DeepchecksLLMClient.execute_app

async DeepchecksLLMClient.execute_app(app_name: str, dataset_name: str, deployment_name: str, version_name: str | None = None, env_type: str | None = None, additional_headers: Dict[str, str] | None = None, show_progress: bool = True, verify_ssl: bool = True, progress_callback: Callable[[int, int], None] | None = None, sample_callback: Callable | None = None) → DatasetRunResult

Run a dataset on a deployment with parallel execution.

This method fetches the dataset samples and deployment configuration, then runs all samples against the deployment endpoint using the configured parallelism and retry settings.

Note: This is an async method and must be called with await: >>> result = await client.execute_app(“my-app”, “test-dataset”, “prod-deployment”)

Parameters:

app_namestr

Application name

dataset_namestr

Dataset name to run

deployment_namestr

Deployment name to run against

version_namestr, optional

Version name to include in dc_fields

env_typestr, optional

Environment type to include in dc_fields

additional_headersdict, optional

Additional headers to include in requests (beyond deployment headers)

show_progressbool, default=True

Whether to show a live progress widget (if in notebook with ipywidgets installed)

verify_sslbool, default=True

Whether to verify SSL certificates

progress_callbackcallable, optional

Callback function that receives (completed_count, total_count) after each sample completes

sample_callbackcallable, optional

Callback function that receives (sample_id, status, keyword args) for each sample status update. Keyword args may include: input, output, error, duration, retries

Returns:

DatasetRunResult

Results of running the dataset, including success/failure counts, individual sample results, and timing information.

Examples

>>> result = await client.execute_app("my-app", "test-dataset", "prod-deployment")
>>> print(f"Success rate: {result.success_rate:.1f}%")
>>> print(f"Failed samples: {result.failed_samples}")
>>> # Re-run only failed samples
>>> if result.failed_samples > 0:
...     retry_result = await result.rerun_failed()
...     print(f"Retry success rate: {retry_result.success_rate:.1f}%")