How to get Tensorflow working with Anaconda on Windows

conda install cudatoolkit==10.1.243 cudnn tensorflow-gpu

Adding NVIDIA GPU support for Ubuntu 18.04

This is from https://www.tensorflow.org/install/gpu

Add NVIDIA package repositories

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin

sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600

sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub

sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"

sudo apt-get update

wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb

sudo apt install ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb

sudo apt-get update

Install NVIDIA driver

sudo apt-get install --no-install-recommends nvidia-driver-450

Reboot. Check that GPUs are visible using the command:

nvidia-smi

wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/libnvinfer7_7.1.3-1+cuda11.0_amd64.deb

sudo apt install ./libnvinfer7_7.1.3-1+cuda11.0_amd64.deb

sudo apt-get update

Install development and runtime libraries (~4GB)

sudo apt-get install --no-install-recommends \
    cuda-11-0 \
    libcudnn8=8.0.4.30-1+cuda11.0  \
    libcudnn8-dev=8.0.4.30-1+cuda11.0

Install TensorRT. Requires that libcudnn8 is installed above.

sudo apt-get install -y --no-install-recommends libnvinfer7=7.1.3-1+cuda11.0 \
    libnvinfer-dev=7.1.3-1+cuda11.0 \
    libnvinfer-plugin7=7.1.3-1+cuda11.0

Install Anaconda

Download installer from: https://www.anaconda.com/download/#linux

Include the bash command regardless of whether or not you are using Bash shell.
If you did not download to your Downloads directory, replace ~/Downloads/ with the path to the file you downloaded.

bash ~/Downloads/Anaconda3-YYYY.MM-Linux-x86_64.sh

Scroll to the bottom of the license terms and enter “Yes” to agree.
Accept the default install location.
The installer prompts “Do you wish the installer to initialize Anaconda3 by running conda init?” Choose “yes”.
Close and open your terminal window for the installation to take effect, or you can enter the command:

source ~/.bashrc

To run conda from anywhere without having the base environment activated by default, use:

conda config --set auto_activate_base False

Create the environment tf2

git clone https://github.com/Htnek/environment.git

conda env create -f environment.yml

conda activate tf2

Registering kernel

python -m ipykernel install --user --name=python3

Removing unwanted-kernel

Find it with:

jupyter kernelspec list

Remove it with:

jupyter kernelspec uninstall unwanted-kernel

Starting Jupyter Notebook

jupyter notebook

Update environment tf2

conda env update --file environment.yml  --prune

Removing environment

Show name of environment

conda info --envs

Removal of environment

conda remove --name name_of_environment --all

Test if GPU is available

Start a terminal

conda activate tf2

Start python

python

In python write:

import tensorflow as tf

In python write:

tf.config.list_physical_devices('GPU')

If all goes well you will see:

Adding visible gpu devices: 0
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

How to watch GPU usage

watch -n 1 nvidia-smi