Beta: Install

Applies to v2.0.0-beta.2. For the stable v1.1.1 procedure, see the User Guides.

Google Chrome is no longer required. The launcher opens in a native window (pywebview) instead of a browser page served over localhost:8889, and nothing opens a network port any more.

Upgrading from Beta 1

This step is required. The launcher moved from Eel to pywebview and onnxruntime was added, so a Beta 1 environment has neither. Without it, python -m yoru stops with:

[yoru] failed to import yoru.app.main: No module named 'webview'

conda activate yoru
conda env update -f YORU.yml --prune
python -m yoru

The launch command itself is unchanged.

Fresh install with conda

Check the installation of Miniconda.

Anaconda’s TERMS OF SERVICE was changed. If you use Anaconda in an organization that has two hundred (200) or more employees or contractors, you have to be careful.

Currently, you can use miniconda freely.

Download or clone the YORU project and check out the beta tag.

a. Install git

 conda install git

b. Clone the repository

 cd "Path/to/download"
 git clone https://github.com/Kamikouchi-lab/YORU.git
 cd YORU
 git checkout v2.0.0-beta.2

Install the GPU driver and the CUDA toolkit.
Create a virtual environment using YORU.yml.
```
 conda env create -f YORU.yml
```
Activate the virtual environment.
```
 conda activate yoru
```
Install PyTorch corresponding to your CUDA version.
- For CUDA==11.8
```
 pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu118
```
- For CUDA==12.1
```
 pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu121
```
(torch, torchvision and torchaudio will be installed.)

RTX 50-series (Blackwell) cards need a newer build. See working-example.md in the repository, which records a working RTX 5070 Ti setup on torch 2.8.0+cu128.

Run YORU.

 conda activate yoru
 cd "Path/to/YORU/project/folder"
 python -m yoru

Alternative: install with uv

uv resolves everything from pyproject.toml / uv.lock, so the conda environment creation (step 4) and the manual PyTorch step (step 6) are not needed.

cd Path/to/YORU
uv sync
uv run python -m yoru

Starting YORU

What you want	Command
Launcher (default)	`python -m yoru` or `yoru`
Launcher with a specific condition file	`yoru gui --config path/to/condition.yaml`
Real-time process directly	`python -m yoru.realtime_yoru_GUI path/to/condition.yaml`
Check the installed version	`yoru --version`

Notes on the launcher in Beta 2:

It is a native window, renders correctly offline, and no browser is involved.
The selected condition file is shown in the window at startup.
It remembers the last-used config instead of resetting to config/template.yaml.
Selecting a config file that no longer exists shows an error dialog instead of only printing to the console.
All windows are a uniform 1000×800. The Real-time Process window is therefore narrower than in Beta 1 (it was 1280×700).

Verifying the install

yoru --version prints the version.
python -m yoru opens the launcher window (no browser).
Open the Training sub-module — if a backend’s dependency is missing, the error names the backends that are available and why the others failed.

Licensing note

Ultralytics YOLO is dual-licensed. It is AGPL-3.0 by default, and that extends to models trained with it, so commercial use requires an Ultralytics Enterprise licence. THIRD_PARTY_LICENSES.md in the repository lists every dependency and its licence.

YORU Your Optimal Recognition Utility