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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.

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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: 1. 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. Best regards, Salem.

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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?

Nice article! 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 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. Thanks, J.

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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. 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 Ok, thank you! 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.

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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.

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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.

maisonducalvet.com/sitios-de-citas-en-crtama.php 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? 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. It has 2. However, compared to laptop CPUs the speedup will still be considerable.