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Add missing -lp shortcut for --launch-profile in dotnet run documentation #50128

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61510fb
Fix outdated CUDA version and add GPU setup note for Model Builder
abhiramch018 Nov 25, 2025
883a306
Add missing -lp shortcut for --launch-profile in dotnet run documenta…
abhiramch018 Nov 25, 2025
33acc61
Fix OPS warnings and formatting in Model Builder GPU documentation
abhiramch018 Nov 25, 2025
c66e428
Add shorthand for launch profile in dotnet run
meaghanlewis Nov 25, 2025
d0a32ef
Apply reviewer feedback: reorder -lp alias and remove redundant note
abhiramch018 Nov 26, 2025
6d48e94
Apply reviewer feedback: reorder -lp alias and remove redundant note
abhiramch018 Nov 26, 2025
4ba0349
Add note that additional dependencies only work for framework-depende…
abhiramch018 Nov 26, 2025
64c1820
Trigger Markdownlint recheck (#48014)
abhiramch018 Nov 26, 2025
c0ca342
Finalize note formatting for framework-dependent apps clarification (…
abhiramch018 Nov 26, 2025
42cc01d
Remove extra line before '--no-build' option
meaghanlewis Nov 26, 2025
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11 changes: 9 additions & 2 deletions docs/core/tools/dotnet-run.md
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Expand Up @@ -111,10 +111,17 @@

- [!INCLUDE [interactive](../../../includes/cli-interactive.md)]

- **`--launch-profile <NAME>`**
- **`--launch-profile <NAME>` | `-lp <NAME>`**

Specifies the name of the launch profile (if any) to use when launching the application.
Launch profiles are defined in the *launchSettings.json* file and are typically named `Development`, `Staging`, or `Production`.
For more information, see [Working with multiple environments](/aspnet/core/fundamentals/environments).

> [!NOTE]
> The `-lp` shortcut is equivalent to `--launch-profile`.
> Both options specify which launch profile from *launchSettings.json* to use when running the app.

The name of the launch profile (if any) to use when launching the application. Launch profiles are defined in the *launchSettings.json* file and are typically called `Development`, `Staging`, and `Production`. For more information, see [Working with multiple environments](/aspnet/core/fundamentals/environments).

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docs/core/tools/dotnet-run.md:124 MD012/no-multiple-blanks Multiple consecutive blank lines [Expected: 1; Actual: 2] https://github.com/DavidAnson/markdownlint/blob/v0.39.0/doc/md012.md
- **`--no-build`**

Doesn't build the project before running. It also implicitly sets the `--no-restore` flag.
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71 changes: 44 additions & 27 deletions docs/machine-learning/how-to-guides/install-gpu-model-builder.md
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@@ -1,7 +1,7 @@
---
title: How to install GPU support in Model Builder
description: Learn how to install GPU support in Model Builder
ms.date: 02/28/2023
ms.date: 11/25/2025
author: luisquintanilla
ms.author: luquinta
ms.topic: how-to
Expand All @@ -22,52 +22,69 @@ Learn how to install the GPU drivers to use your GPU with Model Builder.
- [Model Builder Visual Studio extension](install-model-builder.md). The extension is built into Visual Studio as of version 16.6.1.
- Make sure the appropriate [driver](https://www.nvidia.com/drivers) is installed for the GPU.

### Image classification only
### Image classification and object detection

- NVIDIA developer account. If you don't have one, [create a free account](https://developer.nvidia.com/developer-program).
- 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-archive) from the cuDNN archive. You cannot have multiple versions of cuDNN installed. After downloading the 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 [CUDA **v11.8 or later**](https://developer.nvidia.com/cuda-downloads).

> [!NOTE]
> Using older CUDA versions such as 10.1 may cause *"no kernel found"* or similar errors when running object detection models in Model Builder.
> CUDA 11.8 or newer provides better compatibility with the TensorFlow GPU runtime used by Model Builder.

- Install the corresponding **cuDNN** library version for your installed CUDA version from the [cuDNN archive](https://developer.nvidia.com/rdp/cudnn-archive).
After downloading and unpacking the cuDNN zip file, copy
`\<CUDNN_zip_files_path>\cuda\bin\cudnn64_*.dll`
to
`\<YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.x\bin`.

> [!TIP]
> You cannot have multiple versions of cuDNN installed simultaneously.
> Ensure only one version matching your CUDA installation is present in your system path.

---

## Troubleshooting

**What if I don't have a GPU installed locally?**

Deep learning scenarios tend to run faster on GPUs.

Some scenarios like image classification support training on Azure GPU VMs.
Deep learning scenarios tend to run faster on GPUs.
Some scenarios, such as image classification, support training on **Azure GPU VMs**.
However, if local GPUs or Azure are not an option, these scenarios can still run on CPU — though 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?**

***Check GPU from Settings***

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 are shown under **Display information**.
1. Right-click the Windows start menu icon and select **Settings**.
2. Select **System**.
3. Choose **Display**, then scroll to **Related settings**.
4. Select **Advanced display**. Your GPU's make and model appear 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 is shown.
1. Right-click the Windows start menu icon and select **Task Manager**.
2. Go to the **Performance** tab.
3. In the last pane, select **GPU** (usually at the bottom).
4. The top-right corner displays information about your GPU.

**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 the appropriate [driver](https://www.nvidia.com/drivers) for your GPU.
**I don't see my GPU in Settings or Task Manager, but I know I have an NVIDIA GPU.**

**How do I see what version of CUDA I have?**
1. Open **Device Manager**.
2. Expand **Display adapters**.
3. Install the appropriate [driver](https://www.nvidia.com/drivers) for your GPU.

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

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

**cuda is not available, please confirm you have a cuda-supported gpu**
1. Open a PowerShell or Command Prompt window.
2. Run the command:

1. Open the [GeForce Experience](https://www.nvidia.com/en-us/geforce/geforce-experience/) app.
1. The application should show installed and available driver updates. If you have trouble seeing updates, you can get the latest drivers from [https://www.nvidia.com/geforce/drivers/](https://www.nvidia.com/Download/index.aspx).
1. Install the latest drivers.
```bash
nvcc --version
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