---

name: nitrain description: "Routes Nitrain medical-imaging dataset, preprocessing, training, and prediction workflows." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

Nitrain

Nitrain is a framework-agnostic Python library for medical-imaging datasets, image transforms, sampling, model training, and prediction. Use this skill when users ask about Dataset, Loader, Trainer, Predictor, ANTsPy-based image workflows, or the package-level APIs exposed by nitrain.

Install at a glance

Read references/installation.md for the verified CPU stack, workflow-specific pins, and the small set of packages that cover the inspected workflows.

For a quick import check from an installed environment:

python -c "import nitrain as nt; print(nt.__version__); from nitrain import readers, transforms, samplers; print(readers.ImageReader, transforms.RangeNormalize, samplers.SliceSampler)"

For a broader smoke check, run scripts/check_install.py:

python scripts/check_install.py --mode all

Use --mode base, --mode datasets, --mode preprocess, --mode models, --mode predictor, or --mode torch if you only want one workflow family.

Route map

sub-skills/datasets-readers/SKILL.md

Use this route when you need to build datasets from local files, CSV/TSV columns, folder labels, in-memory arrays or images, nested reader structures, example data, or Google Cloud storage.

Typical requests:

• "Create a dataset from images and participants.csv"
• "Infer readers from nested lists or dictionaries"
• "Load the built-in example-01 fixture"
• "Use a Google Cloud bucket as a dataset source"

Read sub-skills/datasets-readers/SKILL.md when the task is about data layout, reader choice, or missing-file / credential errors.

sub-skills/preprocessing-and-loading/SKILL.md

Use this route when you need to apply transforms, compose random augmentations, choose samplers, or batch data with Loader and Loader.to_keras().

Typical requests:

• "Add aligned transforms to inputs and outputs"
• "Sample slices, patches, or blocks"
• "Make a Keras-ready loader"
• "Explain why a transform key or sampler shape failed"

Read sub-skills/preprocessing-and-loading/SKILL.md when the task is about shape changes, augmentation pipelines, or loader batching.

sub-skills/models-training/SKILL.md

Use this route when you need to discover architectures, list available model families, fetch pretrained weights, or train/evaluate Keras/TensorFlow or Torch/MONAI models with Nitrain trainers.

Typical requests:

• "Create an ANTsPyNet model"
• "List supported architectures"
• "Train a regression model with Trainer"
• "Run the TorchTrainer CPU path"

Read sub-skills/models-training/SKILL.md when the task is about model construction, trainer defaults, metrics, saving, or framework detection.

sub-skills/prediction-and-explanation/SKILL.md

Use this route when you need to run slice-based prediction with Predictor or inspect the current OcclusionExplainer surface.

Typical requests:

• "Predict segmentation outputs from a Dataset"
• "Reconstruct slice predictions into ANTs images"
• "Check what OcclusionExplainer does right now"

Read sub-skills/prediction-and-explanation/SKILL.md when the task is about inference output shape, slice-axis handling, or the explainer stub.

Common prerequisites

• antspyx is the core imaging dependency.
• pandas is needed for CSV/TSV-backed readers.
• antspynet, tensorflow, and tf-keras are needed for the verified Keras/TensorFlow model and trainer workflows.
• torch and monai are needed for the CPU TorchTrainer smoke path.
• google-cloud-storage and google-auth are needed for GCS-backed datasets.
• datalad and git-annex are only needed for networked OpenNeuro-style fetches.

Public quirks worth remembering

• nitrain.fetch_pretrained is a submodule object in this snapshot; import the callable from nitrain.models.fetch_pretrained.
• TorchTrainer is available from nitrain.trainers, not from the package root.
• The README still mentions tx.RandomNoise, but that transform is not present in the inspected source tree.
• TensorFlow may print harmless CPU-only warnings on hosts without CUDA.

When to read the supporting references

• references/installation.md — the verified install matrix and the safest package pins for this snapshot.
• references/troubleshooting.md — cross-cutting import, version, and backend issues that affect several workflows.
• references/repo-provenance.md — confirm whether this skill still matches the current checkout before you reuse it or refresh it.
• scripts/check_install.py — run this to verify the package and the selected workflow family without reopening the source checkout.

How to think about the package

Nitrain is best treated as four user-facing workflow families:

1. dataset and reader construction,
2. preprocessing and loading,
3. model discovery and training,
4. prediction and the current explainer surface.

Each family has its own sub-skill because the package combines imaging data layout, transforms, samplers, model backends, and output post-processing in a way that is easier to use when routed separately.

Before you refresh or reuse this skill

If the current repository commit, package version, or exported API surface has changed, this skill may be stale. Read references/repo-provenance.md first, then refresh the skill instead of assuming the guidance still matches the code.