In-depth Configuration
Configure pipelines at will with ZenML.
This is an older version of the ZenML documentation. To read and view the latest version please visit this up-to-date URL.
The goal of this section is to showcase some critical advanced use-cases regarding the configuration of different ZenML resources.
List of topics
Runtime Settings showcases how to configure different ZenML resource during the runtime of a pipeline.
Passing Custom Data Types through Steps via Materializers is required if one of your steps outputs a custom class or other data types, for which materialization is not defined by ZenML itself.
Specifying Hardware Resources for Steps explains how to specify hardware resources like memory or the amount of CPUs and GPUs that a step requires to execute.
Access metadata within steps via Step Fixtures can, for instance, be used to load the best performing prior model to compare newly trained models against.
Controlling the Step Execution Order explains how to control the order in which steps of a pipeline get executed.
Failure and Success hooks showcases how to use success and failure hooks on steps.
We will keep adding more use-cases to the advanced guide of ZenML. If there is a particular topic you cannot find here or any use-case that you would like learn more about, you can reach us in our Slack Channel.
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