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name: pytorch-workflows description: "Routes Raster Vision PyTorch workflow setup for chip classification, semantic segmentation, and object detection, including GeoDataConfig/ImageDataConfig choices, SolverConfig/model/backbone/external_def selection, model-zoo transfer, and example command rendering." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

pytorch-workflows

Use this sub-skill when you need to configure, explain, or reproduce Raster Vision PyTorch task workflows.

Use this route for

• Picking the right PyTorch backend for chip classification, semantic segmentation, or object detection.
• Choosing between GeoDataConfig and ImageDataConfig, including nochip, allow_streaming, multiband imagery, and scene cropping.
• Selecting SolverConfig, Backbone, ModelConfig, ExternalModuleConfig, external_loss_def, and init_weights.
• Working from the bundled examples: tiny_spacenet, SpaceNet Rio, SpaceNet Vegas, ISPRS Potsdam, COWC Potsdam, and xView.
• Reading training, prediction, bundle, eval, and dataloader debug outputs.
• Using model-zoo bundles for prediction or transfer learning.

Do not use this route for

• Generic CLI invocation details, runner semantics, or split handling. Use pipeline-cli.
• Lower-level raster, vector, label, and scene API details. Use data-and-models.
• AWS, Docker, or remote execution setup. Use cloud-and-filesystems.

Read first

• Task recipes
• Example catalog
• Model zoo and transfer
• Troubleshooting

Skill-owned scripts

• scripts/list_example_commands.py — print safe rastervision run commands for known PyTorch examples; it never executes them and supports --help.

Typical workflow

1. Identify the task family and whether the data is scene-based, chip-based, or bundle-based.
2. Choose GeoDataConfig when Raster Vision should read scenes directly; choose ImageDataConfig when chips already exist.
3. Pick the backend, backbone, and any external model or loss definitions.
4. Use the example catalog and command printer to generate a safe local or remote run command.
5. Inspect train/, predict/, eval/, and bundle/ outputs before deciding on tuning or transfer.

Cross-links

• If the problem is really CLI execution or command parsing, switch to pipeline-cli.
• If you need scene, raster, vector, or label mechanics, switch to data-and-models.
• If you need Docker or AWS execution setup, switch to cloud-and-filesystems.