I am building a Deep Learning rig with a GeForce RTX 2060.
I am wanting to use baselines-stable which isn't tensorflow 2.0 compatible yet.
According to here and here, tensorflow-gpu-1.15 is only listed as compatible with CUDA 10.0, not CUDA 10.1.
Attempting to download CUDA from Nvidia, the option for Ubuntu 20.04 is not available for CUDA 10.0.
Searching the apt-cache does not result in CUDA 10.0 either.
$ sudo apt-cache policy nvidia-cuda-toolkit
[sudo] password for lansford:
nvidia-cuda-toolkit:
Installed: (none)
Candidate: 10.1.243-3
Version table:
10.1.243-3 500
500 http://us.archive.ubuntu.com/ubuntu focal/multiverse amd64 Packages
I would highly prefer not to have to reinstall the OS with an older version of Ubuntu. However experimenting with reinforcement learning was the motive for purchasing this PC.
I see some possible clues that it might be possible to build tensorflow-gpu-1.15 from source with cuda 10.1 support. I also saw a random comment that tensorflow-gpu-1.15 will just-work with tf 1.15, but I am not wanting to make a miss-step installing things until I have a signal that is the direction to go. Uninstalling things isn't always straightforward.
Given the situation is there a way to run tensorflow 1.15 with gpu support on Ubuntu 20.04.1?
As this also bothered me I found a working solution that I think is more versatile than using docker containers.
The main idea is from here (not to claim credit from others).
To make a working solution for Ubuntu 20.04 and TensorFlow 1.15 one needs:
I chose runfile as method which resulted into 1 main runfile and 1 patch runfile being available:
cuda_10.0.130_410.48_linux.run
cuda_10.0.130.1_linux.run
The toolkit can be safely installed using the instructions provided with no risk since each version allocates a different folder in the system (typically this would be /usr/local/cuda-10.0/
).
cudnn-10.0-linux-x64-v7.6.5.32.tgz
. $ sudo cp cuda/include/cudnn.h /usr/local/cuda-10.0/include
$ sudo cp cuda/lib64/libcudnn* /usr/local/cuda-10.0/lib64
$ sudo chmod a+r /usr/local/cuda-10.0/include/cudnn.h /usr/local/cuda-10.0/lib64/libcudnn*
/etc/profile.d/cuda.sh
which will contain the update to the LD_LIBRARY_PATH
variable. It should contain something like: export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda-11.3/lib64:/usr/local/cuda-10.0/lib64:$LD_LIBRARY_PATH
This command would normally do the work:
$ sudo sh -c ‘echo export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda-11.3/lib64:/usr/local/cuda-10.0/lib64:\$LD_LIBRARY_PATH > /etc/profile.d/cuda.sh’
This requires a restart though to be evaluated I think. Anyway, this way the system will search for the relevant so files in:
a) /usr/local/cuda/lib64
(the default symbolic link) and it will fail
b) to the virtually same as the latter /usr/local/cuda-11.3/lib64
and also fail BUT it will search also
c) /usr/local/cuda-10.0/lib64
which will be successful.
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt-get install python3.7
This just installs python3.7 to the system it does not make it default. The default is the previous one.
virtualenv -p python3.7 ~/tensorflow_1-15
which creates a new venv
with Python 3.7 in it.
Now populate with all required modules and you are set to go.