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
Related
According to this link: //pypi.org/project/tensorflow-gpu/ , the "tensorflow-gpu" package is no longer supported and users should instead use the "tensorflow" package, which supposedly supports the GPU.
However after, installing the tensorflow 2.11 package, it will not even detect my GPU device. It only runs on the CPU. How does one use the GPU with Tensorflow 2.11?
It appears that Tensorflow 2.10 is the last version to support the GPU on windows: https://discuss.tensorflow.org/t/2-10-last-version-to-support-native-windows-gpu/12404
I make a Project using TensorFlow Object detection.
Windows might have encountered an error because of the GPU module version. Linux is trying again now.
If you look at some review posts, it seems that GPU version has not been used. Is that right?
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.
I have installed CUDA and cuDNN, but the last was not working, giving a lot of error messages in theano. Now I am training moderate sized deep conv nets in Keras/Tensorflow, without getting any cuDNN error messages. How can I check if cuDNN is now being used?
tl;dr: If tensorflow-gpu works, then CuDNN is used.
The prebuilt binaries of TensorFlow (at least since version 1.3) link to the CuDNN library. If CuDNN is missing, an error message will tell you ImportError: Could not find 'cudnn64_7.dll'. TensorFlow requires that this DLL be installed....
According to the TensorFlow install documentation for version 1.5, CuDNN must be installed for GPU support even if you build it from source. There are still a lot of fallbacks in the TensorFlow code for the case of CuDNN not being available -- as far as I can tell it used to be optional in prior versions.
Here are two lines from the TensorFlow source that explicitly tell and force that CuDNN is required for gpu acceleration.
There is a special GPU version of TensorFlow that needs to be installed in order to use the GPU (and CuDNN). Make sure the installed python package is tensorflow-gpu and not just tensorflow.
You can list the packages containing "tensorflow" with conda list tensorflow (or just pip list, if you do not use anaconda), but make sure you have the right environment activated.
When you run your scripts with GPU support, they will start like this:
Using TensorFlow backend.
2018- ... C:\tf_jenkins\...\gpu\gpu_device.cc:1105] Found device 0 with properties:
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7845
To test it, just type into the console:
import tensorflow as tf
tf.Session()
To check if you "see" the CuDNN from your python environment and therewith validate a correct PATH variable, you can try this:
import ctypes
ctypes.WinDLL("cudnn64_7.dll") # use the file name of your cudnn version here.
You might also want to look into the GPU optimized Keras Layers.
CuDNNLSTM
CuDNNGRU
They are significantly faster:
https://keras.io/layers/recurrent/#cudnnlstm
We saw a 10x improvement going from the LSTM to CuDNNLSTM Keras layers.
Note:
We also saw a 10x increase in VMS (virtual memory) usage on the machine. So there are tradeoffs to consider.
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.