---

name: datasets-models-and-io description: "Route Jittor data loading, transforms, built-in datasets, model zoo constructors, pretrained weights, and checkpoint I/O." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

datasets-models-and-io

Use this sub-skill when a task needs to build or debug a Jittor input pipeline, choose a built-in dataset, apply image transforms, instantiate a model-zoo backbone, avoid accidental downloads, or move checkpoints between Jittor and PyTorch-style files.

Do not use this sub-skill for optimizer details, full training-loop design, low-level Var/autograd semantics, backend installation, performance flags, custom operators, or converter service operation. Route those topics to the sibling sub-skills that own them.

Read or run

• Read references/data-and-transform-reference.md when selecting Dataset, DataLoader, ImageFolder, TensorDataset, MNIST, CIFAR, VOC, or jittor.transform workflows.
• Read references/model-zoo-and-checkpoints.md when constructing jittor.models backbones, deciding whether pretrained=True is safe, saving/loading Jittor state, or attempting PyTorch checkpoint interop.
• Read references/troubleshooting.md when data downloads fail, a cache or checkpoint appears corrupt, workers hang, transforms change layout unexpectedly, pretrained weights try to use the network, or optional PyTorch is unavailable.
• Run scripts/data_model_smoke.py for a no-download synthetic data/transform/TensorDataset plus resnet18(pretrained=False) shape smoke.

Safe default decisions

1. For smoke tests and CI, prefer synthetic arrays or TensorDataset; set all built-in dataset downloads and model pretrained flags off unless data or weights are already present.
2. The bundled smoke script clears CUDA auto-detection before importing Jittor so it stays usable on hosts where a mismatched nvcc is visible.
3. Keep image transforms in a predictable order: PIL-only spatial/color transforms first, then conversion/normalization to CHW arrays, then batching.
4. Treat Jittor model-zoo constructors as NCHW image classifiers by default; use pretrained=False unless the task explicitly permits a network download.
5. For checkpoint loading, validate key names and shapes before trusting a load: Jittor logs skipped or mismatched parameters and may not raise an exception.