Use Your Own Model
This tutorial covers two different cases.
Case 1: Enable A Supported Public Wrapper
If the method is already supported in ST-CNVBench, you only need to configure and enable it.
- Start from
configs/templates/models.template.yaml - Copy the relevant model section into your own
models.yaml - Set
enabled: true - Fill in runtime-specific paths or image names
- Run with
--models <ModelName>
See Model Run for the detailed model configuration contract.
Case 2: Add A New Wrapper
If your method is not yet supported, the public extension path is:
- Add a wrapper under
src/st_cnvbench/model/tools_scripts/<model_name>/ - Register it in
src/st_cnvbench/model/tools_scripts/__init__.py - Add a public config section to
configs/templates/models.template.yaml - If the method should participate in evaluation, add or update the corresponding evaluation loader mappings
Recommended First Check
Before wiring a full execution path, make sure the new wrapper can at least:
- locate the standardized dataset bundle
- prepare its own input files
- build a valid command line
- fail loudly when required paths or files are missing
Try Next
- For the packaged cSCC demo, go to Quickstart Demo And Expected Outputs
- For the CNV profile task example, go to CNV Profile Task Example
- For the tumor-normal task example, go to Tumor-Normal Classification Task Example
- For the subclone task example, go to Subclone Identification Task Example
- To prepare your own input dataset, go to Use Your Own Dataset