When the double arrow pointer appears, drag up or down to position the row. Multi-processes profiling is only supported for timeline data. In this case Unified Memory profiling is not supported. This column displays a tree of events representing the structure of the application's execution on the CPU. The log files are in XML format. Alternatively, one can make use of the new feature to turn on profiling on the nodes of interest using the --profile-all-processes argument to nvprof.
2. Configuring Parallel Training in CNTK in Python
Once again, thank you so much for all the help, Tim! I will be watching out for your new updates for sure! Do you think the choice of a CPU matters that much? The CPU choice can be complex, but I will post a new blog entry in the next days which will deal exactly with this topic — so stay tuned!
I am a beginner in the field of deep learning and I want to buy a laptop to try out some deep learning algorithms such as Convolution Nets, Neural Turing Machine and do some Kaggle projects. For now, I have some options: My room is too small for a desktop so a poweful laptop is my only option. It sure would be a lot easier to follow your recommendations if they were laid out in some sort of table maybe cost versus performance …or something like that.
Thanks for the feedback! I will try to make things clearer with an update. Here a simple algorithm how to find the best GPU. Generally, there are two questions you need to answer yourself: Then there is also eBay which makes things cheaper, but for newer GPUs the price difference is negligible, and so the algorithm above should most often yield the GPU that is best for you.
Specifically the list of both hardware and software? This is the best 4 GPU system, but by far also the most expensive one. I have a core I7 Win 7 laptop I could perhaps install and learn to use the software; yet, I am not sure on the software packages as there are many. I think you mentioned Red Hat and the Nvidia devbox software list mentions Ubuntau plus other apps like Theano, Torch, etc.
I want to make the best choice but the details are fuzzy. During my learning of the software and algorithms on the laptop I would begin building the new machine.
I would use Torch7 and ubuntu It is not only the most productive setup that there is at the moment, but probably also the easiest to use and the one with the most features. So with that combination you will have a versatile, productive system with an okay learning curve. I have a super fast desktop…what are your thoughts on running a VirtualBox and installing ubuntu and Torch on that?
Slow as ice melting and no good for machine learning? The virtualization only allows on some hardware combinations to do GPU computing on a virtual machine; also the performance is often poor for PCIe connections just like on AWS , so I do not recommend using a virtual machine for deep learning.
And I liked his previous post about the divide and conqur way to scale up in a stepwise fashion to build a high end system eventually but slowly, and learn about deep NN in the meantime. My plan is almost the same. I think his query and mine is because currently, we have only one powerful Pc and Windows is very essential to us to run several other software, and it is not easy to make it as a dual boot with Ubuntu, since Deep NN experiments will take days and we cannot afford a dedicated Windows Pc.
What are those hardware combinations? Does this hardware combination allow me to use Torch with GPU computing? There are ways to run Caffe under windows I never tested them, but it seems very possible ; running Torch on windows is rather problematic and I would not recommend trying that. The hardware you need is a CPU with virtualization support — and very important — the GPU must also support virtualization. I think overall your best bet would be to run a deep learning framework on windows besides Caffee, Theano should work too.
This depends mainly on the size of the images and the size of the data set. However, you could always shrink down those images and use a 4GB GPU, but your results will be worse. Also see my GPU advice post for more information. I got a Titan X with 12GB memory. But there seems no improvement. One processor will be better.
Two processors can complicated the PCIe layout which might make your deep learning software that feature parallelism slower. Of course we want to use two infiniband switches for it. I got RDMA GPUDirect working on my ConnectX2 cards, but it is not support by Mellanox so you have to hack yourself through that, so I recommend that you first get a small system and test all the technicalities before you expand your system. Using two ports at the same time should work, but you should be aware of possible performance bottlenecks.
With two ports you will have four messages per GPU pair that will compete for a lane to the network card. If there are some latency issue this might be a considerable bottleneck due to waiting time on consecutive messages, but I have never tested myself nor seen data on this kind of setup.
So again, getting a small system 2 computers with 3 GPUs each and testing if everything works might be best before you make the step to buy the whole system. Hi Tim, Learned a lot from you blog.. I know that sli has no improvement for deep learning, but what if I disable the sli and use 2 m instead?
So my question is will two m better than a single ? Regarding 2x m vs a The two m are more useful if you want to train different architectures at the same time. So these have both advantages and disadvantages, no choice is really better than the other.
However, currently I am working on a cluster of Xeon Phis and probably soon also a cluster of GPUs for which I am currently developing software Tensorflow benchmarks are rather poor, I think I will be able to improve upon that, but it will take some months to get the library into a state which is useful to others. Hi Tim, congrats for this very interesting blog. Because my students have to work on cpu machines and i would like to provide them a reasonable speed but with a very fast learning curve of the basic deep learning tools.
My question is, under you expert point of view, do you see any future on CPU computing for DeepLearning? What about the Xeon Phi cluster you mentioned? Personal view on your question: Regarding framework, Keras and Lasagne are easier to use and understand for students, maybe you should check them. Hi Marc, thanks for your comments. I know the frameworks you mentioned, But now i have my own very finished and ready to put on github. It is quite easy to define a net, something like:.
Hi, Thanks for nice postings. I have several questions about 4 -way Titan X system. And I have 3 questions. Is is related to system stability? Thanks for your reply! There are a few things changed in spec. Now I think the only problem is cooling. I have read all your posts in your blog, but still few questions remain.
Is it OK to use? In this case, is the setting for fixing fan speed to maximum still needed? Assume distance between two PCI-e slots are quite OK, for example, two pci-e slots placed at 1st and 4th bay 5 Do you have any plan to upgrade your system to gtx ? K40 info could be found in nvidia-smi, but for desktop gpus, nvidia-smi may not work?
Yes I did get it working with a Mellanox Infiniband card, but it took quite a bit of work. Then it is a lot of configuration and stuff. Took me a couple of days, but I guess with the newer cards it will be straightforward to get everything working.
No, the GTX is better. After searching the internet ,I think there may be something wrong about my xorg. It would be of much help if anybody could share with me your solutions,thx! I had some problems like that before, installing those drivers can be messy. However, there should be a new ppa which is designed for nvidia driver install and which should work flawlessly.
I do not have the exact commands, but you might find them quickly using a google search. One thing that comes to my head, is that you should disable the X-server when you install your driver, if you did not take this step you might want to try that. Saya benar-benar menikmati setiap sedikit itu.
Saya memiliki Anda buku ditandai untuk memeriksa baru hal yang Anda posting …. I have major in machine learning but not much experience with deep learning so it is unclear to me — whether it is possible to run one deep learning algorithm with a multiples GPUs attached to single CPU and benefits the speed up.
For example, will a machine with 2 titan X be 2x faster than the one with only 1 titan X, is any bottleneck? Will you recommend buying 4 gtx over 2 titan X pascal more memory,nearly same price? Or even 3 gtx over 2 titan X same amount of memory, cheaper.
Is it possible with single CPU? Will two different cards be able to run two different algorithms on a single machine? The 2x Titan vs 4x GTX issue is not straightforward; some algorithms will only run on the Titans not on s.
This depends highly on the application. I recommend to start out with a single GPU and upgrade later. You could get a GTX to get started and figure out if you need more memory or not for your research applications.
Tim this blog is amazing. Thanks for the amazing info. Does sli mode trick software into thinking there is only one GPU? If so will that cause huge speed up? For convolutional nets, especially ones without fully connected layers, 2 GPUs will be nearly twice as fast as 1 GPU 1a.
SLI is a concept that is only relevant for visual display but not for compute. Your blog is awesome for me and I learned quite a lot. Here are 2 options for system choices: The 8 GPU system will be faster, but much harder to use. This can be done without much loss of performance with certain parallelization algorithms, but most frameworks do not offer this solution CNTK being the exception.
I personally would got for the 2 node 4 GPU option, simply because existing software will be easier to run. Although only CNTK and Tensorflow officially supports across node computation, so the goal of parallelizing across 8 GPUs should only be a goal if you really need that speed. For most application 4 GPUs will be rather okay.
I want to make sure about my conclusion from the blog about building a multi-node multi gpu system for deep learning on windows platform: I want to make sure about the mandatory requirement of infiniband.. Is it mandatory or just to provide higher data transfer rates? Yes, bandwidth is the main issue. Another thing might be latency issues; these might arise for Ethernet and destroy performance. I would not rely on Ethernet if I want to do parallelization for deep learning. It is just a big, big hassle, is expensive, you need special software, and the speedup is not that great in the end.
If you still want to go for it, definitely get Infiniband cards. Thanks for your comment and blog post. The new motherboard looks nice and shiny, but I am not really convinced. I am not a big fan of DDR4 boards. It adds almost no improvement in deep learning performance and you have an expensive motherboards, expensive RAM, expensive CPUs which often lack full lane support. Thanks for your input!
Do you know to what extent? You can achieve such behavior by using model parallelism, that is splitting your model among many GPUs, but this is usually not supported by software. Thanks very much for your very detailed posts.
I came here after reading your usefully super-detailed post about which GPUs to use for deep learning. Is one of those optimized for deep learning more than others? Sorry for such a naive question, and thanks again! The brands often add special features, but essentially they all work with the same microchip and all have the same performance. So you can buy any GPU from any of the brands, and practically, I would just go with the cheapest one.
That depends if the other device supports GPU Direct which it probably does not. If it does not support GPU Direct out-of-the-box, one might be able to get it working with some hacks to the drivers since both systems use DMA if the capture card is a PCIe device to transfer data and this should be controllable in some places in the capture card drivers. But such a hack would not be straightforward. Probably another solution will require less time and might be sufficient too.
PyTorch has made good efforts recently to do better, but I would still say that their solution is not really clean. Kind of disheartening to see no good solution to this problem for such a long time. I tried building automatic multi-GPU libraries myself, but it is just to much work to do as a single individual.
Such a cluster must have the right interconnects FDR Infiniband or better to be relevant. Note that working in paralellism is very difficult engineering work with little payoff. People think this work is important, but there are only very few people how are interested in this and with whom you can discuss work so that it can be a very isolating experience especially at conferences.
If you want to do this despite all of this I recommend you to study block momentum and 1-bit gradient descent. These are by far the best methods our there. You should understand why any further research on 1-bit-gradient-descent-like methods is a deadend one can use methods like this on top of it, but it does not get better from there. The real research is improving on block momentum. However, this is not straightforward and block momentum has poor theoretical motivations.
It is an algorithm that should not work and yet it is the best parallel algorithm for deep learning. Your PhD could revolve around why this is and how to improve with these insights. Again this is a difficult topic which requires lots of engineering. It will be easy to publish, but you will not be cited much and it will be difficult to build an academic career on this work. If you want to continue this work in industry though you will be an extremely valuable asset and should be easily getting jobs at companies that do deep learning on GPU clusters.
If you are interested in deep learning in general though it will be very difficult to find a job for that. In your work you would speed up other peopls algorithms, but would not use them yourself in experiments other than benchmarking.
Hope that helps to set some expectations. If you have more questions, feel free to write me an email. Hi Tim, thanks for the article. I have a question regarding using multiple gpus which are different models in one node. So this would not work out in the first place. Thank you Tim, for your reply. But, this thread says that tensorflow supports multi-gpu of different of types. Will the performance be same? What would be the limit of such a configuration in terms of deep learning workloads that can be efficiently handled?
Can you please tell me if I plan to have 2 ti gpus in single computer, will I have to do something to enable p2p access between them?
I am little confused because there are other articles saying p2p is only for tesla and quadro gpus. Having read this I believed it was really informative. I appreciate you spending some time and effort to put this information together. I once again find myself personally spending a lot of time both reading and leaving comments. But so what, it was still worthwhile!
I am considering experimenting with re-purposing an altcoin mining rig for Machine Learning and wanted to know what would be required and how it would compare to, for example, an expensive Tesla P system.
Current specs on Rig: Are there features of this rig that are holding it back from ML performance? Should additional hardware be added or replaced to easily convert? Could I also parallel multiple rigs to form a large ML cluster? This rid looks like a perfect rig for ML.
With this system, you will probably not be able to parallelize all GPUs, but you should be fine to use them in for a neural network each. Would this implementation increase my performance over having four different host computers each with a single GPGPU? SLI will not work for compute, it is for gaming only. You need to use data parallelization algorithms if you want to achieve the same effect. Many frameworks, including PyTorch supports this feature.
This is also true for P2P. All that is needed for DMA is the physical memory address of both the src and dst. Never having to double-copy to host memory GPU memory throughput is also much faster. Few users work this such systems in that way, so its difficult to support them, so they want to save troubles with support requests by just saying it does not work — period.
I think this might be going on here. I have the same suspicion — It must be possible, but for some reason, not supported at this time. I even though about using some daemon, running on Ubuntu which would be running inside a Hyper-V on top of the Win I would use some sort of event or interrupt to trigger the daemon to perform a deviceToDevice memcpy. At any rate, gotta keep on trekking in spite of the bumps in the road; this is part of what engineers do.
Your email address will not be published. Notify me of follow-up comments by email. Notify me of new posts by email. Important components in a GPU cluster When I did my research on which hardware to buy I soon realized, that the main bottleneck will be the network bandwidth, i.
Some sample MPI code. The first action spreads one chuck of data to all others computer in the network; the second action receives one chuck of data from every process. That is all you need to do, it is very easy! Hope this helps — good luck with your system!
Secondly, the SGD block in the config file should contain a sub-block named ParallelTrain with the following arguments:. This specifies starting from which epoch, parallel training algorithms are used; before that all the workers doing the same training, but only one worker is allowed to save the model. This option can be useful if parallel training requires some "warm-start" stage. This specifies how frequently the performance statistics will be printed out.
Other values specifies how often the statistics will be printed out. A sub-block which specifies details of each parallel training algorithm. The name of the sub-block should equal to parallelizationMethod. Python provides more flexibility and usages are shown below for different parallelization methods. Given any of the parallel-training BrainScript configurations above, the following commands can be used to start a parallel MPI job:.
This technique allows to distribute each minibatch over K workers. The resulting partial gradients are then exchanged and aggregated after each minibatch. Directly exchanging partial gradients after each minibatch requires prohibitive communication bandwidth. To address this, 1-bit SGD aggressively quantizes each gradient value Practically, this means that large gradient values are clipped, while small values are artificially inflated. Amazingly, this does not harm convergence if, and only if, a trick is used.
The trick is that for each minibatch, the algorithm compares the quantized gradients that are exchanged between workers with the original gradient values that were supposed to be exchanged.
The difference between the two the quantization error is computed and remembered as the residual. This residual is then added to the next minibatch. As a consequence, despite the aggressive quantization, each gradient value is eventually exchanged with full accuracy; just at a delay.
Experiments show that, as long as this model is combined with a warm start a seed model trained on a small subset of the training data without parallelization , this technique has shown to lead to no or very small loss of accuracy, while allowing a speed-up not too far from linear the limiting factor being that GPUs become inefficient when computing on too small sub-batches. For maximum efficiency, the technique should be combined with automatic minibatch scaling , where every now and then, the trainer tries to increase the minibatch size.
Evaluating on a small subset of the upcoming epoch of data, the trainer will select the largest minibatch size that does not harm convergence. Here, it comes in handy that CNTK specifies the learning rate and momentum hyperparameters in a minibatch-size agnostic way.
In addition, automatic minibatch-scaling should be enabled. These are configured by adding the following parameters to the SGD block:. However, in typical scenarios, especially scenarios in which each model parameter is applied only once like for a feed-forward DNN, this will not be efficient due to high communication-bandwidth needs. Both methods have no or nearly no loss of accuracy at near-linear speed-up. There is no need for warm start in this case.
This is similar to the syncPeriod in ModelAveragingSGD , which specifies how frequent a model synchronization is performed. This means after every synchronization point, the smoothed gradient used in local SGD will be set as 0. The default value of this variable is true.
This means the Nesterov-style momentum update is applied on the block level. See  for more details. The block momentum and block learning rate are usually automatically set according to the number of workers used, i. Our experience indicates that these settings often yield similar convergence as the standard SGD algorithm up to 64 GPUs, which is the largest experiment we performed. It is also possible to manually specify the these parameters using the following options:. It is calculated as:.
To achieve similar throughput per worker, it is necessary to increase the number of samples in a minibatch proportional to the number of workers. This can be achieved by adjusting minibatchSize or nbruttsineachrecurrentiter , depending on whether frame-mode randomization is used. On our speech recognition tasks, reasonable convergence is achieved when starting from seed models trained on 24 hours 8.
At the same time the maximum speed-up factor is relatively robust. It is recommended to set resetSGDMomentum to true; otherwise it often leads to divergence of training criterion. Resetting SGD momentum to 0 after every model synchronization essentially cuts off the contribution from the last minibatches. Therefore, it is recommended not to use a large SGD momentum. For example, for a syncPeriod of ,, we observe a significant accuracy loss if the momentum used for SGD is 0.
Reducing the SGD momentum to 0. Block-Momentum SGD delays and distributes model updates from one block across subsequent blocks. Therefore, it is necessary to make sure that model synchronizations is performed often enough in the training. A quick check is to use blockMomentumAsTimeConstant. It is recommended that the number of unique training samples, N , should satisfy the following equation:. The approximation stems from the following facts: To enable Block-Momentum in Python, similarly to the 1-bit SGD, the user needs to create and pass a block momentum distributed learner to the trainer:.
Model-Averaging SGD is an implementation of the model averaging algorithm detailed in [3,4] without the use of natural gradient. The idea here is to let each worker processes a subset of data, but averaging the model parameters from each worker after a specified period. To make Model-Averaging SGD maximally effective and efficient, users need to tune some hyper-parameters:. Therefore, to make sure each worker produces the same throughput as the standard SGD, it is necessary to enlarge the minibatch size n -fold.
For models that are trained using frame-mode randomization, this can be achieved by enlarging minibatchSize by n times; for models are trained using sequence-mode randomization, such as RNNs, some readers require to instead increase nbruttsineachrecurrentiter by n.