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.
please solve this error i am doing FYP
First of all, my computer does not have an Nvidia card. So I can not install CUDA driver. I downloaded nvcuda.dll and executed
regsvr32 C:\Windows\System32\nvcuda.dll
instruction, they make a fire so as to compile all TensorFlow code that note
ImportError: Could not find 'nvcuda.dll'.
Anyway, please reinstall your TensorFlow:
pip uninstall protobuf
pip uninstall tensorflow
and then
pip install protobuf
pip install tensorflow
The error because , your system couldn't find CUDA enable for tensorflow-GPU version. Please refer link for installing tensorflow-GPU in here. If you want to access GPU version you have to install CUDA toolkit first. Make sure that when you are installing CUDA toolkit and cuDNN should support to your tensrflow version.
Related
I'm trying to use tensorflow with my PC's GPU (Nvidia RTX 3070Ti) in python-conda environment. I'm solving a small image-classification problem from kaggle. I've solved it in google-collab, but now I'm intrested in solving it on my local machine. However TF doesn't work properly locally and I have no idea why. I've read tons of solutions but it didn't help yet.
I'm following this guide and always install proper versions of TF and CUDA: https://www.tensorflow.org/install/source_windows
cuda-toolkit 10.1, cudnn 7.6, tf-gpu 2.3, python 3.8
Also I've installed latest NVidia drivers for videocard.
What I've tried:
I've installed proper version CUDA-toolkit and CUDnn from nvidia site. I've installed it properly and included everything that was needed into PATH. I've checked it - MS Visiual Studio finds both CUDA and CUDnn and can work with it. I've installed proper version of Tensorflow-GPU using conda into my environment.
Result: TF can't find my GPU and uses only CPU.
I've removed all CUDA and CUDAnn drivers. I've installed CUDA-toolkit, CUDnn and Tensorflow-GPU python packages into my conda environment.
Result: TF recognizes my GPU and uses it! But during DNN training happens error: Failed to launch ptxas Relying on driver to perform ptx compilation. Modify $PATH to customize ptxas location. And training goes very bad - accuracy is very low and doesn't improving.
When I use absolutely same code and data on google-collab, everything is going smoothly - I get ~90% accuracy on 5th epoch.
I've tried tf 2.1 and relevant cuda and cudnn, but it's still same result!
I've tried to install cudatoolkit-dev, but it didn't help to solve ptxas problem.
I'm about to give up and use PyTorch instead of Tensorflow.
So here is what worked for me:
Create 3.9 python environment
Install cuda and tensorflow packages from "Esri":
conda install -c esri cudatoolkit
conda install -c esri cudnn
conda install -c esri tensorflow-gpu
Then install tensorflow-hub:
conda install -c conda-forge tensorflow-hub
It will downgrade installations from previous steps, but it works. Maybe installing tensorflow-hub first could help to avoid it, but I didn't test it.
This weekend I have been trying a lot to install and get Tensorflow with GPU support to work on my computer, but I am not very experienced in using pip/conda and are now quite confused after watching and trying a lot of different tutorials/approaches from the web.
I have a GeForce GTX 1650 graphics card, and I have installed Cuda 10.0 (also 11.2, but I removed it from "PATH" and are only using the 10.0 version, I don't think that's a problem).
I have downloaded cuDNN 7.5.0 for CUDA 10, and I think that I have copied and placed the files correctly (installed cuDNN).
I am just trying to get some version of Tensorflow-gpu to work, but you can see the Tensorflow version i have been trying for now on the image.
I have tried to install and uninstall Python from my computer (I've also reinstalled Anaconda a lot of times), because I am not sure if I need to have a Python version installed (on my system) if I install a version of Python inside my Anaconda environment (in my example Python 3.7).
Does anyone know how to install Tensorflow GPU on Windows 10 with my settings (cuDNN 7.5.0, CUDA 10), or maybe have encountered some trouble with Python versions or Anaconda problems similar to mine?
Follow these steps to install Tensorflow GPU on windows system.
Make sure right version of Visual studio is installed. Check here.
Follow the instructions mentioned here to setup CUDA for windows system
Install Tensorflow
#check current python version
python --version
#Create the virtual environment
conda create -n tf python=PYTHON_VERSION
#Activate the tf environment
conda activate tf
#Install Tensorflow
pip install tensorflow
#Install CUDA and cuDNN using conda and make sure CUDA and cuDNN version should match the Tensorflow version
conda install -c anaconda cudatoolkit=10.0 cudnn=7.5
I have some problem with tensorFlow. I'm trying to install it with GPU on my manjaro linux with GTX 1060.
When I try to import tensorFlow in python with:
import tensorflow as tf
I get this error:
{...} ImportError: libcublas.so.8.0: cannot open shared object file:
No such file or directory {...}
With pip, I have installed tensorFlow-gpu:sudo pip install tensorflow-gpu
When I try to install cuda-8.0 (with pacaur -Syu cuda-8.0), after a very long loading, I got an error. Now when I try to install it, it does this:
Errors occurred, no packages were upgraded
Even if it's not on my pacaur list, and there is no reinstalling signed
I have install Keras with: sudo pip install Keras
I have install cudNN with: pacaur -Syu cudnn
I have installed my nvidia driver with (if I remember it right):pacaur -Syu nvidia
I am not familiar with manjaro. Assume you wanna install TensorFlow 1.4, the order would be:
Install latest Nvidia driver (version 384.xx or higher). Check its status in a terminal with nvidia-smi.
Install CUDA 8.0 without the GPU driver (as you have done it in step 1).
Add PATH=/usr/local/cuda-8.0/bin to the environment (in Ubuntu it's /etc/environment).
Added driver and CUDA paths to LD_LIBRARY_PATH. In Ubuntu, it is done by adding export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:/usr/local/cuda/lib64:/usr/lib/nvidia-384:/usr/local/cuda/extras/CUPTI/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} to /etc/bash.bashrc. At this point, you should be able to check CUDA version by nvcc --version.
Copy CUDNN files to somewhere and add that path to LD_LIBRARY_PATH. CUDNN needs no installation.
Install TensorFlow 1.4.
If you wanna install other versions of TensorFlow, you need to first check the supported versions of CUDA and CUDNN.
Hope this helps.
ImportError: libcuda.so.1: cannot open shared object file: No such file or directory
Failed to load the native TensorFlow runtime.
This error is appeared when import tensorflow.
I need to know steps to solve this problem.
If you are using TensorFlow with GPU, you need to install CUDA and cuDNN. Please follow instructions on https://www.tensorflow.org/install/
If you have already install CUDA and cuDNN, but still get this error, then you probably forgot to export your libraries: for Linux, you may need to set LD_LIBRARY_PATH to include CUDA libraries.
After installing TensorFlow 1.11 GPU via Anaconda "conda install tensorflow-gpu" I also experienced the same problem. Before TF 1.11 I used TF 1.04. Just before the TF update I updated Nvidia Driver to the version 396 through ppa.
There was no trace of libcuda.so.1 in my entire linux.
After many trials, the problem was solved when I changed the nvidia driver to 390. The 390 version inserted libcuda.so.1 to /usr/lib/i386-linux-gnu directory, which evidently solved the problem.
On Windows 10 with CUDA 8 and CuDNN 7 installed, I have troubles installing Tensorflow (both the GPU and CPU edition, though I'll focus on the GPU version).
When trying to install it system-wide with python3.5 using pip install tensorflow-gpu, it reports that there are no matching packages.
If I instead install it using the community supported anaconda distribution (using the steps described at the documentation page, it correctly installs Tensorflow, but when I import it into a program the following error is shown:
>>> import tensorflow as tf
[...]
ImportError: DLL load failed: The specified module could not be found.
[...]
ImportError: No module named '_pywrap_tensorflow_internal'
[...]
Failed to load the native TensorFlow runtime.
Entire stack trace is available on paste-bin.
For CUDA, I've set the following system environment variables: CUDA_HOME, CUDA_PATH, and CUDA_PATH_V8_0 as suggested by various tutorials. Furthermore cuDNN has been installed using nVidia's instructions, and path variables have been set to CUDA\v8.0\bin, and CUDA\v8.0\libnvvp.
It's probably because Tensorflow now only supports cuDNN v6.0 or v6.1, at least is what's maintained in the Installation Guide for Windows.
I had the same problem, but after updating Tensorflow from an old version to a newer one where the cuDNN had to be updated.