I'm trying to use my laptop RTX 3070 GPU for CNN model training because I have to employ a exhastive grid search to tune the hyper parameters. I tried many different methods however, I could not get it done. Can anyone kindly point me in the right direction?
I followed the following procedure.
The procedure:
Installed the NVIDIA CUDA Toolkit 11.2
Installed NVIDIA cuDNN 8.1 by downloading and pasting the files (bin,include,lib) into the NVIDIA GPU Computing Toolkit/CUDA/V11.2
Setup the environment variable by including the path in the system path for both bin and libnvvm.
Installed tensorflow 2.11 and python 3.8 in a new conda environment.
However, I was unable to setup the system to use the GPU that is available. The code seems to be only using the CPU and when I query the following request I get the below output.
query:
import tensorflow as tf
print("TensorFlow version:", tf.__version__)
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
Output:
TensorFlow version: 2.11.0
Num GPUs Available: 0
Am I missing something here or anyone has the same issue like me?
You should use DirectML plugin. From tensorflow 2.11 Gpu support has been dropped for native windows. you need to use DirectML plugin.
You can follow the tutorial here to install
Related
I'm using Anaconda prompt to install:
Tensorflow 2.10.0
cudatoolkit 11.3.1
cudnn 8.2.1
I'm using Windows 11 and a RTX 3070 Nvidia graphic card. And all the drives have been updated.
And I tried downloading another version of CUDA and CUdnn in exe.file directly from CUDA website. And
added the directories into system path.The folder looks like this:
But whenever I type in:
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
It gives me Num GPUs Available: 0
And surely it eats my CPU for computing everything.
It once succeeded when I used Colab and use GPU as the accelerator then created a session using my GPU. That time the GPU load has been maximized. But later don't know how, I can't use my own GPU for training in Colab or even their default free GPU.
Please help. ChatGPT doesn't give me correct information since it only referred to knowledge before 2020. It keeps asking me to install 'tensorflow-gpu' which has already been removed.
Has anyone been able to make tensorflow detect the GPU using python 3.7?
How did you do it? I've downloaded cuDNN 8.1, CUDA 11.2, then pip installed tensorflow using pip install tensorflow-gpu==2.5 I've added another environment variable for cuDNN's bin, however I am still getting this result Num GPUs Available 0. Does Tensorflow (CUDA 11.2) even work with the AMD Radeon Vega 8?
No it does not, because cuDNN is a product of NVIDIA and so is CUDA. NVIDIA designs their own GPUs and their product will look for those GPUs. In order for tensorflow to detect the GPU you will have to use one of NVIDIA's GPU.
I installed the tensorflow-gpu version, and tried to test the GPU setup as suggested
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
However, I got the following information
Num GPUs Available: 0
My machine does have GPU card, shown as follows, why it is not picked by Tensorflow
It is likely that you do not have the right combination of the following:
CUDA
CuDNN
TensorFlow
Please check my answer here, for a correct combination of the aforementioned : Tensorflow 2.0 can't use GPU, something wrong in cuDNN? :Failed to get convolution algorithm. This is probably because cuDNN failed to initialize
I'm running the UNet Keras model on a GCP instance with one NVIDIA Tesla P4GPU. But it does not detect the GPU. Instead it runs on the CPU. p.s. I installed drivers & tensorflow-gpu buy it wont work. How to fix this issue?
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (instance-1): /proc/driver/nvidia/version does not exist
Num GPUs Available: 0
You need to first install the driver. Follow this instruction
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.