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DLIP
  • Introduction
  • Prerequisite
  • Image Processing Basics
    • Notes
      • Thresholding
      • Spatial Filtering
      • Masking with Bitwise Operation
      • Model n Calibration
    • Tutorial
      • Tutorial: Install OpenCV C++
      • Tutorial: Create OpenCV Project
      • Tutorial: C++ basics
      • Tutorial: OpenCV Basics
      • Tutorial: Image Watch for Debugging
      • Tutorial: Spatial Filter
      • Tutorial: Thresholding and Morphology
      • Tutorial: Camera Calibration
      • Tutorial: Color Image Processing
      • Tutorial: Edge Line Circle Detection
      • Tutorial: Corner Detection and Optical Flow
      • Tutorial: OpenCV C++ Cheatsheet
      • Tutorial: Installation for Py OpenCV
      • Tutorial: OpenCv (Python) Basics
    • LAB
      • Lab Report Template
      • Lab Report Grading Criteria
      • LAB Report Instruction
      • LAB: Grayscale Image Segmentation
        • LAB: Grayscale Image Segmentation -Gear
        • LAB: Grayscale Image Segmentation - Bolt and Nut
      • LAB: Color Image Segmentation
        • LAB: Facial Temperature Measurement with IR images
        • LAB: Magic Cloak
      • LAB: Straight Lane Detection and Departure Warning
      • LAB: Dimension Measurement with 2D camera
      • LAB: Tension Detection of Rolling Metal Sheet
  • Deep Learning for Perception
    • Notes
      • Lane Detection with Deep Learning
      • Overview of Deep Learning
        • Object Detection
        • Deep Learning Basics: Introduction
        • Deep Learning State of the Art
        • CNN, Object Detection
      • Perceptron
      • Activation Function
      • Optimization
      • Convolution
      • CNN Overview
      • Evaluation Metric
      • LossFunction Regularization
      • Bias vs Variance
      • BottleNeck Unit
      • Object Detection
      • DL Techniques
        • Technical Strategy by A.Ng
    • Tutorial - PyTorch
      • Tutorial: Install PyTorch
      • Tutorial: Python Numpy
      • Tutorial: PyTorch Tutorial List
      • Tutorial: PyTorch Example Code
      • Tutorial: Tensorboard in Pytorch
      • Tutorial: YOLO in PyTorch
        • Tutorial: Yolov8 in PyTorch
        • Tutorial: Train Yolo v8 with custom dataset
          • Tutorial: Train Yolo v5 with custom dataset
        • Tutorial: Yolov5 in Pytorch (VS code)
        • Tutorial: Yolov3 in Keras
    • LAB
      • Assignment: CNN Classification
      • Assignment: Object Detection
      • LAB: CNN Object Detection 1
      • LAB: CNN Object Detection 2
      • LAB Grading Criteria
    • Tutorial- Keras
      • Train Dataset
      • Train custom dataset
      • Test model
      • LeNet-5 Tutorial
      • AlexNet Tutorial
      • VGG Tutorial
      • ResNet Tutorial
    • Resource
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  • Must Read Papers
    • AlexNet
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    • Inception
    • MobileNet
    • SSD
    • ShuffleNet
    • Recent Methods
  • DLIP Project
    • Report Template
    • DLIP 2021 Projects
      • Digital Door Lock Control with Face Recognition
      • People Counting with YOLOv4 and DeepSORT
      • Eye Blinking Detection Alarm
      • Helmet-Detection Using YOLO-V5
      • Mask Detection using YOLOv5
      • Parking Space Management
      • Vehicle, Pedestrian Detection with IR Image
      • Drum Playing Detection
      • Turtle neck measurement program using OpenPose
    • DLIP 2022 Projects
      • BakeryCashier
      • Virtual Mouse
      • Sudoku Program with Hand gesture
      • Exercise Posture Assistance System
      • People Counting Embedded System
      • Turtle neck measurement program using OpenPose
    • DLIP Past Projects
  • Installation Guide
    • Installation Guide for Pytorch
      • Installation Guide 2021
    • Anaconda
    • CUDA cuDNN
      • CUDA 10.2
    • OpenCV
      • OpenCV Install and Setup
        • OpenCV 3.4.13 with VS2019
        • OpenCV3.4.7 VS2017
        • MacOS OpenCV C++ in XCode
      • Python OpenCV
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        • Cheat Sheet
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        • Install
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    • Ubuntu
      • Ubuntu 18.04 Installation
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  • Programming
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      • Python Tutorial - Tips
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      • Python Tutorial - List Tuple, Dic, Set
    • Markdown
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    • Github
      • Create account
      • Tutorial: Github basic
      • Tutorial: Github Desktop
    • Keras
      • Tutorial Keras
      • Cheat Sheet
    • PyTorch
      • Cheat Sheet
      • Autograd in PyTorch
      • Simple ConvNet
      • MNIST using LeNet
      • Train ConvNet using CIFAR10
  • Resources
    • Useful Resources
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On this page
  • CUDA cuDNN
  • Installation of CUDA, cuDNN
  • 1. Installation of NVIDIA Graphic Card Driver
  • 2. Install CUDA & CuDNN using Conda
  • Step 1. Find supported CUDA version
  • Step 2. Install CUDA and cuDNN via CONDA

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  1. Installation Guide

CUDA cuDNN

PreviousAnacondaNextCUDA 10.2

Last updated 1 year ago

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CUDA cuDNN

Installation of CUDA, cuDNN

(updated 2024.4)

For DLIP 2024 course,

CUDA=11.8, PyTorch 2.1

This covers the installation of CUDA, cuDNN on Windows 10/11.

Here, we assume that you have a CUDA-compatible GPU already installed on your PC.

The order of installation is

  1. NVIDIA Graphic Card Driver

  2. CUDA toolkit and cuDNN


1. Installation of NVIDIA Graphic Card Driver

Prerequisite: Visual Studio Community

Visual Studio is a Prerequisite for CUDA Toolkit

Visual Studio Community is required for the installation of Nvidia CUDA Toolkit. If you attempt to download and install CUDA Toolkit for Windows without having first installed Visual Studio, you will get a message for the installation.

Step 1: Find Your GPU Model

You can check your GPU Card in Window Device Manager

항목
Graphics Card Info
Window Version

Short Key

Win Key → Device Manager (장치관리자)

Win key→ System (또는 내PC 우클릭 → 속성)

Step 2: Check If Graphic Driver is Installed

Go to anaconda prompt Admin Mode.

You can also use r Window Command Prompt

Start > All Programs > Accessories > Command Prompt

nvidia-smi

If you have the summary of CUDA version, you already have installed the driver.

Here, CUDA version 11.6 means, the driver can support up to CUDA ~11.6.

If you don't see any information, go to Step 3. Otherwise Skip Step 3

Step 3: Download NVIDIA Graphic Driver

Download Site: https://www.nvidia.co.kr/Download/index.aspx?lang=kr

Select your Graphic Card Model and Window Version. Then, download.

It does not matter whether you select (a) GRD(game-ready driver) (b) NVIDIA Studio Driver.

Now, Install. There is NO need to select GeForce Experience. Choose the default Settings.

Check if you have installed, check the driver version.

Go to anaconda prompt Admin Mode. Then, go to the environment you want to work with.

conda activate py39
nvidia-smi

2. Install CUDA & CuDNN using Conda

Step 1. Find supported CUDA version

Depending on your graphic card, there is a minimum version for CUDA SDK support.

First, find the range of CUDA SDK versions for your Graphic Card (Compatible version)

Example:

  • GTX 1080 is PASCAL(v6.1) and it is supported by CUDA SDK from 8.0 to 12.4

  • RTX 4xxx is AdaLovelace(v8.9) and it is supported by CUDA SDK from 11.8 to 12.4

For most GPUs, CUDA SDK >11.x will work fine

BUT, if you use RTX4xxx, you may need to install CUDA SDK > 11.8

Step 2. Install CUDA and cuDNN via CONDA

It is recommended to install specific CUDA version in the selected Python environment.

CUDA=11.8, for DLIP 2024-1

CUDA=11.2, for DLIP 2023-1

CUDA=10.2.89, for DLIP 2023-1

Run Anaconda Prompt(admistration).

Activate conda virtual environment. Then, Install specific CUDA version

[$ENV_NAME] is your environment name. e.g. conda activate py39

DLIP 2024-1

#conda activate [$ENV_NAME]
conda activate py39
    
# CUDA 11.8 & CuDNN
conda install -c anaconda cudatoolkit=11.8 cudnn 

DLIP 2022-1

#conda activate [$ENV_NAME]
conda activate py39
    
# CUDA 10.2 & CuDNN
conda install -c anaconda cudatoolkit==10.2.89 cudnn 

Important Note

Depending on your CUDA version, the minimum version of PyTorch is determined.

For example

  • CUDA 11.6 supports PyTorch 1.13 or higher

  • CUDA 11.8 supports PyTorch 2.0 or higher

Follow:

If Anaconda is not installed, see here

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See here for more detailed instruction by NVIDIA
Anaconda Installation
See here for detail
See here for CUDA and PyTorch Version matching
How to install Visual Studio Community