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Update cuDNN link #44444

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Jan 22, 2025
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Update cuDNN link #44444

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24 changes: 12 additions & 12 deletions docs/machine-learning/how-to-guides/install-gpu-model-builder.md
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Expand Up @@ -14,8 +14,8 @@ Learn how to install the GPU drivers to use your GPU with Model Builder.

## Hardware requirements

- At least one CUDA compatible GPU. For a list of compatible GPUs, see [NVIDIA's guide](https://developer.nvidia.com/cuda-gpus).
- At least 6GB of dedicated GPU memory.
- At least one CUDA-compatible GPU. For a list of compatible GPUs, see [NVIDIA's guide](https://developer.nvidia.com/cuda-gpus).
- At least 6 GB of dedicated GPU memory.

## Prerequisites

Expand All @@ -25,9 +25,9 @@ Learn how to install the GPU drivers to use your GPU with Model Builder.
### Image classification only

- NVIDIA developer account. If you don't have one, [create a free account](https://developer.nvidia.com/developer-program).
- Install dependencies
- Install dependencies:
- Install [CUDA v10.1](https://developer.nvidia.com/cuda-10.1-download-archive-update2). Make sure you install CUDA v10.1, not any other newer version.
- Install [cuDNN v7.6.4 for CUDA 10.1](https://developer.nvidia.com/rdp/cudnn-download). You cannot have multiple versions of cuDNN installed. After downloading cuDNN v7.6.4 zip file and unpacking it, copy `<CUDNN_zip_files_path>\cuda\bin\cudnn64_7.dll` to `<YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin`.
- Install [cuDNN for CUDA 10.1](https://developer.nvidia.com/rdp/cudnn-download). (You can't have multiple versions of cuDNN installed.)

## Troubleshooting

Expand All @@ -37,7 +37,7 @@ Deep learning scenarios tend to run faster on GPUs.

Some scenarios like image classification support training on Azure GPU VMs.

However, if local GPUs or Azure are not an option for you, these scenarios also run on CPU. Note however that training times are significantly longer.
However, if local GPUs or Azure are not an option for you, these scenarios also run on CPU. However, training times are significantly longer.

**How do I know what GPU I have?**

Expand All @@ -46,25 +46,25 @@ However, if local GPUs or Azure are not an option for you, these scenarios also
1. Right-click on the Windows start menu icon and select **Settings**.
1. Select **Settings** > **System**
1. Select **Display** and scroll down to **Related settings**.
1. Select **Advanced display**. Your GPU’s make and model should be shown under **Display information**.
1. Select **Advanced display**. Your GPU's make and model are shown under **Display information**.

***Check GPU from Task Manager***

1. Right-click on the Windows start menu icon and select **Task Manager**.
1. Select **Performance**.
1. In the last pane of the tab, choose **GPU**. If this option is available, it will likely be at the bottom of the list.
1. In the top right corner of the GPU selection, information about your computer’s GPU will be visible.
1. In the top right corner of the GPU selection, information about your computer's GPU is shown.

**I don't see my GPU in Settings or Task Manager but I know I have an NVIDIA GPU.**

1. Open Device Manager
1. Look at Display adapters
1. Install appropriate [driver](https://www.nvidia.com/drivers) for your GPU.
1. Open Device Manager.
1. Look at Display adapters.
1. Install the appropriate [driver](https://www.nvidia.com/drivers) for your GPU.

**How do I see what version of CUDA I have?**

1. Open a PowerShell or command line window
1. Type in `nvcc --version`
1. Open a PowerShell or command line window.
1. Run the command `nvcc --version`.

**cuda is not available, please confirm you have a cuda-supported gpu**

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