> ## Documentation Index
> Fetch the complete documentation index at: https://docs.telescope.training/llms.txt
> Use this file to discover all available pages before exploring further.

# Installation

## Install

```bash theme={null}
pip install telescope-ui
```

Requires Python 3.11+.

## Configure W\&B

Telescope UI reads training data from Weights & Biases. Run `wandb login` once to authenticate:

```bash theme={null}
wandb login
```

You can also provide a W\&B API key directly through the UI on first launch.

## Start the dashboard

```bash theme={null}
telescope
```

This starts the server and opens the dashboard at `http://localhost:8005`.

| Flag           | Description                          |
| -------------- | ------------------------------------ |
| `--port PORT`  | Port to serve on (default: 8005)     |
| `--host HOST`  | Host to bind to (default: 127.0.0.1) |
| `--no-browser` | Don't open the browser automatically |
| `--debug`      | Enable verbose logging               |

## Connect to training runs

On first launch, add a W\&B project to monitor using the **Add Project** option in the sidebar. Enter the project path in `entity/project` format. Telescope will poll that project for runs tagged with `telescope` — the default training config already includes this tag, so any training runs in the project will appear automatically.

You can add more projects at any time from the sidebar menu.

## Data storage

All synced data is stored locally in a DuckDB database at `~/.telescope/`. You can override this location with the `TELESCOPE_DATA_DIR` environment variable.

## Stack

Telescope UI is built with a FastAPI server, a TypeScript frontend, and a DuckDB database. It runs locally on your personal machine and connects to training data produced by the trainer.