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70 changes: 13 additions & 57 deletions docs/docs/how-tos/use-gpus.mdx
Original file line number Diff line number Diff line change
Expand Up @@ -30,68 +30,24 @@ Overview of using GPUs on Nebari including server setup, environment setup, and

## 2. Creating environments

By default, `conda-store` will build CPU-compatible packages. To build GPU-compatible packages, we do the following.
### Build a GPU-compatible environment
By default, `conda-store` will build CPU-compatible packages. To build GPU-compatible packages, we have two options:
1. **Create the environment specification using `CONDA_OVERRIDE_CUDA` (recommended approach)**:

Conda-store provides an alternate mechanism to enable GPU environments via the setting of an environment variable as explained in the [conda-store docs](https://conda.store/conda-store-ui/tutorials/create-envs#set-environment-variables).
While creating a new config, click on the `**GUI <-> YAML**` Toggle to edit yaml config.
```
channels:
- pytorch
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@marcelovilla marcelovilla Oct 17, 2025

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The pytorch channel has been deprecated and does not have recent versions of PyTorch: pytorch/pytorch#138506

- conda-forge
dependencies:
- pytorch
- ipykernel
variables:
CONDA_OVERRIDE_CUDA: "12.1"
```
Alternatively, you can configure the same config using the UI.

Add the `CONDA_OVERRIDE_CUDA` override to the variables section to tell conda-store to build a GPU-compatible environment.
conda-store provides an alternate mechanism to enable GPU environments via the setting of an environment variable as explained in the [conda-store docs](https://conda.store/conda-store-ui/tutorials/create-envs#set-environment-variables).
Create the environment specification using `CONDA_OVERRIDE_CUDA` by creating a new environment and clicking on the `**GUI <-> YAML**` toggle to edit the yaml config.
```yaml
channels:
- conda-forge
dependencies:
- pytorch
- ipykernel
variables:
CONDA_OVERRIDE_CUDA: "12.4"
```
Alternatively, you can configure the same variable using the UI.

:::note
At the time of writing this document, the latest CUDA version was showing as `12.1`. Please follow the steps below to determine the latest override value for the `CONDA_OVERRIDE_CUDA` environment variable.

Please ensure that your choice from PyTorch documentation is not greater than the highest supported version in the `nvidia-smi` output (captured above).
At the time of writing this document, the latest CUDA version was showing as `12.4`. Please follow the steps [above](#understanding-gpu-setup-on-the-server) to determine the highest supported version to use as an override value for the `CONDA_OVERRIDE_CUDA` environment variable.
:::

2. **Create the environment specification based on recommendations from the PyTorch documentation**:
You can check [PyTorch documentation](https://pytorch.org/get-started/locally/) to get a quick list of the necessary CUDA-specific packages.
Select the following options to get the latest CUDA version:
- PyTorch Build = Stable
- Your OS = Linux
- Package = Conda
- Language = Python
- Compute Platform = 12.1 (Select the version that is less than or equal to the `nvidia-smi` output (see above) on your server)

![pytorch-linux-conda-version](/img/how-tos/pytorch-linux-conda-version.png)

The command `conda install` from above is:
```
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
```
The corresponding yaml config would be:
```
channels:
- pytorch
- nvidia
- conda-forge
dependencies:
- pytorch
- pytorch-cuda==12.1
- torchvision
- torchaudio
- ipykernel
variables: {}
```
:::note
The order of the channels is respected by conda, so keep pytorch at the top, then nvidia, then conda-forge.

You can use `**GUI <-> YAML**` Toggle to edit the config.


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The PyTorch docs do not show installation instructions for conda anymore:

Image

## 3. Validating the setup
You can check that your GPU server is compatible with your conda environment by opening a Jupyter Notebook, loading the environment, and running the following code:
```
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