It’s been just 2 days since Windows 11 came out and I am already setting up my system for the ultimate machine learning environment. Today we are going to setup a new anaconda environment with tensorflow 2.5 with GPU support using NVIDIA CUDA 11.4 and CUDNN 8.2.4 along with Python 3.8. This is going to be a handson practical step by step guide to create this environment from scratch with a fresh Windows 11 installation.
I have a decent machine with a decent GPU and I do some hobby machine learning projects. I know getting the CUDA support up and running on tensorflow is such a pain point and that is why we have all these posts and videos on different platforms how to do it properly. This blog post is yet another such post but the idea here is – I am writing this as I am setting up the things and this blog post should help you setup your environment just like I did.
I would argue that recently it has become very simple and straightforward to get up and running on Windows (Windows 11 specifically). Before we get started here are the set of software that you need to install on your machine. Some of this software is optional but I like to have a fully loaded environment so that I don’t have to wait for the software/tools installation later on when I have some awesome idea.
My Environment
My environment is as follows.
- Operating System: Windows 11 Pro (everything in artical would work on Windows 11 editions)
- Processor: AMD Ryzen Threadripper 2970WX (CPU doesn’t matter)
- Motherboard: Asus Rog X399 (Any motherboard is fine)
- Graphics Card: NVIDIA GeForce RTX 2070 8GB GDRR6 (any RTX or Quadro GPU is fine)
I have added a new way of running tensorflow with GPU using docker containers, that approach is much simpler and more robust then configuring the entire drivers and CUDA libraries on your local machine. Checkout the article at the below link.
https://thegeeksdiary.com/2023/01/29/how-to-setup-tensorflow-with-gpu-support-using-docker/
Steps
You will need the following installed on your machine.
- NVIDIA Graphics Driver: I am using driver version 510.06 gameready drivers. These drivers are special because they also include WSL 2 support which means that I would be able to use the same drivers inside my WSL2 distributions – if you don’t understand WSl 2 – that is fine – you don’t need to. You can download the current version of the drivers from this link.
- Visual Studio 2019 – for C++ runtime: You can down it from the official website here. Please note that Visual Studio 2019 community edition is free to download and install. I will explain the installation process below.
- Miniconda: You can get the latest version on Miniconda from here. As of this writing I am using Miniconda 3 64 bit version. The version of python included with base environment is 3.9.
- CUDA Toolkit for Windows 11: In order for us to be able to use CUDA capable software and libraries we would need the CUDA SDK or CUDA Toolkit as it is often known. This provides us the set of primitives and libraries that interact with the underlying hardware. CUDA Toolkit also includes NVCC compiler which is a specialized C/C++ compiler that compiles code which is able to run CUDA capable GPUs. You can download the CUDA toolkit form this link. As of this writing the current version of CUDA toolkit is 11.4.
- CUDNN: CUDNN is a set of C/C++ Libraries that expose the deep neural network capabilities on top CUDA. All deep learning frameworks use CUDNN to use NVIDIA GPUs – including tensorflow.
- Create a Conda environment: After installing all the software we will just create a new conda environment.
Step 1: NVIDIA Graphics Driver Installation
I am starting off with a fresh install of Windows 11 so I need to make sure that I have applied all the latest updates. Although not necessary it is good to install them so that any dependencies needed by the software that we will install later is installed smoothly. Once done, you can visit this page to get the latest NVIDIA GPU Driver (note that this is a special driver that adds WSL2 GPU sharing capabilities – you can event install the normal driver for the steps mentioned in this article). Even a simple google search will lead you to this page https://www.nvidia.com/Download/index.aspx. On this page you need to select your graphics card and operating system to be able to download the correct driver. Here is an example selection for my setup.
Once you have downloaded the setup binary – just double click on the file to run it. Just click next as many times you are prompted to :-). Congratulations you have just completed step 1.
Step 2: Visual Studio 2019
As of this writing the stable version of Visual Studio that is available is Visual Studio 2019. I am using the community edition which is a full development environment. If you have any other license (professional or enterprise) that is also fine. Download the visual studio installer from https://visualstudio.microsoft.com/downloads/. Double click the file and make sure that you have the highlighted (“Desktop development with C++”) workloads installed. This will install the required C++ toolchain.
This will take a while to install depending on the workloads that you have selected. wait for it to finish. You might have to restart your computer before going on to next step.
Step 3: Miniconda
Anaconda is a python distribution that allows you to have several python environments on one machine. This gives you the freedom to work with multiple isolated versions of python without relying on a global environment which might have package conflicts. Isolation is always good. Head over to https://docs.conda.io/en/latest/miniconda.html to download a trimmed version of Anaconda otherwise known as Miniconda. Download the installer and finish the steps with the regular options.
Step 4: CUDA Toolkit for Windows 11
You can download and install the CUDA toolkit by visiting this link https://developer.nvidia.com/cuda-downloads. As of this writing I am using CUDA Toolkit version 11.4. Download the installer and just click next until finished – it’s simple.
Step 5: CUDNN Library
Now it’s time for us to install the CUDNN library for which is used by tensorflow for creating deep neural networks on GPU using CUDA. Visit the page https://developer.nvidia.com/rdp/cudnn-download and download CUDNN for CUDA Version 11.4 (as of this writing latest version 11.4 for CUDA and 8.2.4 for CUDNN). Please note that you need have a NVIDIA developer account with NVIDIA which is free to create – I guess everybody want have your email address today.
Download the zip file as shown above, extract the contents in any directory. Open the extracted folder, and also open another explorer window with the following path: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA. Inside this folder you will find a folder for your current CUDA toolkit version, for example for me it is v11.4. Copy the files from the extracted folder like so. In the below tables ALL_CAPS values like these refer to the specific parameters specific to your environment for example CUDA_VERSION refers to the version of CUDA that you downloaded, likewise EXTRACTED_FOLDER refers to the path where you extracted the CUDNN Library zip file.
| Source | Destination |
| <EXTRACTED_FOLDER>\bin\* | C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v<CUDA_VERSION>\bin\ |
| <EXTRACTED_FOLDER>\include\* | C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v<CUDA_VERSION>\include\ |
| <EXTRACTED_FOLDER>\lib\x64\* | C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v<CUDA_VERSION>\lib\x64 |
Please note that you need to copy the files of the above folders and not the folders themselves. After this verify that you have the below listed Environment Variables created on you machine.
| Environment Variable | value |
| CUDA_PATH | C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v<CUDA_VERSION> |
| CUDA_PATH_V<CUDA_VERSION> | C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v<CUDA_VERSION> |
If you need to know how to access and set environment variables on Windows 11, refer to this article. Although these instructions are for Windows 10 but they still apply to Windows 11.
Step 6: Create a Conda environment
This is now the moment of truth. In this step we will created the Conda environment with Python version 3.8. Let’s start.
Open the Anaconda Prompt by going to start menu.
Once on the prompt run the following commands.
# install Jupyter Notebook in our base environment conda install -y jupyter # create a new environment with name 'tensorflow-gpu' and python version 3.8 conda create --name tensorflow-gpu python=3.8 # activate the environment conda activate tensorflow-gpu # install python 2.5 pip install tensorflow==2.5 # enable notebook support in our environment conda install -y -c conda-forge nb_conda # add the environment to Jupyter Kernel python -m ipykernel install --user --name tensorflow-gpu --display-name "Python 3.8 (tensorflow-gpu)" # deactivate the environment conda deactivate # create a new directory for our test notebook to check the tensorflow GPU initialization mkdir test-installation # go inside the new directory cd test-installation # activate our test environment conda activate tensorflow-gpu # start jupyter notebook jupyter notebook
This will open a browser window as shown below.
Now create a new notebook by clicking on the “New” toolbar on the right hand corner as shown below, make sure that you select the kernel name as “Python 3.8 (tensorflow-gpu)” – my environment name is “Teflon-GPU-TF (Python 3.8)” but if you followed the above commands yours environment name will be Python 3.8 (tensorflow-gpu). This will open a new blank notebook as shown below.
Yours notebook will be empty (whereas above has code – which you need to paste from below).
import sys
import tensorflow.keras
import tensorflow as tf
import numpy as np
print(f"Tensor Flow Version: {tf.__version__}")
print(f"Keras Version: {tensorflow.keras.__version__}")
print()
print(f"Python {sys.version}")
gpu = len(tf.config.list_physical_devices('GPU'))>0
print("GPU is", "available" if gpu else "NOT AVAILABLE")
After running the above code in the notebook you should see an output similar to the one shown below.
Tensor Flow Version: 2.5.0 Keras Version: 2.5.0 Python 3.8.11 (default, Aug 6 2021, 09:57:55) [MSC v.1916 64 bit (AMD64)] GPU is available
Congratulations you have just completed your environment setup with Miniconda. You can now build great deep learning models using your GPU. Please comment below if you face any issues.
I have added a new way of running tensorflow with GPU using docker containers, that approach is much simpler and more robust then configuring the entire drivers and CUDA libraries on your local machine. Checkout the article at the below link.
https://thegeeksdiary.com/2023/01/29/how-to-setup-tensorflow-with-gpu-support-using-docker/
I have created a series on tensorflow deep learning – checkout the first article in that series here.
https://thegeeksdiary.com/2023/03/23/introduction-to-deep-learning-with-tensorflow-deep-learning-1/
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Installing Anaconda — Step 1
You will need to install Anaconda to follow along with this tutorial. I recommend you download the Python 3.7 version. If you already have Anaconda installed, go to step 2.
Anaconda can be downloaded here:
https://www.anaconda.com/distribution/
Running Anaconda — Step 2
After installation has completed, search for anaconda navigator in the windows search bar. We’re going to be using the anaconda prompt, but first you will need to run anaconda navigator. The navigator goes through a setup process upon startup. When anaconda navigator launches successfully, close it. Now open Anaconda Prompt. You should see a command line tool. This is what we’ll be using to install Tensorflow-GPU.
Creating and Activating A New Environment — Step 3
To avoid our Tensorflow GPU installation conflicting with other python environments. I highly recommend creating a new environment. This is easily done. If you’d like to learn more about python environments and why we need them, click here.
You can create a new environment with the following command. After -n is where we specify the environment name. This is followed by the version of python we want to install. I recommend v3.7.
We then need to activate our environment. The commands differ slightly between Windows and Mac. After entering the command, you should see that (base) has now changed to (your environment name). This means that any commands entered from this point on will be executed within that environment.
Windows:
Mac:
Installing a Python Kernel — Step 4
We need to create a Python Kernel inside of our environment so that we can run our code. Anaconda used to create a kernel by default. However, this is no longer the case, so we must do it manually.
First we need install ipykernel:
We now need to install a kernel. Again, make sure you have activated your environment, (gputest), in this case. To install a kernel, enter the following command. The variables after ‘—name’ and display-name will need to be set to the name of your environment. If entered correctly, you’ll see a response ‘Installed kernelspec’.
Installing Tensorflow-GPU — Step 5
This is very straight forward and requires one simple command. The beauty of using Anaconda to install Tensorflow-GPU is that it takes care of all the complicated stuff for you. Anaconda installs everything you need, including cuDNN and cudatoolkit. Look no further. This can take a while because it install a number of libraries.
That’s it!
You have now successfully installed Tensorflow-GPU to a brand new python environment. We can test to see if everything is working using Jupyter Notebook and Keras.
GPU Detection Test — Step 6
We can test to see if our GPU is working by running a simple script. You can download all the necessary files here:
To run the test, you will need to conda install Jupyter, and pip install Keras. It is important that you pip install Keras. If you conda install Keras, it will downgrade your tensorflow-gpu package and may cause issues.
After installing Jupyter and Keras, we can now launch Jupyter Notebook with the following command:
Copy the download files into your environment directory. You will need to do this via your OS file system. For example my file path looks like this:
Now navigate to the filepath inside of the Jupyter Notebook server. Open the notebook named »tensorflow-gpy_detection_test.ipynb’.
Now navigate to the filepath inside of the Jupyter Notebook server. Open the notebook named ‘tensorflow-gpy_detection_test.ipynb’.
Make sure you are using the correct kernel!
This is where most people go wrong. After opening the notebook, look at the top right of the screen. Ensure you have the correct kernel selected (i.e. the kernel we just installed to our environment.) If your kernel is not correct, you change kernel manually as shown below.
The first code cell begins the GPU detection test. You can execute the code cell by clicking on it and pressing ‘Shift + ENTER’. If everything went well, you should see an output displaying your default GPU device ‘/device:GPU:0’.
Conclusion
Tensorflow-GPU has always been notoriously difficult to install. My way is the quickest and easiest I have seen so far. Library updates can cause things to go wrong, so be prepared for that in the future! Hopefully you were able to follow this tutorial successfully. If you ran into problems, I suggest going through the tutorial again very carefully. You can also leave a comment below and I will try to help you.
Thanks for reading.
Happy Coding 
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TensorFlow enables your data science, machine learning, and artificial intelligence workflows. This page shows how to install TensorFlow using the conda package manager included in Anaconda and Miniconda.
TensorFlow CPU with conda is supported on 64-bit Ubuntu Linux 16.04 or later and macOS 10.12.6 or later.
TensorFlow GPU with conda is only available though version 2.4.1 (2021). For the latest TensorFlow GPU installation, follow the installation instructions on the TensorFlow website.
Install TensorFlow
-
Download and install Anaconda or Miniconda.
-
Open a terminal application and use the default bash shell.
-
Choose a name for your TensorFlow environment, such as “tf”.
-
Use the following commands to install the current release of TensorFlow. CPU-only is recommended for beginners.
TensorFlow is now installed and ready to use.
For using TensorFlow with a GPU, refer to the TensorFlow documentation, specifically the section on device placement.
CUDA versions
GPU TensorFlow uses CUDA. For a version compatibility table for GPU TensorFlow on Linux, see https://www.tensorflow.org/install/source#gpu. For Windows, see https://www.tensorflow.org/install/source_windows#gpu.
GPU TensorFlow conda packages are currently only supported for Windows or Linux.
To install GPU TensorFlow with a non-default CUDA version like 9.0, run the following commands:
Nightly builds
Advanced users may wish to install the latest nightly build of TensorFlow. These
nightly builds are unstable and are only available as pip packages on PyPI.
To install the nightly build of CPU-only TensorFlow:
Or, to install the nightly build of GPU TensorFlow on Linux or Windows:
In this blog, explore how to harness the power of TensorFlow, a versatile machine learning library, as a data scientist’s essential tool. This tutorial guides you through the seamless installation of TensorFlow using Anaconda on a Windows.
As a data scientist, one of the most important tools in your arsenal is a powerful machine learning library. Tensorflow is one such library that has gained a lot of popularity in recent years due to its ease of use and versatility. In this tutorial, we will walk you through the process of installing Tensorflow with Anaconda on Windows.
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What is Tensorflow?
Tensorflow is an open-source machine learning library developed by Google Brain Team. It was initially released in 2015 and has since become one of the most popular machine learning libraries. Tensorflow is used for a wide range of applications, including computer vision, natural language processing, and speech recognition.
What is Anaconda?
Anaconda is a popular platform used by data scientists to manage their packages and environments. It comes with a package manager called conda, which makes it easy to install, update, and remove packages. Anaconda also provides a user-friendly interface called Anaconda Navigator, which allows you to manage your packages and environments with a graphical user interface.
Step-by-Step Guide to Installing Tensorflow with Anaconda on Windows
Step 1: Install Anaconda
The first step is to download and install Anaconda on your Windows machine. You can download the latest version of Anaconda from the official website: https://www.anaconda.com/products/individual#windows. Make sure to select the appropriate version (32-bit or 64-bit) depending on your system architecture.
Step 2: Create a New Environment
Once you have installed Anaconda, the next step is to create a new environment for Tensorflow. You can use the following command to create a new environment called tensorflow:
conda create -n tensorflow python=3.8
This command will create a new environment called tensorflow with Python 3.8 installed.
Step 3: Activate the Environment
After creating the environment, you need to activate it using the following command:
conda activate tensorflow
Step 4: GPU setup (You can skip this step if you only run Tensorflow on CPU)
First you have to install the NVIDIA GPU driver if it’s not already installed
Next, use conda to install CUDA and cuDNN.
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
Step 5: Install Tensorflow
Before installing Tensorflow, make sure to upgrade your pip to the lastest version as Tensorflow requires a recent version of pip.
pip install --upgrade pip
Now that you have created and activated the environment, you can install Tensorflow using the following command:
This command will download and install the latest version of Tensorflow in your environment.
Step 6: Verify the Installation
To verify that Tensorflow is installed correctly, you can open a Python shell and run the following commands:
Verify the CPU setup:
python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
If a tensor is returned, you’ve installed TensorFlow successfully.
Verify the GPU setup:
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
If the output is a list of GPUs, Tensorflow GPU is successfully installed.
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Conclusion
In this tutorial, we have walked you through the process of installing Tensorflow with Anaconda on Windows. By following these steps, you can quickly set up a new environment for Tensorflow and start building machine learning models. Tensorflow is a powerful library that can help you solve a wide range of machine learning problems, and by using Anaconda, you can manage your packages and environments with ease.
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TensorFlow is an end-to-end open-source platform from Google for machine learning. If you want to study AI or work on an AI project, setting up a development environment with TensorFlow on a local machine will allow you have a smooth sailing. GPU (Graphics processing unit) is a specialized processor originally designed to process data simultaneously. It helps to speed up the computation of your deep-learning code. This is a complete guide to installing TensorFlow GPU on Windows. The installation involves many steps. Let’s get started.
Table of Content
- Download Nvidia graphics driver
- Check software compatibilities at Tensorflow site
- Install Visual Studio Community 2019
- Install CUDA Toolkit 12.3
- Install cuDNN 8.9.7
- Install Anaconda3
- Create virtual environment
- Install Tensorflow 2.10
- Install PyCharm
- Install additional packages
1. Download Nvidia graphics driver
If you already have a PC with GPU, you can skip this step. If you are going to get a new machine learning PC with GPU, please check CUDA enabled GPU cards before buying one. I suggest you buy a PC with a Nvidia graphic card having at least 12GB GDDR6 or GDDR6X. If you just got a new PC with Nvidia GPU, you go to Nvidia driver to download the graphic card driver. After downloading, you run the exe file to install.
2. Check software compatibilities at Tensorflow site
Normally you get the latest version when installing any software. That’s not the case here. You have to check the compatibility between software. Go to TensorFlow install guide to get software requirements. This will help you avoid headaches.
3. Install Visual Studio Community 2019
The tensorFlow install guide says “Windows Native Requires Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017, and 2019.” Please note it is not the latest version 2022. You go to Visual Studio old downloads to get 2019. After you sign in, select Community 2019 and download.
When you run the installer, select “code editor” on the right side of the window. Click “Continue” and finish the installation.
4. Install CUDA Toolkit 12.3
CUDA is a software layer that gives direct access to the Nvidia GPU’s virtual instruction set and parallel computational elements. According to the TensorFlow install guide, you need CUDA 12.3. Go to the CUDA Toolkit archive at Nvidia, and select CUDA ToolKet 12.3.0. In the next window, select Windows, the version of your operation system, and the installer type, you can download and follow the default to install it.
It is installed at C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.3\.
5. Install cuDNN 8.9.7
The Nvidia CUDA® Deep Neural Network Library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. Go to Nvidia’s cuDNN Archive, and download cuDNN v8.9.7 for CUDA 12.x.
Extract the downloaded zip file and copy three subfolders, i.e. include, lib and bin.
Paste them to the CUDA install folder at C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.3\. New files are added to CUDA.
Open the “Environment Variables” window, and add the following new two paths:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.3\bin
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.3\libnvvp
6. Install Anaconda3
Conda is an open-source package and environment management system that runs on Windows, macOS, and Linux. Conda quickly installs, runs, and updates packages and their dependencies. To install, go to Anaconda site to download Anaconda installer, click the download button at the top, or go to the bottom to select the particular version for your PC.
After you run the installer, Anaconda3 is installed at C:\Users\yourusername\anaconda3. It also installs Python 3.12 there. Remember to add the following paths in “Environment Variables”:
C:\Users\yourusername\anaconda3
C:\Users\yourusername\anaconda3\Library\bin
C:\Users\yourusername\anaconda3\Scripts
Now let’s create a shortcut to run Anaconda. In the window search bar at the bottom, search for “Anaconda Prompt” or “Anaconda Powershell Prompt” (either one is fine). Right click the icon in the Search Recent, select “Pin to start” or “Pin to taskbar.”
Click the pinned icon, a prompt window opens. It shows “(base) C:\Users\yourusername>.” The name inside the parentheses represents your current environment. The “base” is a default env.
You can run conda or pip commands to see what packages have been installed.
$conda list
$pip list
7. Create virtual environment
A virtual environment is an isolated environment for Python projects. Each project can have its own dependencies, regardless of what dependencies every other project has. Now we create a virtual environment for TensorFlow GPU support. Open the Anaconda prompt window, and enter commands:
$conda create –name tf_env python=3.12
$conda activate tf_env
The “(tf_env)” at the beginning of the prompt shows that you are in the “tf_env” env.
8. Install Tensorflow 2.10
TensorFlow requires a recent version of pip, so upgrade your pip installation to be sure you’re running the latest version. Enter the following command in the Anaconda prompt.
$pip install –upgrade pip
Please check out TensorFlow install guide for windows. Note that you should install TensorFlow 2.10.
To install TensorFlow 2.10, enter the following command in Anaconda prompt.
$pip install “tensorflow<2.11″
This command installs TensorFlow for both CPU and GPU, and other common used packages such as keras, numPy.
To check whether TensorFlow is installed correctly with GPU support, enter the following command in the Anaconda prompt:
$python -c “import tensorflow as tf; print(tf.config.list_physical_devices(‘GPU’))”
If it returns something like the following line, you have successfully installed TensorFlow with GPU support.
[PhysicalDevice(name=’/physical_device:GPU:0′, device_type=’GPU’)]
9. Install PyCharm
Now you can use Jupyter Notebook in Anaconda3 to enter and run your project. However, I prefer to use IDE, which gives me more control. PyCharm is an integrated development environment used for programming in Python. Go to Download PyCharm at jebrains site. Download the Community version and run the installer.
After installation, open PyCharm and create a new project. In the Python interpreter, check “Previously configured interpreter”. In the drop-down, select “tf_env” you just created.
If you have created a project before and want to change the virtual environment, you can go to file->settings -> project. In the Python interpreter, check using the existing environment. In drop-down, you can select other env.
10. Install additional packages
To install additional packages or modules, you can run pip in the Anaconda prompt. For example,
$pip install scikit-learn
$pip install pandas
$pip install transformers
The latest version will be installed.
For some packages, you may need to install a particular version. For example, protobuf has to be 3.19.* to be compatible with TensorFlow 2.10.
$pip install protobuf==3.19.*
Congratulations! Now you are ready to run TensorFlow machine learning projects on your PC.