Solution

I would suggest you to use conda (Ananconda/Miniconda) to create a separate environment and install tensorflow-gpu, cudnn and cudatoolkit. Miniconda has a much smaller footprint than Anaconda. I would suggest you to install Miniconda if you do not have conda already.

Quick Installation

# Quick and dirty: with channel specification
conda create -n tf_gpu_env python=3.8 
conda activate tf_gpu_env
conda install tensorflow-gpu -c anaconda
conda install cudnn -c conda-forge 
conda install cudatoolkit -c anaconda

Check that Tensorflow is using the GPU

For more details see this: https://www.tensorflow.org/guide/gpu

# Sanity check for validating 
# visibility of the GPUs to TF
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

Easily Reproducible Installation with Environment File: environment.yml

Although you can quickly create a conda (Ananconda/Miniconda) environment as shown earlier, it is much more desirable to make the installation process as reproducible as possible: enters the environment.yml file.

Save the environment file at the root of your repository (or project) and run the following command to install all the packages in an isolated conda environment (here I have named it tfgpu_env) using the environment file. The top part of the environment file contains some useful commands.

conda env create -f environment.yml

Save the following as environment.yml under your repository. And consider pinning the following three libraries:

An example:

• tensorflow-gpu=2.4
• cudnn=8
• cudatoolkit=11

## filename: environment.yml

## Environment File Definition

name: tfgpu_env # tensorflow-gpu environment
channels:
  - conda-forge
  - anaconda
  - default
dependencies:
  - python=3.8
  ## Core Necessities
  - numpy # -c conda-forge, anaconda
  - pandas # -c conda-forge, anaconda
  - tabulate # -c conda-forge, anaconda  # necessary for df.to_markdown() in pandas
  - scipy # -c conda-forge, anaconda
  - matplotlib # -c conda-forge, anaconda
  ## Jupyter Related
  - jupyter # -c anaconda, conda-forge
  - jupyterlab # -c anaconda, conda-forge
  - jupyter_dashboards # -c conda-forge  (see: https://medium.com/plotly/introducing-jupyterdash-811f1f57c02e)
  - jupyter_contrib_nbextensions # -c conda-forge
  ## Progressbar
  - tqdm # -c conda-forge, anaconda
  ## Machine Learning
  - tensorflow-gpu # -c anaconda | version: 2.4.1 (linux), 2.3.0 (windows)
  # - tensorflow # -c anaconda | version: 2.2.0 (linux), 2.1.0 (windows)
  - cudnn # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #       # -c anaconda | version: 7.6.5 (linux/windows)
  - cudatoolkit # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #             # -c anaconda | version: 11.0.221 (linux/windows)
  - scikit-learn # -c conda-forge, anaconda
  ## Hyperparameter Optimization
  - optuna # -c conda-forge # works for pytorch, tf/keras, mxnet, scikit-learn, xgboost, lightgbm
  - keras-tuner # -c conda-forge
  ## Image Processing
  - opencv # -c conda-forge, anaconda
  - imageio # -c anaconda, conda-forge
  ## Image Augmentation
  - albumentations # -c conda-forge
  - imgaug # -c conda-forge
  ## Code Linting
  - pylint # -c conda-forge, anaconda
  - autopep8 # -c conda-forge, anaconda
  ## Installations with pip
  - pip:
    ## Web App Framework
    # - Flask-Testing
    - streamlit # https://docs.streamlit.io/en/stable/troubleshooting/clean-install.html

Useful Instructions

You might as well copy and paste the following instructions in the environment file itself, to keep them handy.

# Instruction:
#-----------------------------------------------------------
#
## For an environment installed locally (under: ./.venv)
# mkdir -p .venv && cd .venv
# conda env create --prefix . -f ../environment.yml
## For Updating local environment
# cd .venv
# conda env update --prefix . -f ../environment.yml  --prune
#
## For an environment installed globally
## with a name: fav_env 
# NOTE: The env-name is stored inside the 
#       environment.yml file.
# conda env create -f environment.yml
## For Updating global environment from env-file
# conda env update -f ./environment.yml  --prune
#
## Update conda itself
# conda update -n base -c defaults conda
#
## Creating a global environment in one-line: py37, py38
# conda create -n py37 python=3.7
# conda create -n py38 python=3.8
#
### In each of the envs: base, py37, py38
# conda install jupyter jupyterlab numpy scipy pandas matplotlib scikit-learn scikit-image tqdm plotly imageio requests pylint autopep8 tabulate opencv
#
## Export a platform independent copy of an environment
#  conda env export --from-history > path/to/environment.yml
### Make exports directory (if not present already) and export
# $targetDir = conda_exports
# mkdir ./$targetDir
# conda env export --from-history > ./$targetDir/exported_environment.yml

References

1. How to manage packages using Conda.

2. https://docs.anaconda.com/anaconda/user-guide/tasks/tensorflow/

3. https://www.tensorflow.org/guide/gpu

4. https://www.fastwebhost.in/blog/how-to-find-if-linux-is-running-on-32-bit-or-64-bit/

5. Miniconda installtion: https://docs.conda.io/en/latest/miniconda.html

The .dll file was not installed when I installed Cuda. But I had installed pytorch and that package included the dll file . so I added that to the path variable and next time when I ran tensorflow, it could find the dll (from my recent path addition) and it worked.

dll path in pytorch lib

C:UsersYOUR_USER_NAMEAppDataRoamingPythonPython38site-packagestorchlib

Confirmation

print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
Num GPUs Available:  1

Previously it was 0

Taken directly from Tensorflow’s website: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin

Note: TensorFlow with GPU access is supported for WSL2 on Windows 10 19044 or higher. This corresponds to Windows 10 version 21H2, the November 2021 update. You can get the latest update from here: Download Windows 10. For instructions, see Install WSL2 and NVIDIA’s setup docs for CUDA in WSL.

conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
python3 -m pip install tensorflow

if you have Cuda installed . For me i was using a RTX 3060 so CUDA 11.2 was installed but for some reason the ddls inside it wasnt getting picked up so i copied the required ddls from that folder and pasted in windows/System32 folder it seems to be working now