Okay so just a final follow-up with all the steps taken to fully solve the driver to CUDA issue on my system:
Hope it leads someone in the right direction.
System Specs:
MSI - 11th Gen Intel® Core™ i7-11800H @ 2.30GHz × 16
NVIDIA Corporation GA106M [GeForce RTX 3060 Mobile
16Gb RAM running Ubuntu 22.04
STEPS TAKEN:
Install Anaconda:
sudo apt install curl
mkdir tmp
cd tmp
curl -O https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-x86_64.sh
bash Anaconda3-2022.10-Linux-x86_64.sh
source ~/.bashrc
conda -V
conda update conda
conda update anaconda
Install Nvidia Driver:
V01 [2021]:
sudo ubuntu-drivers list # List available drivers
sudo ubuntu-drivers install nvidia:515 # Install specified driver
reboot
Create and run Conda Virtual with Spyder 5:
INSTALL
conda create -c conda-forge -n spyder-env spyder numpy scipy pandas matplotlib sympy cython
USE CONDA-FORGE:
conda activate spyder-env
conda config --env --add channels conda-forge
conda config --env --set channel_priority strict
UPDATE:
conda update -n base conda
conda activate spyder-env
conda update spyder
CHECK:
conda list Spyder$
Install CUDA Toolkit:
Install conda-forge:
conda config --add channels conda-forge
conda config --set channel_priority strict
Install CUDA & CUDNN:
With CONDA:
# - LIST -
conda search cudatoolkit --channel conda-forge
conda search cudnn --channel conda-forge
# - Install -
conda install cudatoolkit <-- 'nvcc' still version not found
conda install cudnn
Install missing packages:
conda install pytorch
- Included cudatoolkit / cudnn / matplotlib / pandas / numpy
- Non Included: torchvision / tensorflow-gpu / sklearn / keras
conda install torchvision
conda install tensorflow-gpu <-- GPU: 1
conda install -c conda-forge scikit-learn
================================================================
Check Installations:
================================================================
conda activate spyder-env
Driver Check:
nvidia-smi
cat /proc/driver/nvidia/version
CUDA Check:
conda list cudatoolkit <-- 11.7.0
conda list cudnn <-- 8.4.1.50
nvcc --version <-- not found
Tensoflow Check:
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
CUDA Check:
>>> import torch
>>> print(torch.version.cuda) <-- 11.2
Sub-Packages Check:
import tensorflow as tf
import keras
import torch
import sklearn
import matplotlib
import pandas
from torchvision.models import resnet50
================================================================
Final Results:
================================================================
cat /proc/driver/nvidia/version
NVRM version: NVIDIA UNIX x86_64 Kernel Module 515.65.01 Wed Jul 20 14:00:58 UTC 2022
GCC version:
nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |
| N/A 30C P0 22W / N/A | 5MiB / 6144MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1922 G /usr/lib/xorg/Xorg 4MiB |
+-----------------------------------------------------------------------------+
conda list cudatoolkit
conda list cudnn
# Name Version Build Channel
cudatoolkit 11.7.0 hd8887f6_10 conda-forge
cudnn 8.4.1.50 hed8a83a_0 conda-forge
nvcc --version
Command 'nvcc' not found, but can be installed with:
sudo apt install nvidia-cuda-toolkit
In Python, the GPU is now recognised:
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
Returns: Num GPUs Available: 1
