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TokTagger

TokTagger Logo

An open source, interactive annotation platform for Tokamak diagnostic data.

Workflow: CI Workflow: Dependabot License: MIT Linting: ruff Testing: pytest

What It Does

TokTagger is a web-based platform for curating labeled datasets from tokamak diagnostics. It lets users browse shots, inspect signals and images, apply consistent labels, and manage annotations in one place. The Python API and React UI support local or team workflows, making it straightforward to create datasets for downstream analysis and machine-learning models.

It currently supports the following features:

  • Data Browsing: Explore tokamak shots, signals, and images through an intuitive interface.
  • Annotation Tools: Apply consistent labels to signals and images using a customizable tagging system.
  • ML Models: Train and infer from ML models within the UI.
  • Dataset Management: Organize and manage annotations in a central repository.
  • Extensible API: A Python API for integrating with existing workflows and tools.

Installation

To run the application locally:

Install via pip

To install the package via pip (or similarly via Poetry or uv package managers):

python -m venv .venv
source .venv/bin/activate

To install the package for labelling only (without ML Model functionality):

GIT_LFS_SKIP_SMUDGE=1 pip install git+ssh://[email protected]/ukaea/toktagger.git

Or to include the ML models:

GIT_LFS_SKIP_SMUDGE=1 pip install git+ssh://[email protected]/ukaea/toktagger.git#egg=toktagger[models]

If you intend to add custom data loaders or models to your TokTagger instance, this is the recommended route.

Install as a uv tool

Alternatively, it can be installed as a tool using uv. To install the package for labelling only (without ML Model functionality):

GIT_LFS_SKIP_SMUDGE=1 uv tool install --lfs --python 3.12.6 git+ssh://[email protected]/ukaea/toktagger.git`

Or to include the ML models:

GIT_LFS_SKIP_SMUDGE=1 uv tool install --lfs --python 3.12.6 git+ssh://[email protected]/ukaea/toktagger.git#egg=toktagger[models]`

Quick Start

To get started, run:

toktagger

This will start a local instance of the application running at http://localhost:8002.