I am trying to use tensorflow with gpu and installed CUDA 8.0 toolkit and cuDNn v5.1 libraries as described in nvidia website. But when I try to import tensorflow as module in python3.5, it does not load cuDNn libraries (outputs nothing, just loads tensorflow module). And I do not observe speed in processing (same speed I obtained when I use CPU) with GPU.
Fresh install is the key but there are some important points:
1. Install CUDA 8.0 toolkit
2. Install cuDNn 5.1 version (not 6.0)
3. Install from source(bazel) and configure to use ensorflow with CUDA
Above steps worked for me! Hope it helps anyone.
Related
Hi I'm struggling to get Tensorflow V2.11 to find my eGPU (RTX 3060 Ti)
I am currently on Windows 11
CUDA version is 12
I am currently downloading CUDA 11 as well as CUDnn as I've heard it is recommended
I have tried the following code:
import tensorflow as tf
tf.config.list_physical_devices('GPU')
which outputs:
[]
any help would be great
Tensorflow 2.11 is not supporting GPU on Windows machine. TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. So you can try by installing Tensorflow 2.10 for the GPU setup.
Also you need to install the specific version of CUDA and cuDNN for GPU support in your system which is CUDA 11.2 and cuDNN 8.1 for Tensorflow 2.10(Tensorflow>=2.5).
Please check the Hardware/Software requirements as mentioned in the link and set the path to the bin directory after installing these software.
Now follow the step by step instructions mentioned in the same link and verify the GPU setup using below code.
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
I have encounter "I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8400
Could not load library cudnn_cnn_infer64_8.dll. Error code 193"
will working with TensorFlow.
version:
TensorFlow 2.8
CUDA 11.6
CUDNN 8.4
The versions you installed for TensorFlow and NVIDIA CUDA probably don't match.
Try using one of the versions tested here: Tensorflow GPU Source Install
Don't forget to install "tensorflow-gpu" module instead of "tensorflow" in order to use NVIDIA GPU Acceleration.
i simply used conda insted pip to install cuda and cudnn. then used pip for the tensorflow gpu instalation. vrsions that worked with each other are cuda 11.2 and tensorflow2.10. anything above 2.10 not suport gpu
I am trying to use keras in tensorflow to train a CNN network for some image classification. Obviously, the training running on my CPU is incredibly slow and so I need to use my GPU to do the training. I've found many similar questions on StackOverflow, none of which have helped me get the GPU to work, hence I am asking this question separately.
I've got an NVIDIA GeForce GTX 1060 3GB and the 466.47 NVIDIA driver installed. I've installed the CUDA toolkit from the NVIDIA website (installation is confirmed with nvcc -V command outputting my version 11.3), and downloaded the CUDNN library. I unzipped the CUDNN file and copied the files to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.3, as stated on the NVIDIA website. Finally, I've checked that it's on PATH (C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.3\bin and C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.3\libnvvp are both in the environment variable 'Path').
I then set up an environment using conda, downloading some packages that I need, like scikit-learn, as well as tensorflow-gpu=2.3 After booting my environment into Jupyter Notebook, I run this code to check to see if it's picking up the GPU:
import tensorflow as tf
print(tf.__version__)
print(tf.config.list_physical_devices())
And get this:
2.3.0
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]
I have tried literally everything I have come into contact with on this topic, but am not getting any success in getting it to work. Any help would be appreciated.
You, first, have to install all CUDA requirements. If you have Ubuntu 20.04, here is how you can install the requirements. Then it's the right time to install tensorflow. Asa you intended to utilize your GPU, you have install tensorflow-gpu library, not tensorflow alone.
I'm guessing you have installed TensorFlow correctly using pip install tensorflow.
NVIDIA GPU cards with CUDA architectures 3.5, 5.0, 6.0, 7.0, 7.5, 8.0 and higher than 8.0 are currently supported by TensorFlow. If you have the supported cards but TensorFlow cannot detect your GPU, you have to install the following software:
NVIDIA GPU drivers —CUDA 11.0 requires 450.x or higher.
CUDA Toolkit —TensorFlow supports CUDA 11 (TensorFlow >= 2.4.0)
cuDNN SDK 8.0.4
You can optionally install TensorRT 6.0 to improve latency and throughput for inference on some models.
For more info, please refer to the TensorFlow documentation: https://www.tensorflow.org/install/gpu
I recommend to use conda to install the CUDA Toolkit packages as well as CUDNN, which will avoid wasting time downloading the right packages (or making changes in the system folders)
conda install -c conda-forge cudatoolkit=11.0 cudnn=8.1
Then you can install keras and tensorflow-gpu by typing
conda install keras==2.7
pip install tensorflow-gpu==2.7
and it will work directly.
Based on this issue
I started learning about the tensorflow recently and decided to switch to the GPU version, because it is much faster, but I can not import it, it always gives the same error.
I already tried:
Installing it by pip, in python 3.6.8, cuda 10 and the most recent cuDNN for cuda 10
I tried reinstalling python, CUDA and cuDNN
Tried installing Visual Studio and installed CUDA 9 and cuDnn
I tried installing the latest Anaconda, created a "default" env and another in python 3.6 (also tried in 3.5), pip install tensorflow-gpu in both cases
my last attempt was to follow a tutorial on youtube, I did exactly as demonstrated (https://www.youtube.com/watch?v=KZFn0dvPZUQ)
Everything i tried returned the same error.
Traceback: https://pastebin.com/KMEsZAmq
The complete code: https://pastebin.com/7tS0Rd5S (was working on CPU version)
.
My Specs:
i5-8400
8 GB Ram
GTX 1060 6GB
W10 home x64
just have a look here:
https://www.tensorflow.org/install/gpu
Tensorflow supports CUDA 9.0, you will need to downgrade your CUDA or use one of the tensorflow's docker images:
https://www.tensorflow.org/install/docker
via docker it won't use your CUDA drivers
trying to install tensorflow gpu on windows 10 since three days.
https://www.tensorflow.org/install/install_windows#requirements_to_run_tensorflow_with_gpu_support
says :
If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system:
CUDA® Toolkit 9.0. For details, see NVIDIA's documentation Ensure that you append the relevant Cuda pathnames to the %PATH% environment variable as described in the NVIDIA documentation.
The NVIDIA drivers associated with CUDA Toolkit 9.0.
cuDNN v6.0. For details, see NVIDIA's documentation. Note that cuDNN is typically installed in a different location from the other CUDA DLLs. Ensure that you add the directory where you installed the cuDNN DLL to your %PATH% environment variable.
GPU card with CUDA Compute Capability 3.0 or higher. See NVIDIA documentation for a list of supported GPU cards.
I downloaded cuda toolkit 9.0 from archives.
but there is no cudnn 6.0 for cuda 9.0 here : https://developer.nvidia.com/rdp/cudnn-download
It's driving me mad, as only thing available there is cudnn v7.
Please help me.
Apparently I cant comment... but I am having this exact same issue! Tensorflow has conflicting requirements for install. Cuda Tookit V8.0 is the last supported version for cudnn V6.0
For everyone who comes to this thread with issues on cudNN or cudart errors, here's a few notes:
Tensorflow documentation may or may not be updated quickly enough after a new release.
Tensorflow can be compiled (built) from scratch, which allows you to decide what CUDA and cuDNN version to use, so if you are using a pre-compiled binary, you will need the version of CUDA and cuDNN it was built for.
You need to have cuDNN in the path.
Tensorflow's documentation for installing a binary will always specify the version of CUDA and cuDNN it needs.
If things don't work, try running a simple hello world tensorflow program and read the errors to know what version of CUDA / cuDNN to use.
For example, a missing cudart64_81.dll needs the 64 bit version of CUDA 8.1.
A missing cudnn64_6.dll needs cuDNN 6.0
CUDA can be downloaded from: https://developer.nvidia.com/cuda-toolkit-archive
cuDNN can be downloaded from: https://developer.nvidia.com/rdp/cudnn-archive