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Command Line Interface¤

Command-line helpers for validating configs, scaffolding templates, and inspecting a Datarax installation. Pipelines themselves are built in Python (see the DAG Construction Guide).

Commands¤

Command Purpose Example
datarax validate Validate a pipeline config datarax validate -c pipeline.toml
datarax create Scaffold a config template datarax create -o pipeline.toml -t basic
datarax list List available components datarax list --type sources
datarax benchmark Show a calibrax store summary datarax benchmark --dataset synthetic
datarax version Print the installed version datarax version

Key points

  • Config files are TOML and are validated, not executed, by the CLI
  • datarax benchmark delegates to calibrax and prints a store summary; for comparative benchmarks use uv run python -m benchmarks.cli run
  • Build and run pipelines with the Python API

Quick Start¤

# Scaffold a template, then validate it
datarax create --output pipeline.toml --template basic
datarax validate --config-path pipeline.toml

# List the registered source components
datarax list --type sources

# Print the installed version
datarax version

Modules¤

  • main - Main CLI entry point and commands
  • benchmark - python -m datarax.cli.benchmark timing tool

Config File Format¤

datarax create writes a TOML template describing the pipeline as a list of nodes and edges:

[pipeline]
name = "my_pipeline"

[[nodes]]
id = "source"
type = "DataSource"
class = "MemorySource"

[nodes.params]
num_samples = 1000
sample_shape = [28, 28, 1]

[[nodes]]
id = "batch"
type = "BatchNode"

[nodes.params]
batch_size = 32

[[edges]]
from = "source"
to = "batch"

datarax validate accepts this [[nodes]] layout as well as configs that declare a [dag] or [sources] section.

See Also¤