Which GPU(s) to Get for Deep Learning
41–50 of 119 posts
Re: Which GPU(s) to Get for Deep Learning
#42Question: if I'm learning about neural networks and want to e.g. train a network to recognize MNIST digits, do I need a discrete graphics card (probably attached to a VPS that I would rent)? Or can I use the i5 Kaby Lake (which has an integrated GPU) in my laptop to train my network?
Re: Which GPU(s) to Get for Deep Learning
#43Here's a much more cost effective answer: Use https://cloud.google.com/tpu/
Unless you have an unlimited supply of free electricity and don't care about the increased hardware management overhead, it's a waste of money to buy Pascal GPUs for large-scale deep learning.
The following cards have much more optimized deep learning silicon and are publicly available /right now/:
- Nvidia Tesla V100 (Tensor cores only: 120 TFLOPS FP16)
- Google TPU2 (180 TFLOPS FP16)
Additionally, Intel Xeon chips with the Nervana deep learning accelerator built-in will probably be available early next year.
If you must control the physical hardware yourself and can't use cloud services, go buy Tesla V100s or wait for the Nervana Xeons.
Re: Which GPU(s) to Get for Deep Learning
#44Question: if I'm learning about neural networks and want to e.g. train a network to recognize MNIST digits, do I need a discrete graphics card (probably attached to a VPS that I would rent)? Or can I use the i5 Kaby Lake (which has an integrated GPU) in my laptop to train my network?
You actually don't even need the iGPU for simple MNIST training. The CPU is enough. Check out the sample MNIST training example with Tensorflow.
If you are asking about using the iGPU for a bit more power, most frameworks don't support them. The only frameworks with which you can use with iGPUs are which have OpenCL support, like caffe[1] (even for those you need to have a recent enough CPU)
Tensorflow either uses CPU or CUDA (Not even AMD, but the support is coming for openCL)
Re: Which GPU(s) to Get for Deep Learning
#45Question: if I'm learning about neural networks and want to e.g. train a network to recognize MNIST digits, do I need a discrete graphics card (probably attached to a VPS that I would rent)? Or can I use the i5 Kaby Lake (which has an integrated GPU) in my laptop to train my network?
To train an NN to recognize MNIST digits you need any cpu made in the last 5 years. Last 10-20 years if you want to wait a little longer. MNIST recognition was first done by convolutional neural network in the late 90s.
With less than 1% error. Here's a link to the paper: http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf
Re: Which GPU(s) to Get for Deep Learning
#46How hard does the GPU work on these tasks? Is it Bitcoin mining level power consumption?
Re: Which GPU(s) to Get for Deep Learning
#47Interestingly, AMD is claiming that their upcoming Vega Frontier Editions chips outperform Nvidia's current P100 Deep Learning chip. http://hexus.net/media/uploaded/2017/5/30f5633b-1bbf-49b7-9f...
I think signs are pointing to Vega being a HPC beast. But what they really need is the software ecosystem and support, and so far that hasn't been there. So while the new hardware looks cool, I'm really waiting for an announcement that OpenCL tooling got a lot better, or that CUDA is getting first-party support from AMD, anything to tempt those customers away from nvidia.
You might be interested in knowing that OpenCL is merging with Vulkan's API[1], so there'll likely be a lot more support in the future.
Re: Which GPU(s) to Get for Deep Learning
#48If anyone here doesn't want to spend money on $500+ GPU (or the $1k+ ones!), then I'd suggest getting the lowest tier Nvidia GPU for $100~[1]
If that GPU is a real bottleneck for you, then you're much better off spending money on GCP/AWS's GPU offerings. That's because consumer GPUs get superseeded every year and online offering's price will only go down.
So you can spend 10% of $1k every year and keep getting better return on compute / dollar every year.
[1]:https://www.newegg.com/Product/Product.aspx?Item=N82E1681448...
Re: Which GPU(s) to Get for Deep Learning
#49Question: if I'm learning about neural networks and want to e.g. train a network to recognize MNIST digits, do I need a discrete graphics card (probably attached to a VPS that I would rent)? Or can I use the i5 Kaby Lake (which has an integrated GPU) in my laptop to train my network?
MNIST is a toy problem. It shouldn't take you more than a few days or even hours to completely understand it. Do yourself a favor and get a GPU because soon you will want to try bigger networks on real world images.
1060, 1070, or 1080Ti are all good choices, depending on your budget and ambitions.
Re: Which GPU(s) to Get for Deep Learning
#50Earlier quoted context omitted.
The question is not AMD's processing power, the question is the tools available for AMD cards, which are scant to say the best. While AMD doesn't create something like cuDNN to go along with their cards, no real work will start being done in porting most important DL libraries to AMD cards. And even in that case, it will be a lost generation for AMD, only in the 2nd generation where AMD actually offers a real alterna…
They stated at the analyst day that they will support TensorFlow, Theano, Caffee etc. And will release something called MiOpen. They also stated this last year: > On top of ROCm, deep-learning developers will soon have the opportunity to use a new open-source library of deep learning functions called MIOpen that AMD intends to release in the first quarter of next year. This library offers a range of functions pre-opt…
That would be nice, more competition is better for the consumer. Still, it will affect purchasing decision only when it happens, and it hasn't happened yet.