How to install ZenML
ZenML is a Python package that can be installed directly via
pip install zenml
pip install "zenml[server]"
If you do not have deployed infrastructure, and want to quickly spin up combinations of tools on the cloud, the MLOps stack sister repository contains a series of Terraform-based recipes to provision such stacks. These recipes can be used directly with ZenML:
pip install "zenml[stacks]"
As mentioned above, make sure that your virtual environment uses one of the supported Python versions.
Once the installation is completed, you can check whether the installation was successful through:
docker run -it zenmldocker/zenml /bin/bash
If you would like to run the ZenML server with Docker:
docker run -it -d -p 8080:80 zenmldocker/zenml-server
If you want to use the bleeding edge of ZenML that has not even been released yet, you can install our
Installing develop is mainly useful if there are key features or bug fixes that you urgently need so you can get those immediately and do not have to wait for the next release.
Remote orchestrators like Kubeflow require Docker Images to set up the environments of each step. By default, they use the official ZenML docker image that we provide with each release. However, if you install from develop, this image will be outdated, so you need to build a custom image instead, and specify it in the configuration of your orchestrator accordingly (see the MLOps Stacks Orchestrator page of your specific orchestrator flavor for more details on how this can be done).
Linux, MacOS (Intel), Windows
docker build -t <IMAGE_NAME> -f docker/local-dev.Dockerfile .
docker build --platform linux/amd64 -t <IMAGE_NAME> -f docker/local-dev.Dockerfile .