install.mdx

title	Installation
description	Install the Tilebox Python SDK packages using your preferred package manager to start working with datasets, workflows, and satellite data storage.
icon	download

Package Overview

Tilebox offers a Python SDK for accessing Tilebox services. The SDK includes separate packages that can be installed individually based on the services you wish to use, or all together for a comprehensive experience.

Access Tilebox datasets from Python Workflow client and runner for Tilebox

Installation

Install the Tilebox python packages using your preferred package manager.

For new projects Tilebox recommend using [uv](https://docs.astral.sh/uv/). ```bash uv uv add tilebox-datasets tilebox-workflows tilebox-storage ``` ```bash pip pip install tilebox-datasets tilebox-workflows tilebox-storage ``` ```bash poetry poetry add tilebox-datasets="*" tilebox-workflows="*" tilebox-storage="*" ``` ```bash pipenv pipenv install tilebox-datasets tilebox-workflows tilebox-storage ```

Setting up a local JupyterLab environment

To get started quickly, you can also use an existing Jupyter-compatible cloud environment such as [Google Colab](https://colab.research.google.com/).

If you want to set up a local Jupyter environment to explore the SDK or to run the Sample notebooks locally, install JupyterLab for a browser-based development environment. It's advised to install the Tilebox packages along with JupyterLab, ipywidgets, and tqdm for an enhanced experience.

mkdir tilebox-exploration
cd tilebox-exploration

uv init --no-package
uv add tilebox-datasets tilebox-workflows tilebox-storage
uv add jupyterlab ipywidgets tqdm
uv run jupyter lab

Trying it out

After installation, create a new notebook and paste the following code snippet to verify your installation. If you're new to Jupyter, you can refer to the guide on interactive environments.

from tilebox.datasets import Client

client = Client()
datasets = client.datasets()

collection = datasets.open_data.copernicus.landsat8_oli_tirs.collection("L1T")
data = collection.query(temporal_extent=("2015-01-01", "2020-01-01"), show_progress=True)
data

If the installation is successful, the output should appear as follows.