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
Related
I have tried to get my laptop gpu to work with tensorflow, however I keep encountering this issue
I had tensorflow installed through pip (on anaconda env) with CUDA 11.2 and CUDnn 8.1, and it won't work!
I then tried a previously known version to work (tensorflow 2.4 with CUDA 11.0 and so on.
-but pip will not install tensorflow 2.4.0 (I am assuming it is no longer supported)
I have included a photo with proof of my cuda and cudnn versions
I believe the issue may lie in the folder you extract your cuDNN to.
Personally, I've extracted my cuDNN to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2.
When you open the zip cuDNN file, open the "cuda" file in the zip, and then extract the rest (bin etc.) into the above mentioned directory.
Make sure you restart the program/kernel so it can detect the new files.
Also, don't forget to add the CUDA path to your environment variables, though as it knows to look for cudnn64_8.dll I expect this is fine.
I have already read this previous issue, but it did not answer my question. Different CUDA versions shown by nvcc and NVIDIA-smi
The above issue answers the question whether there is a problem with the installation. But it does not answer my question "If I install other applications in Python that require CUDA, which CUDA version should I assume that I have?".
In the previous issue, the author had intentionally installed two different versions of CUDA on his system. But I have only installed CUDA 10.1 on my computer, yet Python claims that I have installed version 11.1.
CUDA was installed on my computer following instructions on Nvidias homepage, by downloading installer files. I have not installed CUDA packages via pip or pip3 in python.
Version according to cmd.
Version in the file system.
Version according to System Environment Variables.
Version according to nvidia-smi called from a python console.
If I install other applications in Python, which CUDA version should I assume that I have? How can I get rid of the 11.1 version, and only keep the 10.1 version?
"If I install other applications in Python that require CUDA, which CUDA version should I assume that I have?"
You have CUDA 10.1. You will satisify the needs of any CUDA application in python such as tensorflow, if that application was linked against CUDA 10.1.
If I install other applications in Python, which CUDA version should I assume that I have?
You have CUDA 10.1
How can I get rid of the 11.1 version, and only keep the 10.1 version?
You can't, and don't want to. The CUDA 11.1 version reported is the version of the CUDA driver API. CUDA applications that are usable in Python will be linked either against a specific version of the runtime API, in which case you should assume your CUDA version is 10.1, or else they will be linked against the driver API. If linked against the driver API only, then based on your GPU driver install, any linkage against any driver API version up through CUDA 11.1 will work. That would include any driver API applications linked against CUDA 10.1.
If you were to uninstall the driver that is reporting the 11.1 version, you would break your CUDA install and nothing would work. The driver reporting 11.1 is perfectly fine and no problem at all for usage of CUDA applications that expect CUDA 10.1.
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 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.
I am trying to install tensorflow(GPU) for windows using Python 3.5 but I get error when I try to import the tensorflow package.
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:119] Couldn't open CUDA library cublas64_80.dll
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\cuda\cuda_blas.cc:2294] Unable to load cuBLAS DSO.
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:119] Couldn't open CUDA library cudnn64_5.dll
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\cuda\cuda_dnn.cc:3459] Unable to load cuDNN DSO
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:119] Couldn't open CUDA library cufft64_80.dll
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\cuda\cuda_fft.cc:344] Unable to load cuFFT DSO.
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library nvcuda.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:119] Couldn't open CUDA library curand64_80.dll
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\cuda\cuda_rng.cc:338] Unable to load cuRAND DSO.
Did you follow the instructions for installing CUDA Toolkit? You can find a link to the instructions here.
Requirements to run TensorFlow with GPU support
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 8.0. 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 8.0. cuDNN v5.1. 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.