⛵️ Overview

We recommend using Docker to get started.

Docker is a tool that allows you to run Mage in a containerized environment: you can run Mage on any operating system that supports Docker, including Windows, Mac, and Linux. Using Docker means that you don’t have to worry about installing dependencies or configuring your environment. If you’d like to install Mage without Docker, you can use pip or conda.

If you’re familiar with Docker Compose or plan on adding or extending images (e.g. Postgres) in your project, we recommend starting from the Docker compose template. Otherwise, we recommend Docker run.

🪄 Get Mage

🏃‍♂️ Run your first pipeline

If you haven’t already, open a browser to http://localhost:6789. From the pipelines page, select example_pipeline and open the notebook view by selecting Edit pipeline from the left side nav.

Select the first block by clicking it and select the “play” icon in the top right to run the block. You’ve just ran your first Mage block & loaded data from a dataset!

Do the same for the following cells in the pipeline to transform and export the data. Congrats, you’re now a Mage ninja! 🥷

🧙🏻‍♂️ Install Mage dependencies (optional)

Mage also has the following add-on packages:

allmage-ai[all]install all add-ons
azuremage-ai[azure]install Azure related packages
clickhousemage-ai[clickhouse]use Clickhouse for data import or export
dbtmage-ai[dbt]install dbt packages
google-cloud-storagemage-ai[google-cloud-storage]use Google Cloud Storage for data import or export
hdf5mage-ai[hdf5]process data in HDF5 file format
mysqlmage-ai[mysql]use MySQL for data import or export
postgresmage-ai[postgres]use PostgreSQL for data import or export
redshiftmage-ai[redshift]use Redshift for data import or export
s3mage-ai[s3]use S3 for data import or export
snowflakemage-ai[snowflake]use Snowflake for data import or export
sparkmage-ai[spark]use Spark (EMR) in your Mage pipeline
streamingmage-ai[streaming]use Streaming pipelines

To install these, run the following command from the Mage terminal:

pip install "mage-ai[spark]"

or add the following to your requirements.txt file:


You can access the terminal from the side nav on the right in the pipeline editor page. Read more about installing from requirements.txt here.

🧭 Journey on

Navigate to our tutorials to learn more about Mage and how to build your own pipelines or continue exploring our docs for advanced configuration and deployment options.

If you’re interested in connecting a database in Docker, check out our guide for more information.