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Listing jobs

list_jobs(self)
List all jobs associated with the API key.
Returns: list: A list of job details.

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Getting job status

get_job_status(self, job_id: str)
Get the status of a job by its ID.
Parameters:
  • job_id (str): The ID of the job to retrieve the status for.
Returns: dict: The status of the job.

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Getting job results

get_job_results(self, job_id: str, include_inputs: bool = False, include_cumulative_logprobs: bool = False, with_original_df: pl.DataFrame | pd.DataFrame = None, output_column: str = 'inference_result')
Get the results of a job by its ID.
Parameters:
  • job_id (str): The ID of the job to retrieve the results for.
  • include_inputs (bool, optional): Whether to include the inputs in the results. Defaults to False.
  • include_cumulative_logprobs (bool, optional): Whether to include the cumulative logprobs in the results. Defaults to False.
  • with_original_df (Union[pl.DataFrame, pd.DataFrame], optional): Original DataFrame to join results with. Defaults to None.
  • output_column (str, optional): Name of the column containing results. Defaults to “inference_result”.
  • disable_cache (bool, optional): Whether to disable reading from or writing to the local job results cache. Defaults to False.
  • unpack_json (bool, optional): If the output_column is formatted as a JSON string, decides whether to unpack the top level JSON fields in the results into separate columns. Defaults to True.
Returns: Union[pl.DataFrame, pd.DataFrame]: Results as a DataFrame.
  • If with_original_df is provided: Returns the same type as the input DataFrame with results added as a new column
  • If with_original_df is None: Returns a polars DataFrame by default
The DataFrame will contain:
  • inputs column (if include_inputs=True). Each cell contains the input string given to the model.
  • inference_result column (or custom name via output_column)
  • cumulative_logprobs column (if include_cumulative_logprobs=True)
Example: 
# Get just the results
results = sutro.get_job_results(job_id)
# Returns: pl.DataFrame with one column 'inference_result'

# Get results with inputs
results = sutro.get_job_results(job_id, include_inputs=True)
# Returns: pl.DataFrame with columns ['inputs', 'inference_result']

# Add results back to original DataFrame
df_with_results = sutro.get_job_results(job_id, with_original_df=original_df)
# Returns: Same type as original_df with 'inference_result' column added. Matches the return shape of .infer(...) when stay_attached=True.

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Cancelling jobs

cancel_job(self, job_id: str)
Cancel a job by its ID.
Parameters:
  • job_id (str): The ID of the job to cancel.
Returns: dict: The status of the job cancellation.