Runs a variety of precomputation steps for a model.
Deprecated
This client method is being deprecated and will not be supported in future versions of the client. This method is called automatically now when calling client.add_model_surrogate() or client.add_model_artifact().
Note
This method should be called after client.upload_model_package(). It is not necessary after calling client.register_model() as this step happens automatically when onboarding a model.
| Input Parameter | Type | Default | Description |
|---|---|---|---|
| project_id | str | None | The unique identifier for the project. |
| model_id | str | None | A unique identifier for the model. |
| dataset_id | str | None | The unique identifier for the dataset. |
| overwrite_cache | Optional [bool] | True | If True, will overwrite existing cached information. |
| batch_size | Optional [int] | 10 | The batch size used for global PDP calculations. |
| calculate_predictions | Optional [bool] | True | If True, will precompute and store model predictions. |
| cache_global_impact_importance | Optional [bool] | True | If True, global feature impact and global feature importance will be precomputed and cached when the model is registered. |
| cache_global_pdps | Optional [bool] | True | If True, global partial dependence plots will be precomputed and cached when the model is registered. |
| cache_dataset | Optional [bool] | False | If True, histogram information for the baseline dataset will be precomputed and cached when the model is registered. |
PROJECT_ID = 'example_project'
DATASET_ID = 'example_dataset'
MODEL_ID = 'example_model'
client.trigger_pre_computation(
project_id=PROJECT_ID,
dataset_id=DATASET_ID,
model_id=MODEL_ID
)