Pipelines automate your machine learning operations on Valohai ecosystem.
You can read more about the reasoning behind general pipeline concepts like graphs, nodes and edges on the Pipelines core concepts page.
pipeline definition has 3 required properties:
name: name for the pipeline
nodes: list of all nodes (executions) in the pipeline
edges: list of all edges (requirements) between the nodes
A simple pipeline could look something like this:
# define "generate-dataset" and "train-model" steps above... - pipeline: name: simple-pipeline nodes: - name: generate type: execution step: generate-dataset - name: train type: execution step: train-model edges: - [generate.output.images*, train.input.dataset-images] - [generate.output.labels*, train.input.dataset-labels]
Here we have a pipeline with 2 nodes, and the second node train will wait its inputs to be generated
by generate node. All files in
/valohai/outputs that start with either
labels will be passed
between the executions.