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name: cli-and-io description: "Use TorchIO's command-line interface and I/O integration surfaces for image metadata, conversion, transform commands, cache inspection, visualization exports, and local or remote NIfTI/NIfTI-Zarr backends." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TorchIO CLI and I/O

Use this sub-skill when the task is to run or generate torchio CLI commands, inspect or convert medical image files, apply a one-off transform from the shell, manage TorchIO's dataset cache, troubleshoot plotting or animation exports, or reason about local, remote, and NIfTI-Zarr image loading.

Route Map

• For CLI syntax, safe command patterns, command-specific caveats, and the bundled smoke check, read CLI reference.
• For accepted image sources, lazy backends, remote URI behavior, NIfTI-Zarr conversion, and optional cloud extras, read I/O backends.
• For failures involving missing extras, Tyro argument parsing, cache side effects, remote credentials, .nii.zarr dependencies, or non-notebook plotting, read troubleshooting.
• To verify a TorchIO CLI installation on synthetic data, run python sub-skills/cli-and-io/scripts/torchio_cli_smoke.py --help first, then run the script without arguments.

Quick Start

1. Confirm the CLI is installed: torchio --version or python -m torchio.cli --version.
2. Inspect an image: torchio info image.nii.gz.
3. Convert between supported image formats: torchio convert input.nii.gz output.nii or torchio convert input.nii.gz output.nii.zarr when the zarr extra is installed.
4. Apply a one-off transform: torchio transform input.nii.gz output.nii.gz Noise std=0.1 or torchio transform input.nii.gz crop.nii.gz CropOrPad target_shape=128.
5. Save visual output explicitly outside notebooks: torchio plot image.nii.gz --output slices.png or torchio animate image.nii.gz sweep.gif --seconds 3 --direction I.

Boundaries

• This sub-skill owns CLI command generation, image file I/O behavior, optional visualization/export dependencies, remote/Zarr/cloud caveats, and cache command safety.
• For in-Python image construction such as tio.ScalarImage(tensor) or subject metadata modeling, route to the data-model sub-skill.
• For composing transform pipelines in Python, current transform class names, and transform API details, route to the transforms sub-skill.
• For patch sampling, training queues, aggregation, and inference tiling, route to the patch-workflows sub-skill.

Safety Defaults

• Prefer torchio cache dir over torchio cache clean; cleaning without a dataset name removes all TorchIO cached data.
• Treat cloud URIs as network and credential-dependent; avoid running them in smoke checks unless the user explicitly provides a safe test endpoint and credentials.
• Use --output for plot and explicit .gif or .mp4 paths for animate in non-interactive sessions.