FAQ
Find answers to the most frequently asked questions about ZenML.
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Find answers to the most frequently asked questions about ZenML.
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This page addresses common questions about ZenML, including general information about the project and how to accomplish specific tasks.
We built it because we scratched our own itch while deploying multiple machine-learning models in production over the past three years. Our team struggled to find a simple yet production-ready solution whilst developing large-scale ML pipelines. We built a solution for it that we are now proud to share with all of you! Read more about this backstory .
Not really! An orchestrator in MLOps is the system component that is responsible for executing and managing the execution of an ML pipeline. ZenML is a framework that allows you to run your pipelines on whatever orchestrator you like, and we coordinate with all the other parts of an ML system in production. There are that ZenML supports out-of-the-box, but you are encouraged to in order to gain more control as to exactly how your pipelines are executed!
X? How does the tool Y integrate with ZenML?Take a look at our (in particular the ), which contains instructions and sample code to support each integration that ZenML supports out of the box. You can also check out to see active examples of many of our integrations in action.
The ZenML team and community are constantly working to include more tools and integrations to the above list (check out the for more details). You can you'd like and add your ideas to the roadmap.
Most importantly, ZenML is extensible, and we encourage you to use it with whatever other tools you require as part of your ML process and system(s). Check out with extending ZenML to learn more!
ZenML is distributed under the terms of the Apache License Version 2.0. A complete version of the license is available in the in this repository. Any contribution made to this project will be licensed under the Apache License Version 2.0.
ZenML officially supports Windows if you're using WSL. Much of ZenML will also work on Windows outside a WSL environment, but we don't officially support it, and some features don't work (notably anything that requires spinning up a server process).
Yes, ZenML does support Macs running on Apple Silicon. You just need to make sure that you set the following environment variable:
This is a known issue with how forking works on Macs running on Apple Silicon, and it will enable you to use ZenML and the server. This environment variable is needed if you are working with a local server on your Mac, but if you're just using ZenML as a client / CLI and connecting to a deployed server, then you don't need to set it.
ZenML is designed to be stack-agnostic, so you can use it with any cloud infrastructure or MLOps stack. Each of the documentation pages for stack components explain how to deploy these components on the most popular cloud providers.
We develop ZenML together with our community! To get involved, the best way to get started is to select any issue from the .
Please read for more information. For small features and bug fixes, please open a pull request as described in the guide. For anything bigger, it is worth or so we can best discuss and support your plans.
Please start by on implementing a custom stack component, which offers some general advice on what you'll need to do.
From there, each of the custom stack component types has a dedicated section about adding your own custom components. For example, to add a custom orchestrator, you would .
Check out on some ways you can try to solve these dependency and versioning issues.
Read , in which several options are presented.
on implementing this is the place to learn more.
To reset your ZenML client, you can run zenml clean which will wipe your local metadata database and reset your client. Note that this is a destructive action, so feel free to before doing this if you are unsure.
Please read our to start with. You might also find the code examples in which is related to this topic.
allow you to get going quickly with ZenML. We recommend the Starter template (starter) for most use cases, which gives you a basic scaffold and structure around which you can write your own code. You can also build templates for others inside a Git repository and use them with ZenML's templates functionality.
Upgrading your ZenML client package is as simple as running pip install --upgrade zenml in your terminal. For upgrading your ZenML server, please refer to , which covers most of the ways you might do this as well as common troubleshooting steps.
For information on how to use a specific stack component, please refer to , which contains all our tips and advice on how to use each integration and component with ZenML.
The first point of contact should be . Ask your questions about bugs or specific use cases, and someone from the core team will respond.
