---
title: CUDA 8 on Ubuntu 16
---

# Tutorial: Installing CUDA 8 on Ubuntu 16

## Introduction

CUDA is a parallel computing platform and programming model that makes using a GPU for general-purpose computing simple and elegant. Developers can program in familiar languages like C, C++, Fortran, and others, incorporating extensions through basic keywords.

These keywords allow developers to express massive parallelism and direct the compiler to the parts of the application that will run on the GPU.

Below are the steps for installing CUDA 8 and cuDNN 7.1.4 on Ubuntu 16, along with removing the latest NVIDIA packages on a new instance.

## Installation Steps

### Perform System Update and Upgrade

First, update and upgrade the system packages:

```bash
    apt update
	apt upgrade
	reboot

```

**Remove the existing installed Cuda Packages**

```bash
    apt remove cuda-*
	apt autoremove
```

**Install Nvidia driver which supports Tesla v100**

```bash
    wget http://us.download.nvidia.com/tesla/440.33.01/NVIDIA-Linux-x86_64-440.33.01.run
	chmod +x NVIDIA-Linux-x86_64-440.33.01.run
	./NVIDIA-Linux-x86_64-440.33.01.run

```

**To check nvidia driver output**

```bash
nvidia-smi

```

ignore cuda version which is seen in this output

***Install Cuda8 debian packages***

```bash

        wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/patches/2/cuda-repo-ubuntu1604-8-0-local-cublas-performance-update_8.0.61-1_amd64-deb
        dpkg -i cuda-r	epo-ubuntu1604-8-0-local-cublas-performance-update_8.0.61-1_amd64-deb


```

**Reboot the system**

```bash
reboot

```

**To check Cuda version**

```bash
    nvcc --version

```
### To Install cuDNN Version 7.1.4 for CUDA

1. Download the three required packages for Ubuntu 16 from the [cuDNN archive](https://developer.nvidia.com/rdp/cudnn-archive).

2. Follow the installation and verification steps provided in the official [cuDNN installation guide](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-deb) for detailed instructions.

These steps will ensure that cuDNN is properly installed and ready to work with CUDA 8 for your machine learning applications.


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