Add run_info file support to ShellModelRunner and R SDK example #20
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Add RunInfo class containing runtime information passed from CHAP to models: - prediction_length: Number of periods to predict - additional_continuous_covariates: User-specified additional covariates - future_covariate_origin: Origin/source of future covariate forecasts Update all model runner implementations (BaseModelRunner, FunctionalModelRunner, ShellModelRunner) to accept and pass run_info parameter. Update TrainRequest and PredictRequest schemas to include run_info field. This change aligns chapkit with the model contract defined in chap_core, enabling models to receive runtime configuration separately from user-defined model configuration.
Add PeriodType StrEnum (any, week, month, year) to specify which time period types a model supports. MLServiceInfo now exposes this field in the /api/v1/info endpoint for CHAP to read model capabilities.
This enables integration with the CHAP R SDK by:
1. ShellModelRunner now writes run_info.yml to the workspace
- Available via {run_info_file} variable in command templates
- Written for both train and predict operations
- Contains prediction_length, additional_continuous_covariates, etc.
2. Added R model integration example (examples/ml_r/)
- Demonstrates using chap.r.sdk with chapkit
- Uses create_chapkit_cli() for named argument interface
- Shows info --format json for metadata discovery
The R SDK's info command outputs structured JSON that chapkit can use
for configuration schema validation and model metadata discovery.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Codecov Report✅ All modified and coverable lines are covered by tests. 📢 Thoughts on this report? Let us know! |
This enables automatic configuration schema discovery from R models
(or any external script that implements an info command).
New features:
- discover_model_info(): Execute info command and parse JSON output
- create_config_from_schema(): Convert JSON Schema to Pydantic BaseConfig
- ModelInfo: Container for discovered service_info and config_class
The R example now uses schema discovery instead of duplicating the
schema in Python. The schema is defined once in R and automatically
discovered at service startup.
Usage:
```python
from chapkit.ml import discover_model_info, ShellModelRunner
model_info = discover_model_info("Rscript model.R info --format json")
# model_info.config_class is a Pydantic model matching the R schema
# model_info.period_type, required_covariates, etc. from service_info
app = MLServiceBuilder(
config_schema=model_info.config_class,
...
)
```
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Use Path(__file__).parent to locate model.R script, allowing the example to run from any working directory. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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Summary
This PR enables integration between chapkit and the CHAP R SDK by:
ShellModelRunner now writes
run_info.ymlto the workspace{run_info_file}variable in command templatesprediction_length,additional_continuous_covariates,future_covariate_originAdded R model integration example (
examples/ml_r/)chap.r.sdkwith chapkitcreate_chapkit_cli()for named argument interfaceinfo --format jsonfor metadata discoveryUsage
R models using
chap.r.sdkcan now receive run_info via the CLI:The R SDK's
info --format jsoncommand outputs structured metadata for chapkit:$ Rscript model.R info --format json { "service_info": { "period_type": "any", "required_covariates": [], "allows_additional_continuous_covariates": false }, "config_schema": { ... } }Test plan
info --format jsonworks correctly🤖 Generated with Claude Code