how do you install only a CPU version of Tensorflow 2.x using pip ?
In the past, it was possible to install this 2 different versions.
Since I am running the scripts in a nonen GPU device ( without envidia card, intel card available without cuda support), I am getting following error:
2020-04-14 23:28:14.632879: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2020-04-14 23:28:14.632902: E tensorflow/stream_executor/cuda/cuda_driver.cc:313] failed call to cuInit: UNKNOWN ERROR (303)
In the past my workaround was to use a CPU only version.
Thanks for the hints in advance
You can choose the CPU-only version of tensorflow depending on your python version.
Check the list here:
https://www.tensorflow.org/install/pip#package-location
e.g. you will need to do the following for Python 3.8:
pip3 install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.3.0-cp38-cp38-manylinux2010_x86_64.whl
Issue solved after installing a CPU only version.
I used pin tensorflow-cpu and the version of the release. Somehow the fallback solution for CPU did not work in my setup.
Related
I am using a package that requires tensorflow-gpu == 2.0.0 and CUDA=10.0.0 with cudann==7.6.0
I am running this code on a NVIDIA gpu cluster and when I run nvidia-smi it shows
this. It still shows cuda 11, which I guess is the one installed on the actually server.
I was told that I can basically 'override' this version by installing the cudatoolkit in the version that I need. I did that and installed cudatoolkit==10.0.
Unfortunately I am now running into a problem when trying to run an LSTM based model with tensorflow-gpu. I get the following:
2022-06-14 17:02:26.988359: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.2/lib64
2022-06-14 17:02:26.989175: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.2/lib64
2022-06-14 17:02:26.989208: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
In the path I still see cuda11. Is this causing the problem? How can I resolve this?
As you mentioned in the comment you need to use TensorFlow 2.1, then you need to install cuDNN 7.6 and CUDA 10.1 specifically.
Please follow the below tested build configurations to know about Python and TensorFlow versions compatible CUDA and cuDNN .
Please check this link for more details on GPU setup.
While training with Tensorflow 2 Object Detection API in Google Collab, I get the following errors:
2022-03-07 15:27:51.475298: E tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded runtime CuDNN library: 8.0.5 but source was compiled with: 8.1.0. CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library. If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile configuration.
2022-03-07 15:27:51.477142: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at conv_ops.cc:1120 : UNKNOWN: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
I have tried updating my CuDNN library in Google collab, using this StackOverflow answer:
Loaded runtime CuDNN library: 8.0.5 but source was compiled with: 8.1.0. when using google colab.
However, this does not solve the issue.
I am using Tensorflow 2.7.0.
The following is a workaround for the problem, as posted on the official Github
This may not work always and may not be safe, but as a workaround for the time being, you may try running !apt install --allow-change-held-packages libcudnn8=8.1.0.77-1+cuda11.2
Machine Setting:
GPU: GeForce RTX 3060
Driver Version: 460.73.01
CUDA Driver Veresion: 11.2
Tensorflow: tensorflow-gpu 1.14.0
CUDA Runtime Version: 10.0
cudnn: 7.4.1
Note:
CUDA Runtime and cudnn version fits the guide from Tensorflow official documentation.
I've also tried for TensorFlow-gpu = 2.0, still the same problem.
Problem:
I am using Tensorflow for an objection detection task. My situation is that the program will stuck at
2021-06-05 12:16:54.099778: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10
for several minutes.
And then stuck at next loading process
2021-06-05 12:21:22.212818: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
for even longer time. You may check log.txt for log details.
After waiting for around 30 mins, the program will start to running and WORK WELL.
However, whenever program invoke self.session.run(...), it will load the same two library related to cuda (libcublas and libcudnn) again, which is time-wasted and annoying.
I am confused that where the problem comes from and how to resolve it. Anyone could help?
Discussion Issue on Github
===================================
Update
After #talonmies 's help, the problem was resolved by resetting the environment with correct version matching among GPU, CUDA, cudnn and tensorflow. Now it works smoothly.
Generally, if there are any incompatibility between TF, CUDA and cuDNN version you can observed this behavior.
For GeForce RTX 3060, support starts from CUDA 11.x. Once you upgrade to TF2.4 or TF2.5 your issue will be resolved.
For the benefit of community providing tested built configuration
CUDA Support Matrix
while importing tensorflow
Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory
2020-08-28 00:21:19.206030: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
system
Hp 245 g5 notebook
operating system ubuntu 18.4
How to solve the problem?
It seems you are trying to use the TensorFlow-GPU version and you have downloaded conflicting software versions for it.
Note: GPU support is available for Ubuntu and Windows with CUDA enabled cards only.
If you have a Cuda enabled card follow the instructions provided below.
As stated in Tensorflow documentation. The software requirements are as follows.
Nvidia gpu drivers - 418.x or higher
Cuda - 10.1 (TensorFlow >= 2.1.0)
cuDNN - 7.6
Make sure you have these exact versions of the software mentioned above. See this
Also, check the system requirements here.
For downloading the software mentioned above see here.
For downloading TensorFlow follow the instructions provided here to correctly install the necessary packages.
I'm running a Windows computer with just a CPU (no GPU). When I run pip install tensorflow -vvv in order to see what pip is doing, it lists a lot of links, but for all of them, it says "Skipping link ... it is not compatible with this Python."
Does tensorflow support Python 3.6.4 on Windows? If so, what binary URL should I use to install it?
(I previously installed with this version due to reading this, but ran into this error without the DLL load failed message, so I'm wondering if there's a better version I should use.)
Also, I'm aware that Tensorflow says they support Python 3.x, but right now it hasn't been working for me.
You have probably installed Python 32bits, you need the 64bits version