# Installation

After the usual R package installation, `torch` requires installing other 2 libraries: LibTorch and LibLantern. They are automatically installed by detecting information about you OS if you are using `torch` in interactive mode. If you are running `torch` in non-interactive environments you need to set the `TORCH_INSTALL` env var to 1, so it’s automatically installed or manually call `torch::install_torch()`.

Starting from torch v0.9.1.9000 it’s possible to install torch from pre-built package binaries available in a custom CRAN-like repository. This binaries include all shared objects necessary to run torch, thus it’s not required to install any additional software. See the Install from pre-built binaries session for more information.

We have provide pre-compiled binaries for all major platforms and you can find specific installation instructions below.

## Windows

### CPU

If you don’t have a GPU or want to install the CPU version of `torch`, you can install with:

    install.packages("torch")

Some Windows distributions don’t have the Visual Studio runtime pre-installed and you will observe an error like:

    Error in cpp_lantern_init(normalizePath(install_path())): C:\Users\User\Documents\R\R-4.0.2\library\torch\deps\lantern.dll - The specified module could not be found.

See here for instructions on how to install it.

### GPU

torch has very specific requirements in terms of CUDA and CUDNN versions it supports. We recomment installing torch using pre-built binaries. See (#pre-built) for more information.

Since version 0.1.1 torch supports GPU installation on Windows. In order to use GPU’s with torch you need to:

- Have a CUDA compatible NVIDIA GPU. You can find if you have a CUDA compatible GPU here.

- Have properly installed the NVIDIA CUDA toolkit version 12.8. For CUDA v12.8, follow the installation instructions here. Note: The version of the CUDA toolkit must match exactly what’s mentioed above.

- Have installed cuDNN - a version compatible with CUDA v12.8. Follow the installation instructions available here.

Once you have installed all pre-requisites you can install `torch` with:

    install.packages("torch")

If you have followed default installation locations we will detect that you have CUDA software installed and automatically download the GPU enabled Lantern binaries. You can also specify the `CUDA` env var with something like `Sys.setenv(CUDA="12.8")` if you want to force an specific version of the CUDA toolkit. Refer to the compatibility matrix to select your version.

## MacOS

### CPU

We support CPU builds of torch on MacOS. On MacOS you can install torch with:

    install.packages("torch")

### GPU

On Apple Silicon architecture we support GPU through MPS:

    install.packages("torch")

## Linux

### CPU

To install the cpu version of `torch` you can run:

    install.packages("torch")

### GPU

torch has very specific requirements in terms of CUDA and CUDNN versions it supports. We recommand installing torch using pre-built binaries. See (#pre-built) for more information.

To install the GPU version of `torch` on linux you must verify that:

- You have a NVIDIA CUDA compatible GPU. You can find if you have a CUDA compatible GPU here.

- You have correctly installed the NVIDIA CUDA Toolkit versions 12.6, 12.8, or 12.9, follow the instructions here.

- You have installed cuDNN (a version compatible with your CUDA version). Follow the installation instructions available here.

Once you have installed all pre-requisites you can install `torch` with:

    install.packages("torch")

If you have followed default installation locations we will detect that you have CUDA software installed and automatically download the GPU enabled Lantern binaries. You can also specify the `CUDA` env var with something like `Sys.setenv(CUDA="12.8")` if you want to force an specific version of the CUDA toolkit. Refer to the compatibility matrix to select your version.

## Installing from pre-built binaries

As of torch v0.9.1.9000 it’s now possible to install torch from pre-built package binaries from a CRAN like repository hosted on Google Cloud Storage. We currently provide pre-built binaries for CPU (for macOS, Linux and Windows) and GPU (Windows and Linux).

Packages provided by the CRAN-like repository bundles all necessary for its execution, including CUDA and CUDNN in the case of the GPU builds. This means that by installing it you agree with the included software licenses. See PyTorch’s LICENSE and CUDA libraries EULA.

When installing from the pre-built binaries, you don’t need to manually install CUDA or cuDNN. If you have CUDA installed, it doesn’t need to match the installation ‘kind’ chosen below.

To install from the pre-built binaries, you can use the following:

    options(timeout = 600) # increasing timeout is recommended since we will be downloading a 2GB file.
    # For Windows and Linux: "cpu", "cu128" are the only currently supported
    # For MacOS the supported are: "cpu-intel" or "cpu-m1"
    kind <- "cu128"
    version <- available.packages()["torch","Version"]
    options(repos = c(
      torch = sprintf("https://torch-cdn.mlverse.org/packages/%s/%s/", kind, version),
      CRAN = "https://cloud.r-project.org" # or any other from which you want to install the other R dependencies.
    ))
    install.packages("torch")

## Using cudatoolkit R packages

Instead of installing the full CUDA toolkit on your system, you can use the `cuda12.6`, `cuda12.8`, or `cuda12.9` R packages to provide the necessary CUDA runtime libraries. These packages bundle the CUDA shared libraries so torch can use GPU acceleration without a system-wide CUDA installation.

    # Install the cudatoolkit R package for your desired CUDA version
    install.packages("cuda12.8", repos = c("https://mlverse.r-universe.dev", "https://cloud.r-project.org"))
    # Then install torch as usual
    install.packages("torch")

When torch starts, it automatically detects installed cudatoolkit R packages and uses them to provide CUDA libraries. If multiple versions are installed, the newest supported version is preferred.

### Environment variables

The `TORCH_CUDATOOLKIT` environment variable controls cudatoolkit R package detection:

- Not set (default): torch auto-detects installed cudatoolkit R packages.
- Set to a version (e.g. `TORCH_CUDATOOLKIT=12.8`): forces torch to use that specific cudatoolkit R package. Errors if the package is not installed. If `CUDA` is also set, the two values must agree.
- Set to `FALSE`: disables cudatoolkit R package detection entirely. torch will only use system CUDA (via `CUDA_HOME`, `CUDA_PATH`, or `nvcc`).

The full CUDA version detection priority is:

1. `TORCH_CUDATOOLKIT` — explicit cudatoolkit R package selection.
2. `CUDA` — if a matching cudatoolkit R package is installed, use it; otherwise use the value directly for system CUDA.
3. Auto-detect installed cudatoolkit R packages.
4. System CUDA — `CUDA_HOME` / `CUDA_PATH`, conventional paths, `nvcc` on PATH.

When a cudatoolkit R package is found (via any of steps 1-3), system CUDA detection is skipped entirely.

## Troubleshooting

### Debug installation messages

As there are many installation failure scenario, you may want to activate verbose messages for better understanding what is going on during the installation process. This is done through the `TORCH_INSTALL_DEBUG` environment variable:

    Sys.setenv(TORCH_INSTALL_DEBUG=1)

### Large file download timeout

If you encounter timeout during library download, or if after a while, downloads end-up with a warning such as:

    Warning messages:
    1: In utils::download.file(library_url, temp_file) :
      downloaded length 44901568 != reported length 141774525
    2: In utils::download.file(library_url, temp_file) :
      URL '...': Timeout of 60 seconds was reached
    3: Failed to install Torch, manually run install_torch(). download from 'https://download.pytorch.org/libtorch/cpu/libtorch-macos-1.7.1.zip' failed

This means you encounter a download timeout. then, you should increase the timeout value in `install_torch()` like

    install_torch(timeout = 600)

### File based download

In cases where you cannot reach download servers from the machine you intend to install torch on, last resort is to install Torch and Lantern library from files. This is done in 3 steps :

1- get the download URLs of the files.

    get_install_libs_url()

2- save those files into the machine filesystem. We will use `/tmp/` here as an example .

3- install torch from files

    # then after making both files available into /tmp/
    Sys.setenv(TORCH_URL="/tmp/libtorch-v1.13.1.zip")
    Sys.setenv(LANTERN_URL="/tmp/lantern-0.9.1.9001+cpu+arm64-Darwin.zip")
    torch::install_torch()

### Installing older versions

As of v0.13.0 torch shifted from using Google Cloud Storage service to AWS S3 as the storage service for the required Lantern binaries.

We will keep the files in the GCS bucket for as long as possible, but we might need to remove them at some point in time. Those files have been backed up in the new AWS S3 bucket using the same file structure, so if torch tries to download some from a URL starting with `https://storage.googleapis.com/torch-lantern-builds`, you should now replace it with `https://torch-cdn.mlverse.org`.

For torch versions between v0.10.0 and v0.12.0 (both included), you should be able to set the environment variable `LANTERN_BASE_URL=https://torch-cdn.mlverse.org/binaries/` to point to the new address of the binaries. For older versions of torch, you might need to manually download the file from the new address and extract it to the expected `TORCH_HOME` directory. Feel free to open an issue on GitHub if you need help with this.