I'm using a laptop which has Intel Corporation HD Graphics 520.
Does anyone know how to it set up for Deep Learning, specifically Pytorch? I have seen if you have Nvidia graphics I can install cuda but what to do when you have intel GPU?
PyTorch doesn't support anything other than NVIDIA CUDA and lately AMD Rocm.
Intels support for Pytorch that were given in the other answers is exclusive to xeon line of processors and its not that scalable either with regards to GPUs.
Intel's oneAPI formerly known ad oneDNN however, has support for a wide range of hardwares including intel's integrated graphics but at the moment, the full support is not yet implemented in PyTorch as of 10/29/2020 or PyTorch 1.7.
But you still have other options. for inference you have couple of options.
DirectML is one of them. basically you convert your model into onnx, and then use directml provider to run your model on gpu (which in our case will use DirectX12 and works only on Windows for now!)
Your other Option is to use OpenVino and TVM both of which support multi platforms including Linux, Windows, Mac, etc.
OpenVino and TVM use ONNX models so you need to first convert your model to onnx format and then use them.
Lately(as of 2023),IREE (Intermediate Representation Execution Environment) (torch-mlir in this case) can be used as well.
Intel provides optimized libraries for Deep and Machine Learning if you are using one of their later processors. A starting point would be this post, which is about getting started with Intel optimization of PyTorch. They provide more information about this in their AI workshops.
Related
I have a very simple Toshiba Laptop with i3 processor. Also, I do not have any expensive graphics card. In the display settings, I see Intel(HD) Graphics as display adapter. I am planning to learn some cuda programming. But, I am not sure, if I can do that on my laptop as it does not have any nvidia's cuda enabled GPU.
In fact, I doubt, if I even have a GPU o_o
So, I would appreciate if someone can tell me if I can do CUDA programming with the current configuration and if possible also let me know what does Intel(HD) Graphics mean?
At the present time, Intel graphics chips do not support CUDA. It is possible that, in the nearest future, these chips will support OpenCL (which is a standard that is very similar to CUDA), but this is not guaranteed and their current drivers do not support OpenCL either. (There is an Intel OpenCL SDK available, but, at the present time, it does not give you access to the GPU.)
Newest Intel processors (Sandy Bridge) have a GPU integrated into the CPU core. Your processor may be a previous-generation version, in which case "Intel(HD) graphics" is an independent chip.
Portland group have a commercial product called CUDA x86, it is hybrid compiler which creates CUDA C/ C++ code which can either run on GPU or use SIMD on CPU, this is done fully automated without any intervention for the developer. Hope this helps.
Link: http://www.pgroup.com/products/pgiworkstation.htm
If you're interested in learning a language which supports massive parallelism better go for OpenCL since you don't have an NVIDIA GPU. You can run OpenCL on Intel CPUs, but at best you can learn to program SIMDs.
Optimization on CPU and GPU are different. I really don't think you can use Intel card for GPGPU.
Intel HD Graphics is usually the on-CPU graphics chip in newer Core i3/i5/i7 processors.
As far as I know it doesn't support CUDA (which is a proprietary NVidia technology), but OpenCL is supported by NVidia, ATi and Intel.
in 2020 ZLUDA was created which provides CUDA API for Intel GPUs. It is not production ready yet though.
I am currently working on an ML project on my personal computer that has an AMD graphics card. I have an old NVidia 8800GT card that I could plug in for CUDA accelerated convolution, but I haven't found if it is compatible with Theano. Googling has surprisingly been unsuccessful.
I know the 8800GT supports CUDA and I've done some CUDA work with it in the past, but is compatible with Theano? (or TensorFlow?)
Best,
Joe
Theano has no specific requirements for cards other than "it works with cuda".
If you want to use the cuDNN layers or other specilized things, then you might need a more recent card and the requirements for those is specified in the documentation for those libraries.
I'd like to use pylearn2, theano and scikit-neuralnetwork to build neural network models. But my friend told me that all this module can only run on NVIDIA GPU based platform (because they would import the pycuda module). But I only have an AMD GPU(R9 270,and an AMD FX-8300 CPU),and I wish to take advantage of AMD GPU to speed up computing. Can I use all of the modules metioned above? Or is there any substitutes I can use to build neural network models ? Thanks!
Currently, Theano only supports nvidia GPUs. There is a partial implementation of an OpenCL backend that would support AMD GPUs but it is incomplete and unsupported.
scikit-neuralnetwork builds on PyLearn2 and PyLearn2 builds on Theano so none of those packages can operate on AMD GPUs.
Torch appears to already have some OpenCL support. Caffe's OpenCL support appears to be under development.
I want to learn GPGPU and CUDA programming. But I know that only Nvidia card support it. My laptop has an Intel HD Graphic Card. So I need to search if it is possible to do GPGPU or something like that with Intel graphic card. Thanks for any information.
To develop in CUDA your options are:
Use an NVIDIA GPU - all NVIDIA server, desktop and laptop GPUs support CUDA since around 2006, since your laptop does not have one you could try using one remotely.
Use PGI CUDA x86, not free but does what you want.
Use gpuocelot to execute the PTX on the CPU, that's an open-source project in development so YMMV.
You cannot do GPGPU on Intel HD Graphics cards today, unless you do shader-based programming (which was common practice in the days before CUDA and OpenCL).
In my experience, the PGI X86 stuff seems to have fallen flat and I'm not aware of anyone using that. Ocelot is another attempt at the same, but it is very reasearchy and not fully robust at this point.
The only OpenCL compliant devices from Intel are the latest CPUs (Sandy Bridge and Ivy Bridge).
What CPU do you have in your system?
CUDA is Nvidia specific as starter. The GPU emulators are always there in CUDA, so you can use them without a graphics card easily, though it will be slow. A faster solution is the
the x86 implementation. Any of these will allow you to learn the basics of CUDA without using the GPU at all.
If you are want to learn GPGPU in general you still have the option to learn OpenCL, which more widely supported, including AMD, Intel, Nvidia etc... E.g. Intel has an OpenCL SDK (the target is the CPU then, but I guess is irrelevant for you).
After learning the basics of either CUDA or OpenCL, the other will be easy to learn. Neither the syntax nor the semantics are the same, but it is easy step forward as the concepts are the same.
I'm taking a course that teaches CUDA. I would like to use it my personal laptop, but I don't have Nvidia graphics processor. mine is ATI . so I was thinking is there any Virtual Hardware simulator that I can use? or that there is no other way than using a PC with CUDA Graphics processor.
Thank you very much
The CUDA toolkit used to ship with a host CPU emulation mode, but that was deprecated early in the 3.0 release cycle and has been fully removed from toolkits for the best part of two years.
Your only real option today is to use Ocelot. It has a PTX assembly translator and a pretty reliable reimplementation of the CUDA runtime for x86 CPUs, and there is also a rather experimental PTX to AMD IL translator (I have no experience with the latter). On a modern linux system with an up to date GNU toolchain, Ocelot is reasonably easy to get running. I am not sure if there is a functioning Windows port or not.
CUDA has its own emulation mode witch runs everything on CPU. Problem is that in such case you don't have real concurrency so programs that runs successfully in emulation mode can fail (and usually does) in normal mode. You can develop your code in emulation mode, but then you have to debug it on computer with CUDA card.