How deeply does Distributed Machine Learning Patterns cover conditional syntax?

Defines the when clause through one branching example, staying within Argo Workflows’ basic syntax.

Conditional syntax

As explained in Distributed Machine Learning Patterns

Conditional syntax is a workflow-language feature that controls whether a step executes based on a condition.

Conditional syntax controls whether a workflow step executes by evaluating a condition in Argo Workflows’ when clause. A preceding step produces a result, and dependent steps branch from that value. section 8.4.1

Coin-flip branching

A flip-coin script prints either heads or tails. The heads step runs only when its condition matches the output; otherwise, the tails step runs. The completed workflow contains the flip-coin Pod and the selected branch Pod. section 8.4.1

How Distributed Machine Learning Patterns covers it

Cover of Distributed Machine Learning Patterns

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Related concepts

Argoused by · Argo is an open-source, Kubernetes-native suite of tools for continuous delivery, workflow orchestration, and workload automation.
when clausepart of · A when clause is an Argo Workflows expression that specifies a condition for executing a workflow step.
syntaxkind of · Syntax is the set of rules governing how elements of a language or notation may be arranged to form valid expressions or constructs.

This explainer was generated by Manning's knowledge graph from the text of Distributed Machine Learning Patterns. The section links point to the parts of the book it draws on.