# How To Install TensorFlow GPU (With Detailed Steps)

In this blog, we will understand how to install tensorflow on an Nvidia GPU system. Let us look at the various steps involved in the process of installation:

When I started working on Deep Learning (DL) models, I found that the amount of time needed to train these models on a CPU was too high and it hinders your research work if you are creating multiple models in a day. Later I heard about the superior performance of the GPUs, so I decided to get one for myself.

One of the basic problems that I initially faced was the installation of TensorFlow GPU. After a lot of trouble and a burnt motherboard (not due to TensorFlow), I learnt how to do it.

A few days earlier I spoke to someone who was facing a similar issue, so I thought I might help people who are stuck in a similar situation, by writing down the steps that I followed to get it working.

### Step 1: Uninstall Nvidia

This may not look like a necessary step, but believe me, it will save you a lot of trouble if there are compatibility issues between your current driver and the CUDA. Once you login to your system, go to the control panel, and then to the ‘Uninstall a program’ link. Then scroll below to the section with programs that have been published by the NVIDIA Corporation.

Here, you uninstall all the NVIDIA programs. Do not worry if you have some drivers, they can be updated later once you finish the setup. Once you have removed all the programs, go to the C drive and check all the program files folders and delete any NVIDIA folders in them.

### Step 2: Install Visual Studio

In the next step, we will install the visual studio community from here

Here, make sure that you select the community option.

Note: Installing the Visual Studio Community is not a prerequisite. Any other IDE or no IDE could be used for running TensorFlow with GPU as well.

### Step 3: Install CUDA

This is a tricky step, and before you go ahead and install the latest version of CUDA (which is what I initially did), check the version of CUDA that is supported by the latest TensorFlow, by using this link. I have a windows based system, so the corresponding link shows me that the latest supported version of CUDA is 9.0 and its corresponding cuDNN version is 7.

Once the download is complete, install the base installer first followed by the patches starting from Patch 1 to Patch 4. If you face any issue during installation, please check the forums using this link.

### Step 4: Install cuDNN

Then choose the appropriate OS option for your system.

This will download a zip file on to your system. Once you unzip the file, you will see three folders in it: bin, include and lib. Extract these three files onto your desktop.

Once you have extracted them. Go to the C drive, there you will find a folder named NVIDIA GPU Computing Toolkit. Inside this, you will find a folder named CUDA which has a folder named v9.0. In this folder, you can see that you have the same three folders: bin, include and lib. Copy the contents of the bin folder on your desktop to the bin folder in the v9.0 folder. Similarly, transfer the contents of the include and lib folders. Once you are done with the transfer of the contents, go to the start menu and search for ”edit the environment variables”. Click on the search result and open the System Properties window and within it open the Advanced tab.

Now click on the 'Environment Variables',

and under System Variables look for PATH, and select it and then click edit. Add the following two paths to the path variable:

• C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin
• C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\libnvvp

Once you are done with this, you can download Anaconda, and if you already have it, then create a Python 3.5 environment in it.

### Step 5: Install Anaconda

To install Anaconda on your system, visit this link. Here choose your OS and the Python 3.6 version, then click on download. Follow the instructions in the setup manager and complete the installation process. Once you have completed the installation of Anaconda. Create a python 3.5 environment using the following command in the terminal or anaconda prompt.

conda create -n tensorflow python=3.5

Once the environment is created, activate it using the following command in the terminal or anaconda prompt:

activate tensorflow

### Step 6: Install TensorFlow-GPU

Once you have the environment ready, you can install the tensorflow GPU using the following command in the terminal or anaconda prompt:

pip install --ignore-installed --upgrade tensorflow-gpu

You will need to specify the version of tensorflow-gpu, if you are using a different version of CUDA and cuDNN than what is shown in this blog. The above line installs the latest version of tensorflow by default. If you have any issues while installing tensorflow, please check this link.

### Step 7: Install Keras

Once the tensorflow is installed, you can install Keras. Using the following command:

pip install keras

Once the installation of keras is successfully completed, you can verify it by running the following command on Spyder IDE or Jupyter notebook:

import keras

Some people might face an issue with the msg package. In case you do, you can install it using the following command

conda install -c anaconda msgpack-python

### Conclusion

I hope you have successfully installed the tensorflow- gpu on your system. In this article, we have covered many important aspects like how to install Anaconda, how to install tensorflow, how to install keras, by installing tensorflow gpu on windows. We started by uninstalling the Nvidia GPU system and progressed to learning how to install tensorflow gpu.

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