutional nets; 2 GB will also be limiting of course, but you could use it on most Kaggle competitions I think. The commend is quite outdated now.
I personally favor PyTorch. I believe one can be much more productive with PyTorch — at least I am. Is this possible to check before purchasing? Any comments on this new Maxwell architecture Titan X? Along that line, are the memory bandwith specs not apples to apples comparisons across different Nvidia architectures? You can compare bandwidth within microarchitecture Maxwell: With Maxwell the NVIDIA engineers developed an architecture which has both energy efficiency and good bandwidth utilization, but the double precision suffered in turn — you just cannot everything.
Thus Maxwell cards make great gaming and deep learning cards, but poor cards for scientific computing. The GTX Titan X is so fast, because it has a very large memory bus width bit , an efficient architecture Maxwell and a high memory clock rate 7 Ghz — and all this in one piece of hardware.
I think you can also get very good results with conv nets that feature less memory intensive architectures, but the field of deep learning is moving so fast, that 6 GB might soon be insufficient. Right now, I think one has still quite a bit of freedom with 6 GB of memory. So this would be an acceptable procedure for very large conv nets, however smaller nets with less parameters would still be more practical I think. Thanks for this post Tim, is very illustrating.
Overall, the fan design is often more important than the clock rates and extra features. The best way to determine the best brand, is often to look for references of how hot one card runs compared to another and then think if the price difference justifies the extra money. Most often though, one brand will be just as the next and the performance gains will be negligible — so going for the cheapest brand is a good strategy in most cases.
Results may vary when GPU Boost is enabled. Hi Tim, great post! I feel lucky that I chose a a couple of years ago when I started experimenting with neural nets. Windows went on fine although I will rarely use it and Ubuntu will go on shortly. Yeah, I also had my troubles with installing the latest drivers on ubuntu, but soon I got the hang of it. You want to do this: Download driver and remember the path where you saved the file 1. Purge system from nvidia and nouveau driver 2. Blacklist nouveau driver 3.
And you should be done. Sometimes I had troubles with stopping lightdm; you have two options: You can find more details to the first steps here: Thanks for the reply Tim. I was able to get it all up and running pretty painlessly. One possible information portal could be a wiki where people can outline how they set up various environments theano, caffe, torch, etc..
A holistic outlook would be a very education thing. I found myself building the base libraries and using the setup method for many python packages but after a while there were so many I started using apt-get and pip and adding things to my paths…blah blah…at the end everything works but I admin I lost track of all the details. Having a wiki resource that I could contribute to during the process would be good for me and for others doing the same thing…. I mention this because you probably already have a ton of traffic because of a couple key posts that you have.
Thanks, johno, I am glad that you found my blog posts and comments useful. A wiki is a great idea and I am looking into that. Maybe when I move this site to a private host this will be easy to setup. Right now I do not have time for that, but I will probably migrate my blog in a two months or so. X-org-edgers PPA has them and they keep them pretty current.
It also blacklists Nouveau automatically. You can toggle between driver versions in the software manager as it shows you all the drivers you have. Once you have the driver working, you are most of the way there. I ran into a few troubles with the CUDA install, as sometimes your computer may have some libraries missing, or conflicts. Hi Tim- Does the platform you plan on DLing on matter?
Hi Jack- Please have a look at my full hardware guide for details, but in short, hardware besides the GPU does not matter much although a bit more than in cryptocurrency mining. Hi Tim, I have benefited from this excellent post. I have a question regarding amazon gpu instances.
Can you give a rough estimate of the performance of amazon gpu? Thanks, this was a good point, I added it to the blog post. If you perform multi-GPU computing the performance will degrade harshly. Hi Tim, Thanks for sharing all this info. Obviously same architecture, but are they much different at all? Why it seems hard to find Nvidia products in Europe? So this is the way how a GPU is produced and comes into your hands: You buy the GPU from either 5. Both GPUs run the very same chip.
So essentially, all GPUs are the same for a given chip. Hi Tim, Thank you for your advices I found them very very useful. I have many questions please and feel very to answer some of them. Could you please tell me if this possible and easy to make it because I am not a computer engineer, but I want to use deep learning in my research. If there are technical details that I overlooked the performance decrease might be much higher — you will need to look into that yourself. While most deep learning libraries will work well with OSX there might be a few problems here and there, but I think torch7 will work fine.
However, consider also that you will pay a heavy price for the aesthetics of apple products. Does it need external hardware or power supply or just plug in? You recommended all high-end cards. What about mid-range cards for those with a really tight budget? Will such a card likely give a nice boost in neural net training assuming it fits in the cards mem over a mid-range quad-core CPU? Maybe I should even include that option in my post for a very low budget. Thanks for this great article.
What do you think of the upcoming GTX Ti? The GTX Ti seems to be great. If you use Nervana System 16 bit kernels which will be integrated into torch7 then there should be no issues with memory even with these expensive tasks.
Hi, I am a novice at deep nets and would like to start with some very small convolutional nets. I was thinking of using a GTXTI in my part of the world it is not really very cheap for a student.
I would convince my advisor to get a more expensive card after I would be able to show some results. Will it be sufficient to do a meaning convolutional net using Theano? Your best choice in this situation will be to use an amazon web service GPU spot instance. This should be the best solution. Because deep learning is bandwidth-bound, the performance of a GPU is determined by its bandwidth. Comparisons across architectures are more difficult and I cannot assess them objectively because I do not have all the GPUs listed.
To provide a relatively accurate measure I sought out information where a direct comparison was made across architecture. So all in all, these measure are quite opinionated and do not rely on good evidence. Therefore I think it is the right thing to include this somewhat inaccurate information here.
Thanks a lot for the updated comparison. Can you comment on this note on the cuda-convnet page https: It will be slow. This is very much true. The performance of the GTX is just bad. So probably it is better to get a GTX if you find a cheap on. If this is too expensive, settle for a GTX How bad is the performance of the GTX ? Is it sufficient to have if you mainly want to get started with DL, play around with it, do the occasional kaggle comp, or is it not even worth spending the money in this case?
Ah I did not realize, the comment of zeecrux was on my other blog post, the full hardware guide. Here is the comment:. It should be sufficient for most kaggle competitions and is a perfect card to get startet with deep learning. Hey Tim, Can i know where to check this statement? Check this stackoverflow answer for a full answer and source to that question.
The Pascal architecture should be a quite large upgrade when compared to Maxwell. However, you have to wait more than a year for them to arrive. If your current GPU is okay, I would wait. Not sure what am I missing. You will need a Mellanox InfiniBand card. Even with that I needed quite some time to configure everything, so prepare yourself for a long read of documentations and error google search queries. My questions are whether there is anything I should be aware of regarding using quadro cards for deep learning and whether you might be able to ball park the performance difference.
We will probably be running moderately sized experiments and are comfortable losing some speed for the sake of convenience; however, if there would be a major difference between the and k, then we might need to reconsider. I know it is difficult to make comparisons across architectures, but any wisdom that you might be able to share would be greatly appreciated. Thus is should be a bit slower than a GTX I am in a similar situation. No comparison of quadro and geforce available anywhere.
Just curious, which one did you end up buying and how did it work out? Talking about the bandwidth of PCI Ex, have u ever heard about plx tech with their pex bridge Chip. Anandtech has a good review on how does it work and effect on gaming: They even said that it can also replicate 4 x16 lanes on a cpu which is 28lanes.
Someone mentioned it before in the comments, but that was another mainboard with 48x PCIe 3. It turns out that this chip switches the data in a clever way, so that a GPU will have full bandwidth when it needs high speed. However, when all GPUs need high speed bandwidth, the chip is still limited by the 40 PCIe lanes that are available at the physical level.
When we transfer data in deep learning we need to synchronize gradients data parallelism or output model parallelism across all GPUs to achieve meaningful parallelism, as such this chip will provide no speedups for deep learning, because all GPUs have to transfer at the same time. Transferring the data one after the other is most often not feasible, because we need to complete a full iteration of stochastic gradient descent in order to work on the next iterations.
This would make this approach rather useless. Maybe a pre-built specs with http: However, compared to laptop CPUs the speedup will still be considerable. To do more serious deep learning work on a laptop you need more memory and preferably faster computation; a GTX M or GTX M should be very good for this. This is my first time. Looks like a solid cheap build with one GPU.
The build will suffice for a Pascal card once it comes available and thus should last about 4 years with a Pascal upgrade. The GTX is a good choice to try things out, and use deep learning on kaggle. Once you get the hang of it, you can upgrade and you will be able to run the models that usually win those kaggle competitions.
Both come with same price. Which one do you think is better for conv net? Or Multimodal Recurrent Neural Net. Running multiple algorithms different algorithms on each GPU on the two GTX will be good, but a Titan X comes close to this due to its higher processing speed. It seems that we can only get the latter.
There are no issue with the card, it should work flawlessly. Reason I ask is that a cheap used superclocked Titan Black is for sale on ebay as well as another cheap Titan Black non-superclocked.
Yes, this will work without any problem. I myself have been using 3 different kind of GTX Titan for many months. In deep learning the different of compute clock also makes hardly a difference, so that the GPUs will not diverge during parallel computation. So there should be no problems. Thank you very much for you in-depth hardware analysis both this and the other one you did.
But in a lot of places I read about this imagenet db. The problem there seems to be that i need to be a researcher or in education to download the data. Do you know anything about this? Is there any way for me as a private person that is doing this for fun to download the data? The reason why I want this dataset is because it is huge and it also would be fun to be able to compare how my nets works compared to other people.
Hello Mattias, I am afraid there is no way around the educational email address for downloading the dataset. It is really is a shame, but if these images would be exploited commercially then the whole system of free datasets would break down — so it is mainly due to legal reasons.
There are other good image datasets like the google street view house number dataset; you can also work with Kaggle datasets that feature images, which has the advantage that you get immediate feedback how well you do and the forums are excellent to read up how the best competitors did receive their results.
Thank you for your article. I understand that researchers need a good GPU for training a top performing convolutional neural network. Can you share any thought on what compute power is required or what is typically desired for transfer learning i. Tim, Such a great article. I see that it has 6gb x 2. I guess my question is: Is the Titan Z have the same specs as the Titan X in terms of memory? How does this work from a deep learning perspective currently using theano.
Please have a look at my answer on quora which deals exactly with this topic. That makes much more sense. Thanks again — checked out your response on quora. Hey Tim, not to bother too much. I bought a Ti, and things have been great. I was wondering what your thoughts are on this? Based upon numbers, it seems that the AMD cards are much cheaper compared to Nvidia. I was hoping you could comment on this! Theoretically the AMD card should be faster, but the problem is the software: Come across the internet for deep learning on this blog is great for newbie like me.
I have 2 choices in hands now: Which one do you recommend that should come to the hardware box for my deep learning research? However, the 2 GTX Ti will much better if you run independent algorithms and thus enables you to learn how to train deep learning algorithms successfully more quickly. On the other hand, the 3GB on them is rather limiting and will prevent you to train current state of the art convolutional networks.
Thank you very much for the advice. Yes, you could run all three cards in one machine. However you can only select one type of GPU for your graphics; and for parallelism only the two will work together. There might be problems with the driver though, and it might be that you need to select your Maxwell card to be your graphics output. In a three card system you could tinker with parallelism with the s and switch to the if you are short on memory.
I will benchmark and post the result once I got hand on to run the system with above 2 configuration. How does this card rank compared to the other models?
More importantly, are there any issues I should be aware of when using this card or just doing deep learning on a virtual machine in general?
Generally there should not be any issue other than problems with parallelism. Are there any on demand solution such as Amazon but with Ti on board? Amazon needs to use special GPUs which are virtualizable. Currently the best cards with such capability are kepler cards which are similar to the GTX However, other vendors might have GPU servers for rent with better GPUs as they do not use virtualization , but these server are often quite expensive.
First of all, I bounced on your blog when looking for Deep Learning configuration and I loved your posts that confirm my thoughts. I have two questions if you have time to answer them: I would like to have answers by seconds like Clarifai does. I guess this is dependent of the number of hidden layers I could have in my DNN. However, this benchmark page by Soumith Chintala might give you some hint what you can expect from your architecture given a certain depth and size of the data.
You usually use LSTMs for labelling scenes and these can be easily parallelized. However, running image recognition and labelling in tandem is difficult to parallelize. You are highly dependent on implementations of certain libraries here because it cost just too much time to implement it yourself. So I recommend to make your choice for the number of GPUs dependent on the software package you want to use.
I have heard from other people that use multiple GPUs that they had multiple failures in a year, but I think this is rather unusual. If you keep the temperatures below 80 degrees your GPUs should be just fine theoretically. Awesome work, this article really clears out the questions I had about available GPU options for deep learning. What can you say about the Jetson series, namely the latest TX1?
I was also thinking about the idea to get a Jetson TX1 instead of a new laptop, but in the end it is more convenient and more efficient to have a small laptop and ssh into a desktop or an AWS GPU instance. I use various neural nets i. What is better if we set price factor aside? What concrete troubles we face using on large nets?
So the GTX does not have memory problems. Regarding your question of vs. The is much better if you can stay below 3. If you train sometimes some large nets, but you are not insisting on very good results rather you are satisfied with good results I would go with the GTX If you train something big and hit the 3. Indeed, I overlooked the first screenshot, it makes a difference. I was thinking about GTX issue again.
According to the test, it loses bandwidth above 3. But what does it mean exactly? I guess no -does it decrease GPU computing performance itself? I guess no -what if input data allocated in GPU memory below 3.
In that case upper 0. What do you think on this? May I be able to give pascal voc as well? The GTX will be a bit slow, but you should still be able to do some deep learning with it. If you are using libraries that support 16bit convolutional nets then you should be able to train Alexnet even on ImageNet; so CIFAR10 should not be a problem.
Thanks you very much. Your article and help was of great help to me sir and I thank you from the bottom of my heart. God bless you Hossein. Hi Tim Thanks a lot for this article. I was looking for something like this. I have a quick question.
A rough idea would do the job? I wonder what exactly happens when we exceed the 3. I want to know, if passing the limit and getting slower, would it still be faster than the GTX? If it is so , that would be great.
Has anyone ever observed or benchmarked this? Thanks alot, actually I dont want to play with this card, I need its bandwidth and its memory to run some applications a deep learning Framework called caffe. My current cards bandwidth is only 80!
So I just need to know, Do I have access to the whole 4 gigabyte of vram? Does it crash if it exceeds the 3. Rather, it seems is slightly faster than Would you tell me the reason?
Hmm this seems strange. It might be that the GTX hit the memory limit and thus is running more slowly so that it gets overtaken by a GTX On what kind of task have you tested this?
It will be a bit slower to transfer data to the GPU, but for deep learning this is negligible. So not really a problem. Thank you for sharing this. Please update the list with new Tesla P and compare it with TitanX. Hi Tim Thanks a lot for sharing such valuable information. Do you know if it will be possible to use and external GPU enclosure for deep learning such as a Razer core? This should still be better than the performance you could get for a good laptop GPU.
What can I expect from a Quadro MM see http: I keep coming to this great article. I was about to buy a ti only when discovered that today nvidia announced the pascal gtx to be released in the end of may I will update the blog post soon.
I want to wait until some reliable performance statistics are available. Titan x in Amazon priced around to usd vs usd in nvidia online store. Do you advise against buying the original nvidia? What is the difference? Which brand you prefer? I read this interesting discussion about the difference in reliability, heat issues and future hardware failures of the reference design cards vs the OEM design cards: The opinion was strongly against buying the OEM design cards. I read all the 3 pages and it seems there is no citation or any scientific study backing up the opinion, but it seems he has a first hand of experience who bought thousands of NVidia cards before.
So what is your comment about this? I asked the same question to the author of this blog post Matt Bach of Puget systems and he was kind to answer based on around Nvidia cards that they have installed in his company: I will quote the discussion happened in the comments of the above article, in case anybody is interested:. I will tell you, however, that we lean towards reference cards if the card is expected to be put under a heavy load or if multiple cards will be in a system. That is fine for a single card, but as soon as you stack multiple cards into a system it can produce a lot of heat that is hard to get rid of.
The Linus video John posted in reply to your comment lines up pretty closely what we have seen in our testing. I did go ahead and pull some failure numbers from the last two years. The most telling is probably the field failure rate since that is where the cards fail over time.
Overall, I would definitely advise using the reference style cards for anything that is heavy load. We find them to work more reliably both out of the box and over time, and the fact that they exhaust out the rear really helps keep them cooler — especially when you have more than one card. Recently Nvidia began selling their own cards by themselves with a bit higher price.
What will be your preference? The cards that Nvidia are manufacturing and selling by themselves or a third party reference design cards like EVGA or Asus? Really hard to know if NVIDIA would have a different reliability than other brands but my gut instinct is that the difference would be minimal.
Your blog posts have become a must-read for anyone starting on deep learning with GPUs. Very well written, especially for newbies.
I am thinking of putting together a multi GPU workstation with these cards. If you could compare the with Titan or series cards, that would be super useful for me and i am sure quit a few other folks. Thank you for this great article. What is your opinion about the new Pascal GPUs?
Both cards are better. I do not have any hard data on this yet, but it seems that the GTX is just better — especially if you use bit data.
How good is GTX m for deep learning? A GTX m is pretty okay, especially the 6GB variant will be enough to explore deep learning and fit some good models on data. However, you will not be able to fit state of the art models, or medium sized models in good time. I was under the impression that single precision could potentially result in large errors. I admit I have not experimented with this, or tried calculating it, but this is what I think. The problem with actual deep learning benchmarks is hat you need the actually hardware and I do not have all these GPUs.
Working with low precision is just fine. The error is not high enough to cause problems. It was even shown that this is true for using single bits instead of floats since stochastic gradient descent only needs to minimize the expectation of the log likelihood, not the log likelihood of mini-batches.
Yes, Pascal will be better than Titan or Titan Black. Half-precision will double performance on Pascal since half-floating computations are supported. This is not true for Kepler or Maxwell, where you can store bit floats, but not compute with them you need to cast them into bits. Hi, Very nice post! Found it really useful and I felt GeForce suggestion for Kaggle competitions really apt. Any thoughts on this? If you have a slower 6GB card then you have to wait longer but it is still much faster than a laptop CPU, and although slower than a desktop you still get a nice speedup and a good deep learning experience.
Getter one of the fast cards is however often a money issue as laptops that have them are exceptionally expensive.
So a laptop card is good for tinkering and getting some good results on kaggle competition. However, if you really want to win a deep learning kaggle competition computational power is often very important and then only the high end desktop cards will do. You only need InfiniBand if you want to connect multiple computers.
Hi Tim, thanks for updating the article! Your blog helped me a lot in increasing my understanding of Machine Learning and the Technologies behind it.
Unfortunately I have still some unanswered questions where even the mighty Google could not help! Could you please suggest one?
Titan X does not allow this. Try to recheck your configuration. I am running deep learing and a display driver on a GTX Titan X for quite some time and it is running just fine. One final question, which may sound completely stupid. It seems that mostly reference cards are used. I was eager to see any info on the support of half precision 16 bit processing in GTX Some articles were speculating few days before their release that they might be inactivated by nVidia and reserving this feature for future nVidia P pascal cards.
However after around1 month from releasing the gtx series, nobody seems to mention anything related to this important feature. And as you mentioned it will add the bonus for less memory requirements up to half.
However it is still not clear whether the accuracy of the NN will be the same in comparison to the single precision and whether we can do half precision for all the parameters. Which of course are important to estimate how much will be the speedup and how much less is the memory requirement for a given task. But i keep getting errors. Thanks for a great article, it helped a lot. I have a question regarding the amount of CUDA programming required if I decide to do some sort of research in this field.
I have mostly implemented my vanilla models in Keras and learning lasagne so that I can come up with novel architecture. I know quite many researchers whose CUDA skills are not the best. You often need CUDA skills to implement efficient implementations of novel procedures or to optimize the flow of operations in existing architectures, but if you want to come of with novel architectures and can live with a slight performance loss, then no or very little CUDA skills are required.
Sometimes you will have cases where you cannot progress due to your lacking CUDA skills, but this is rarely an issue. So do not waste your time with CUDA! I know its a crap card but its the only Nvidia card I had lying around. Should I go to windows 10? Your card, although crappy, is a kepler card and should work just fine. Windows could be the issue here. Often it is not well supported by deep learning frameworks. You could try CNTK which has better windows support.
If you try CNTK it is important that you follow this install tutorial step-by-step from top to bottom. I would not recommend Windows for doing deep learning as you will often run into problems. I would encourage you to try to switch to Ubuntu. Although the experience is not as great when you make the switch, you will soon find that it is much superior for deep learning. Getting things going on OSX was much easier. What are you thoughts on the GTX ? Over-clocking the looks like it can get close to a FE minus 2GB of memory.
What your thoughts about the investment on a Pascal architecture based GPU currently? However, this of course depends on your applications and then of course you can always sell your Pascal GPU once Volta hits the market.
Both options have its pro and cons. Unified memory is more a theoretical than practical concept right now. Currently you will not see any benefits for this over Maxwell GPUs. I have a used 6gb on hand. I am planning to get into research type deep learning.
However I am still getting started and dont understand all the nitty gritty of parameter tuning batch sizes etc. You mention 6gb would be limiting in deep learning. If I understand right using small batch sizes would not converge on large models like resnet with a I am shooting in the dark here wrt terminology since I still a beginner. Could I use some system ram to remove the 6gb limitation. Since and by inference 6gb since they both have gp also has this ConcurrentManagedAccess set to 1 according to https: It might be an good alternative.
I think the easiest and often overlooked option is just to switch to bit models which doubles your memory. Other than that I think one could always adjust the network to make it work on 6GB — with this you will not be able to achieve state-of-the-art results, but it will be close enough and you save yourself from a lot of hassle. I think this also makes practically the most sense. My brother recommended I would possibly like this blog.
He used to be totally right. This submit actually made my day. They have been very usefull for me. I realized two benchmarks in order to compare performance in different operating systems but with practically same results. Is there any other framework which support Pascal architecture with full speed?
Windows 7 64bit Nvidia drivers: Visual studio 64bit, CUDA 7. GTX Ti perfomance: I am not entirely sure how convolutional algorithm selection works in Caffe, but this might be the main reason for the performance discrepancy.
The cards might have better performance for certain kernel sizes and for certain convolutional algorithms. But all in all these are quite some hard numbers and there is little room for arguing.
I think I need to update my blog post with some new numbers. To learn that the performance of Maxwell cards is such much better with cuDNN 4. I will definitely add this in an update to the blog post.
I am a little worry about upgrading later soon. Maybe this was a bit confusing, but you do not need SLI for deep learning applications. The GPUs communicate via the channels that are imprinted on the motherboard. So you can use multiple GTX in parallel without any problem. Extremely thankful for the info provided in this post. I am facing some hardware issues with installing caffe on this server. It has ubuntu Without that you can still run some deep learning libraries but your options will be limited and training will be slow.
I am interested in having your opinion on cooling the GPU. Furthermore, they would discourage adding any cooling devices such as EK WB as it would void the warranty. What are your thoughts? Is the new Titan Pascal that cooling efficient? If not, is there a device you would recommend in particular?
Do you think it could deliver increased performance on single experiment? I would also like to add that looking at the DevBox components, No particular cooling is added except for sufficient GPU spacing and upgraded front fans. From my experience addition fans for your case are negligible less than 5 degrees differences; often as low as degrees.
If you only run a single Titan X Pascal then you will indeed be fine without any other cooling solution. Sometimes it will be necessary to increase the fan speed to keep the GPU below 80 degrees, but the sound level for that is still bearable. If you use more GPUs air cooling is still fine, but when the workstation is in the same room then noise from the fans can become an issue as well as the heat it is nice in winter, then you do not need any additional heating in your room, even if it is freezing outside.
If you have multiple GPUs then moving the server to another room and just cranking up the GPU fans and accessing your server remotely is often a very practical option. If those options are not for you water cooling offers a very good solution. Do But perhaps I am missing something….
Is it clear yet whether FP16 will always be sufficient or might FP32 prove necessary in some cases? We will have to wait for Volta for this I guess. Probably FP16 will be sufficient for most things, since there are already many approaches which work well with lower precision, but we just have to wait. I think you can do regular computation just fine. However, I do not know how the support for Tensorflow is, but in general most the deep learning frameworks do not have support for computations on 8-bit tensors.
You might have to work closer to the CUDA code to implement a solution, but it is definitely possible. If work with 8-bit data on the GPU, you can also input bit floats and then cast them to 8-bits in the CUDA kernel; this is what torch does in its 1-bit quantization routines for example. Would multi lower tier gpu serve better than single high tier gpu given similar cost? Here is one of my quora answer s which deals exactly with this problem. The cards in that example are different, but the same is true for the new cards.
Thank for the reply. One more question, does slower ddr ram bandwidth will impact the performance of deeplearning? That is correct, for multiple cards the bottleneck will be the connection between the cards which in this case is the PCIe connection.
This comparison however is not valid between different GPU series e. I have been given a Quadro M 24GB. How do you think it compares to a Titan or Titan X for deep learning specifically Tensorflow? The Quadro M is an excellent card!
I do not recommend it because it is not very cost efficient. However, the very large memory and high speed which is equivalent to a regular GTX Titan X is quite impressive. So I would definitely stick to it! Hey Tim, thank you so muuuch for your article!! I am more specifically interested in autonomous vehicle and Simultaneous Localization and Mapping. You article has helped me clarify my currents needs and match it with a GPU and budget.
Thank you for this fantastic article. I have learned a lot in these past couple of weeks on how to build a good computer for deep learning. My question is rather simple, but I have not found an answer yet on the web: In the past I would have recommended one faster bigger GPU over two smaller, more cost-efficient ones, but I am not so sure anymore. The parallelization in deep learning software gets better and better and if you do not parallelize your code you can just run two nets at a time.
However, if you really want to work on large datasets or memory-intensive domains like video, then a Titan X Pascal might be the way to go. I think it highly depends on the application. If you do not necessarily need the extra memory — that means you work mostly on applications rather than research and you are using deep learning as a tool to get good results, rather than a tool to get the best results — then two GTX should be better.
Otherwise go for the Titan X Pascal. First of all thank you for your reply. I am ready to finally buy my computer however I do have a quick question about the ti and the Titan xp. I understand that in your first post you said that the Titan X Pascal should be the one, however I would like to know if this is still the case on the newer versions of the same graphics cards.
I think two GTX Ti would be a better fit for you. It does not sound like you would need to push the final performance on ImageNet where a Titan Xp really shines. Thanks so much for your article. It was instrumental in me buying the Maxwell Titan X about a year ago. For example, if it takes me 0. Were you getting better performance on your Maxwell Titan X? It also depends heavily on your network architecture; what kind of architecture were you using?
Data parallelism in convolutional layers should yield good speedups, as do deep recurrent layers in general. However, if you are using data parallelism on fully connected layers this might lead to the slowdown that you are seeing — in that case the bandwidth between GPUs is just not high enough. I just have one more question that is related to the CPU. I understand that having more lanes is better when working with multiple GPUs as the CPU will have enough bandwidth to sustain them.
However, in the case of having just one GPU is it necessary to have more than 16 or 28 lanes? Is this going to be too much of an overkill for the Titan X Pascal? If you are having only 1 card, then 16 lanes will be all that you need. Even if you are using 8 lanes, the drop in performance may be negligible for some architectures recurrent nets with many times steps; convolutional layers or some parallel algorithms 1-bit quantization, block momentum.
So you should be more than fine with 16 or 28 lanes. I compared quadro k with m I am looking for a higher performance single-slot GPU than k Check your benchmarks and if they are representative of usual deep learning performance. The K should not be faster than a M What kind of simple network were you testing on? I tested the simple network on a chainer default example as below. Great article, very informative.
Ah this is actually true. I did not realize that! Thanks for pointing that out! I live at a place where kwh costs The electricity bills grows exponentially. I usually train unsupervised learning algorithms on 8 terabytes of video. It will take maximum minutes and after that you'll receive the requested amount in your wallet. Info The activities are delayed due to high traffic. Is this tool free?
Yes, this tool is free and is developed and hosted by our team to help each of you to enjoy the power of bitcoin. How it works our tool? Our software is a brand new tool that makes bitcoin mining more faster than any tool on the market.
We are a team of programmers with over 5 years experience in the bitcoin industry. Is this tool safe? Our tool is safe because you don't need to download anything and every process is executed on our servers. Claim Bitcoin For Free!
This operation takes a while and cannot be stopped, check your address before confirming.