At first, let me explain what i have to do.
My develop enviroment is Tizen OS. may be you are unfamilier that, anyway this os is using linux kernel based redhat and targeting on mobile, tv, etc.. And my target device is consists of exynos 5422 and arm mali-t628.
My main work is implement some gpu library to let tensorflow lite's operation can use the library.
I proceeded to build and install tensorflow lite as a rpm package file.
I am googling many times about the tensorflow and gpu. and get some useless information about cuda. i didnt see any info for my case(tizen and mali gpu).
i think linux have gpu instruction like the cpu or library.. but i cant find them.
can you suggest search keyword or document?
You can go to nvidia’s cuda toolkit page, where you can find the documentation and
Training buttons / options.
Also there’s the CUDA programming guide wich i myself find very usefull and helpull for CUDA.
I believe that one or two of those may help you.
CUDA is for NVidia GPU. Mali is not NVidia's, but ARM's. So you CANNOT use CUDA in your given hardware. Besides, if you want CUDA, you'd better drop Tensorflow-lite and use Tensorflow.
If you want to use CUDA, get a hardware with supported NVidia GPU (e.g., x64 machine with NVidia GPU). Note that you can use Tensorflow-GPU & CUDA/CUDNN in Tizen with x64+NVidia GPU. You just need to be careful on nvidia GPU kernel driver version and userspace driver version. Because NVidia's GPU userspace driver and CUDA/CUDNN are statically built, its Linux drivers are compatible with Tizen. (I've tested tensorflow-gpu, CUDA/CUDNN in Tizen with NVidia driver version 111... probably in winter, 2017)
If you want to use Tizen/Tensorflow-lite in the given hardware, forget CUDA.
Related
How to set up different versions of CUDA in one OS?
Here is my problem: Lastest Tensorflow with GPU support requires CUDA 11.2, whereas Pytorch works with 11.3. So what is the solution to install both libraries in Windows and Ubuntu?
One solution is to use Docker Container Environment, which would only need the Nvidia Driver to be of version XYZ.AB; in this way, you can use both PyTorch and TensorFlow versions.
A very good starting point for your problem would be this one(ML-WORKSPACE) : https://github.com/ml-tooling/ml-workspace
I have AMD graphics card with me. Now, when I try to import tensorflow, I get the following error.
I think (as I understand) that if I use normal TF and not TF-GPU, then this issue may resolve. However, I do want to use the graphics card because my reinforcement learning algorithms are really very slow otherwise. Is there any workaround?
ImportError: Could not find 'nvcuda.dll'. TensorFlow requires that
this DLL be installed in a directory that is named in your %PATH%
environment variable. Typically it is installed in
'C:\Windows\System32'. If it is not present, ensure that you have a
CUDA-capable GPU with the correct driver installed.
Currently, right now with AMD, there are two ways you can go about it.
Either using the lastest AMD's ROCm to install tensorflow.
official ROCm install
and
official ROCm tensorflow install
check if you use the supported AMD GPU check it over here
or using the OpenCL implementation of TensorFlow if your video card does not support ROCm
https://github.com/benoitsteiner/tensorflow-opencl
or
https://github.com/hughperkins/tf-coriander
According to this Vulkan tutorial, I can use vkEnumeratePhysicalDevices to get a list of available GPUs. However, I don't see my external NVIDIA GPU in there, only my Intel iGPU.
This eGPU is connected via Thunderbolt and is running CUDA code just fine. Is there anything I might have missed? Is it supposed to work out of the box?
My machine is running Arch Linux with up-to-date proprietary NVIDIA drivers.
The eGPU is a NVIDIA GTX 1050 (Lenovo Graphics Dock). Is it possible that it just does not support Vulkan somehow?
Vulkan support should work just as well with external GPUs (eGPUs). Seeing the eGPU enumerated as a Vulkan device may require the eGPU to be recoznized by Xorg (or Wayland in the future).
See recently created https://wiki.archlinux.org/title/External_GPU#Xorg for changes probably required in Xorg config.
My MacBook Pro doesn't have a NVIDIA gpu. So it's not possible to run CUDA. I'm wondering which of the earlier versions of TensorFlow have gpu support for Mac OS? And how can I install on Anaconda?
As stated on the official site:
Note: As of version 1.2, TensorFlow no longer provides GPU support on
Mac OS X.
..so installing any earlier version should be fine. But since your hardware does not have NVIDIA graphics card with CUDA support, it doesn't matter anyway.
In terms of installing TensorFlow on Mac OSX using Anaconda, you can just follow steps nicely described in the official docs
TensorFlow relies on CUDA for GPU use so you need Nvidia GPU. There's experimental work on adding OpenCL support to TensorFlow, but it's not supported on MacOS.
On anecdotal note, I've heard bad things from people trying to use AMD cards for deep learning. Basically AMD doesn't care about deep learning, they change their interfaces without notice so things break or run slower than CPU.
From TensorFlow Download and Setup under
Docker installation I see:
b.gcr.io/tensorflow/tensorflow latest 4ac133eed955 653.1 MB
b.gcr.io/tensorflow/tensorflow latest-devel 6a90f0a0e005 2.111 GB
b.gcr.io/tensorflow/tensorflow-full latest edc3d721078b 2.284 GB
I know 2. & 3. are with source code and I am using 2. for now.
What is the difference between 2. & 3. ?
Which one is recommended for "normal" use?
TLDR:
First of all - thanks for Docker images! They are the easiest and cleanest way to start with TF.
Few aside things about images
there is no PIL
there is no nano (but there is vi) and apt-get cannot find it. yes i probable can configure repos for it, but why not out of the box
There are four images:
b.gcr.io/tensorflow/tensorflow: TensorFlow CPU binary image.
b.gcr.io/tensorflow/tensorflow:latest-devel: CPU Binary image plus source code.
b.gcr.io/tensorflow/tensorflow:latest-gpu: TensorFlow GPU binary image.
gcr.io/tensorflow/tensorflow:latest-devel-gpu: GPU Binary image plus source code.
And the two properties of concern are:
1. CPU or GPU
2. no source or plus source
CPU or GPU: CPU
For a first time user it is highly recommended to avoid the GPU version as they can be any where from difficult to impossible to use. The reason is that not all machines have an NVidia graphic chip that meet the requirements. You should first get TensorFlow working to understand it then move onto using the GPU version if you want/need.
From TensorFlow Build Instructions
Optional: Install CUDA (GPUs on Linux)
In order to build or run TensorFlow with GPU support, both Cuda
Toolkit 7.0 and CUDNN 6.5 V2 from NVIDIA need to be installed.
TensorFlow GPU support requires having a GPU card with
NVidia Compute Capability >= 3.5. Supported cards include but are not limited to:
NVidia Titan
NVidia Titan X
NVidia K20
NVidia K40
no source or plus source: no source
The docker images will work without needing the source. You should only want or need the source if you need to rebuild TensorFlow for some reason such as adding a new OP.
The standard recommendation for someone new to using TensorFlow is to start with the CPU version without the source.