# Ubuntu 14.04 Environment Configuration

## 1. Check GPU device recognition
```
  $ sudo lspci | grep NVIDIA
  3D controller: NVIDIA Corporation GK210GL [Tesla K80] indicates recognition as K80
  3D controller: NVIDIA Corporation GP102GL [Tesla P40] (rev a1) indicates recognition as P40
```

## 2. Obtain cuda network source and configure:

NVidia official source address http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/

```
  $ wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_8.0.44-1_amd64.deb
  $ sudo dpkg -i cuda-repo-ubuntu1404_8.0.44-1_amd64.deb
  $ sudo apt-get update
```

## 3. Install cuda 8.0
Before installation, please use uname -a to detect the current kernel version, and then make sure the kernel-header package of the corresponding version has been installed. Otherwise, the driver cannot be compiled normally.
```
  $ uname -a
  $ Linux X-X-X-X 3.13.0-123-generic #172-Ubuntu SMP Mon
  $ sudo apt search 3.13.0-123-generic
  $ p   linux-cloud-tools-3.13.0-123-generic   - Linux kernel version specific cloud tools for version 3.13.0-123                      
  $ p   linux-headers-3.13.0-123-generic      - Linux kernel headers for version 3.13.0 on 64 bit x86 SMP                             
  $ p   linux-headers-3.13.0-123-generic:i386   - Linux kernel headers for version 3.13.0 on 32 bit x86 SMP
  $ sudo apt-get install  linux-headers-3.13.0-123-generic 
```
Install cuda
```
  $ sudo apt-get install cuda-8.0
```

### 3.1 Check driver status
<code>
$ sudo nvidia-smi 
</code>When the following output appears, it indicates that the GPU driver is normal:
![img](https://cdn.udelivrs.com/2025/04/714ff4511c479122e2af59d454b6e31f_1744190227016.jpg)<br>

## 4. Test GPU basic function (optional)

### 4.1 Add LD path
<code>
  $ export LD_LIBRARY_PATH="/usr/local/cuda-7.5/lib64:/usr/lib64/:$LD_LIBRARY_PATH"
</code>

### 4.2 Install cuda examples
```
  $ cd /usr/local/cuda/bin
  $ sh cuda-install-samples-8.0.sh ~/cuda-test/
  $ cd ~/cuda-test/NVIDIA_CUDA-8.0_Samples
  $ make
  $ ./bin/x86_64/linux/release/deviceQuery to get the device status
  $ ./bin/x86_64/linux/release/bandwidthTest to test device bandwidth
```
In case of lnvcuvid error during compilation, you can execute:
```
$ find . -type f -execdir sed -i 's/UBUNTU_PKG_NAME = "nvidia-367"/UBUNTU_PKG_NAME = "nvidia-375"/g' '{}' \    
```
where nvidia-375 is the version of the currently installed driver

## 5. Install cudnn
Optional. Note: different AI frameworks support different versions of cudnn

### 5.1 Download cudnn software package
https://developer.nvidia.com/cudnn, you need to register a nvidia account before you can download

### 5.2 Install
This example uses cudnn5.1, because TensorFlow currently only supports 5.1.

Ubuntu can select cuDNN v5.1 Runtime Library for Ubuntu14.04 (Deb)
<code>
  $ sudo dpkg -i libcudnn5_5.1.10-1+cuda8.0_amd64.deb
</code>

## 5. Close Ubuntu's automatic kernel update and NVidia Tools
Recommended operation
<code>
$ sudo vim /etc/apt/apt.conf.d/10periodic
</code>
Change APT::Periodic::Update-Package-Lists "1"; to APT::Periodic::Update-Package-Lists "0";
To prevent Ubuntu from automatically updating packages

## FAQ

### 1. Why does nvidia-smi show 100% GPU utilization?

This problem is caused by the system's inaccurate reading of the GPU status information. The following command can correct it and make the system read the command correctly.
<code>
    # nvidia-smi -pm 1
</code>

### 2. In addition to self-installation, are there other ways to obtain the driver image?

You can submit a ticket or contact staff to obtain the image including GPU driver and Cuda environment made by SCloud, which can save manual installation time.