└── README.md


/README.md:
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 1 | # Setup-NVIDIA-GPU-for-Deep-Learning
 2 | 
 3 | ## Step 1: NVIDIA Video Driver
 4 | 
 5 | You should install the latest version of your GPUs driver. You can download drivers here:
 6 |  - [NVIDIA GPU Drive Download](https://www.nvidia.com/Download/index.aspx)
 7 | 
 8 | ## Step 2: Visual Studio C++
 9 | 
10 | You will need Visual Studio, with C++ installed. By default, C++ is not installed with Visual Studio, so make sure you select all of the C++ options.
11 |  - [Visual Studio Community Edition](https://visualstudio.microsoft.com/vs/community/)
12 | 
13 | ## Step 3: Anaconda/Miniconda
14 | 
15 | You will need anaconda to install all deep learning packages
16 |  - [Download Anaconda](https://www.anaconda.com/download/success)
17 | 
18 | ## Step 4: CUDA Toolkit
19 | 
20 |  - [Download CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive)
21 | 
22 | ## Step 5: cuDNN
23 | 
24 |  - [Download cuDNN](https://developer.nvidia.com/rdp/cudnn-archive)
25 | 
26 | 
27 | ## Step 6: Install PyTorch 
28 | 
29 |  - [Install PyTorch](https://pytorch.org/get-started/locally/)
30 | 
31 | 
32 | 
33 | 
34 | ## Finally run the following script to test your GPU
35 | 
36 | ```python
37 | import torch
38 | 
39 | print("Number of GPU: ", torch.cuda.device_count())
40 | print("GPU Name: ", torch.cuda.get_device_name())
41 | 
42 | 
43 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
44 | print('Using device:', device)
45 | ```
46 | 


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