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.
Related
I installed the CUDA toolkit and CUDNN and added it to the PATH, but this function still returns False.
import tensorflow as tf
print(tf.test.is_built_with_cuda())
I use JupyterNotebook from the Anaconda distribution.
The OS is Windows 11. CUDA v11. CUDNN v8.7. Also have zlib dll.
The command
nvcc -V
in PowerShell works, it outputs the CUDA version.
TensorFlow also does not detect the graphics card because of CUDA.
print(tensorflow.config.list_physical_devices())
This code returns information only about CPU.
You need to install CUDA 11.2 and cuDNN 8.1 as per this build configurations to enable Tensorflow GPU support in your system.
Please install all the required software for GPU support and set the PATH for these software to the bin directory.
Then use below code for TF-gpu setup in conda environment.
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
pip install --upgrade pip
# Anything above 2.10 is not supported on the GPU on Windows Native
pip install "tensorflow-gpu<2.11"
To verify the GPU setup:
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
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.
I am trying to run jax on an nvidia dgx box, but am failing miserably, thus:
>>> import jax
>>> import jax.numpy as jnp
>>> x = jnp.arange(10)
2021-10-25 13:00:05.863667: W
external/org_tensorflow/tensorflow/stream_executor/gpu/asm_compiler.cc:80] Couldn't
get ptxas version string: INTERNAL: Couldn't invoke ptxas --version
2021-10-25 13:00:05.864713: F
external/org_tensorflow/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc:435]
ptxas returned an error during compilation of ptx to sass: 'INTERNAL: Failed to
launch ptxas' If the error message indicates that a file could not be written,
please verify that sufficient filesystem space is provided.
Aborted (core dumped)
Any suggestions would be much appreciated.
This means that your CUDA installation is not configured correctly, and can generally be fixed by ensuring that the CUDA toolkit binaries (including ptxas) are present in your $PATH. See https://github.com/google/jax/discussions/6843 and https://github.com/google/jax/issues/7239 for responses to users reporting similar issues.
For this problem you need to install nvidia-driver, cuda and cudnn correctly and the risky command here would be: sudo apt install nvidia-cuda-toolkit avoid this command if you have installed those 3 already.
the way which works for me:
Install nvidia-driver: follow this and proper version also. you can try sudo ubuntu-drivers devices in ubuntu
Install cuda : for finding which cuda version works for you run nvidia-smi and on top-left you will see compatible version for the cuda then go nvidia cuda archive and follow the instructions there.
at this step you should be able to see cuda foder when you type ls /usr/local. if you want to install header also you can find useful command from nvidia installation guide for cuda.
Install cudnn which means copy paste some files into /usr/local/cuda directory if you go through cuDNN nvidia guide you would find the best way.
the last step you need to refer to the cuda path (/usr/local/cuda if you follow above). for example if you use docker you need to mount it like here. avoid install nvidia-cuda-toolkit it would remove your previous installation and instead you can install it in conda-env by conda install -c nvidia cuda-nvcc which doesn't interfere your cuda installation.
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
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.