Installation

Installing ZenML and getting started.

1

Install ZenML

ZenML currently supports Python 3.9, 3.10, 3.11, and 3.12. Please make sure that you are using a supported Python version.

Base package

ZenML is a Python package that can be installed using pip or other Python package managers:

pip install zenml

Installing the base package only allows you to connect to a deployed ZenML server. If you want to use ZenML purely locally, install it with the local extra:

pip install 'zenml[local]'

Local Dashboard

If you want to use the ZenML dashboard locally, you need to install ZenML with the server extra:

pip install 'zenml[server]'

If you want to run a local server while running on a Mac with Apple Silicon (M1, M2, M3, M4), you should set the following environment variable:

export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES

You can read more about this here.

Jupyter Notebooks

If you write your ZenML pipelines ins Jupyter notebooks, we recommend installing ZenML with the jupyter extra which includes improved CLI output and logs:

pip install 'zenml[jupyter]'

2

Verifying Installations

Once the installation is completed, you can check whether the installation was successful either through Bash or Python:

Bash

zenml version

Python

import zenml

print(zenml.__version__)

If you would like to learn more about the current release, please visit our PyPi package page.

Running with Docker

zenml is also available as a Docker image hosted publicly on DockerHub. Use the following command to get started in a bash environment with zenml available:

docker run -it zenmldocker/zenml /bin/bash

If you would like to run the ZenML server with Docker:

docker run -it -d -p 8080:8080 zenmldocker/zenml-server

Starting the local server

By default, ZenML runs without a server connected to a local database on your machine. If you want to access the dashboard locally, you need to start a local server:

# Make sure to have the `server` extra installed
pip install "zenml[server]"
zenml login --local  # opens the dashboard locally 

However, advanced ZenML features are dependent on a centrally deployed ZenML server accessible to other MLOps stack components. You can read more about it here. For the deployment of ZenML, you have the option to either self-host it or register for a free ZenML Pro account.