LinuxQuestions.org - [SOLVED] TensorFlow 2 for NVIDIA Quadro RTX 6000 GPU on Slackware current ?

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TensorFlow 2 for NVIDIA Quadro RTX 6000 GPU on Slackware current ?

does anybody out there have experience in setting up Slackware current for a NVIDIA Quadro RTX 6000 GPU with TensorFlow, CUDA, CUDNN, Python3, JDK, Nvidia_Kernel and whatever else is needed for TF2 ?

i got something similar running with slack14.2 and an old nVidia GPU but that was for TF1 : https://www.linuxquestions.org/quest...-a-4175631725/

i plan to do this on one of the new Linode.com GPU VPS servers. what obstacles can i expect to encounter ? do you suggest to wait with current and go with slack 14.2 ?

i still need some help here ! i just can't get this TensorFlow v2 pipeline to work :

Quote:

Linode VPS - nVidia RTX6000 GPU - slack142 - CUDA v10.0 - cuDNN v7.6.5 - TF2

Slack-Current is out because its gcc v9 is much too new unfortunately, though i didn't try to install an older gcc in parallel - perhaps i should do that ?

here is what i just tried w/o success as it fails right at the beginning :

Code:

lsmod | grep nouveau  --> empty, which is expected

sh cuda_10.0.130_410.48_linux.run --override --driver --toolkit --silent



  The packaged driver you're attempting to install is incompatible with the architecture of your system

i tried a dozen of solutions, nothing works ! i can't really shake up versions as TF2 requires CUDA10 and so on and so forth...

please, don't suggest other distros, i need and want to go with Slackware for various reasons.

ultimately it wasn't that difficult to get that python3/tensorflow pair running under Slackware-Current on a Linode VPS with NVIDIA Quadro RTX 6000 GPU - you just need to have all software versions in the pipeline in harmony. here my very rudimentary summary - but i really suggest to read carefully the nVidia's CUDA and cuDNN instructions :

0) install Slack-Current temporarilly on a Linode VPS type g6-nanode-1
1) install gcc v7.5.0 (because v9 is much too new) in /opt
2) type (so that gcc75 is invoked before gcc9) :

Code:

PATH="/opt/bin:$PATH"

3) disable nouveau :

Code:

installpkg xf86-video-nouveau-blacklist-noarch-1.txz

4) clone Linode nanode into a g1-gpu-rtx6000-1 (expensive!). use linode-cli for that. boot that GPU box.
5) PATH="/opt/bin:$PATH
6) install the regular nVidia graphics driver (not CUDA!) :

Quote:

sh NVIDIA-Linux-x86_64-410.104.run

7) nvidia-smi --> see output
8) reboot
9) PATH="/opt/bin:$PATH
10) ls -l /dev/nvidia*
11) install CUDA (without nVidia graphics driver !) :

Code:

sh cuda_10.0.130_410.48_linux.run --override --toolkit --samples --silent

12) /etc/bashrc :

Code:

PATH="/opt/bin:$PATH"

export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}

export LD_LIBRARY_PATH=/usr/local/cuda-0.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

13) /etc/rc.d/rc.local :

Code:

/usr/bin/nvidia-persistenced --verbose

14) reboot
15) PATH="/opt/bin:$PATH
16) cat /proc/driver/nvidia/version --> see output
17) nvcc -V --> see output
18) cd /root/NVIDIA_CUDA-10.0_Samples ; make
19) ./bin/x86_64/linux/release/deviceQuery --> see output
20) ./bin/x86_64/linux/release/bandwidthTest --> see output
21) lspci -v | grep -i nvidia --> see output
22) dmesg | grep -i NVRM --> see output
23) ls -l /usr/lib64/libcuda.so*
24) install cuDNN :

Code:

cd /tmp/GPU ; tar xfv cudnn-10.0-linux-x64-v7.6.5.32.tgz

25) copy *cudnn* files to designated directories and do chmod a+r on them
26) deb2tgz libcudnn7-doc_7.6.5.32-1+cuda10.1_amd64.deb
27) tar xfv libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.txz
28) cd $PWD/usr/src/cudnn_samples_v7/mnistCUDNN
29) gcc --version (should be 7.5 !)
30) make clean ; make
31) ./mnistCUDNN --> see output
32) reboot
33) install TensorFlow 2:

Code:

pip3 install --upgrade pip

pip3 install --upgrade tensorflow-gpu

34) python3 :

Code:

>>> import tensorflow as tf

>>> print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

>>> print(tf.reduce_sum(tf.random.normal([1000, 1000])))

35) clone the Linode g1-gpu-rtx6000-1 VPS back into a g6-nanode-1 VPS (to save money):

Code:

linode-cli linodes clone yourSourceID --linode_id yourTargetID

36) delete the g6-nanode-1 VPS
37) now you got your affordable g6-nanode-1 template VPS which you can anytime clone into any of the Linode VPS types including those expensive GPU VPSs.

good luck...

Question - your post re: Quadro

Hi there,

I saw your post re: installing with NVIDIA Quadro (etc).

I just purchased a refurbed machine (Dell T3610) with NVIDIA Quadro graphics. I'd like to install Slackware64 14.2 on it if possible. Right now it has Win 10 and I have not plugged it in at all.

I'm not a gamer, nor do I need super fast graphics. What I'm wondering is if you found it even possible to install with this board 'bare-bones', not with a fast new driver - albeit without blazing speed or anything. Or, did it just refuse to install.

Thanks for reply,

jrc