Install Instructions

Pre-requisites

torchtune requires PyTorch, so please install for your proper host and environment using the “Start Locally” page. You should also install torchvision (for multimodal LLMs) and torchao (for quantization APIs). You can install either stable or nightly versions with the following commands:

# Install stable version of PyTorch libraries using pip
pip install torch torchvision torchao

# Or nightly install for latest features
pip install --pre torch torchvision torchao --index-url https://download.pytorch.org/whl/nightly/cu121 # full options are cpu/cu118/cu121/cu124

Install via PyPI

The latest stable version of torchtune is hosted on PyPI and can be downloaded with the following command:

pip install torchtune

To confirm that the package is installed correctly, you can run the following command:

tune

And should see the following output:

usage: tune [-h] {download,ls,cp,run,validate} ...

Welcome to the torchtune CLI!

options:
-h, --help            show this help message and exit

...

Install via git clone

If you want the latest and greatest features from torchtune or if you want to become a contributor, you can also install the package locally with the following command.

git clone https://github.com/pytorch/torchtune.git
cd torchtune
pip install -e .

# or for a developer installation
pip install -e .["dev"]

Install nightly build

torchtune gets built every evening with the latest commits to main branch. If you want the latest updates to the package without installing via git clone, you can install with the following command:

pip install --pre torchtune --extra-index-url https://download.pytorch.org/whl/nightly/cpu --no-cache-dir

Note

--no-cache-dir will direct pip to not look for a cached version of torchtune, thereby overwriting your existing torchtune installation.

If you already have PyTorch installed, torchtune will default to using that version. However, if you want to use the nightly version of PyTorch, you can append the --force-reinstall option to the above command. If you opt for this install method, you will likely need to change the “cpu” suffix in the index url to match your CUDA version. For example, if you are running CUDA 12, your index url would be “https://download.pytorch.org/whl/nightly/cu121”.