def generate_job_name()

in runtool/runtool/experiments_converter.py [0:0]

• 22 lines of code
• 4 McCabe index (conditional complexity)

    def generate_job_name(self, run: int) -> str:
        """
        Generates a training job name for a sagemaker job.

        There is a hierarchy of how a job should be named where any expression
        provided within the `job.job_name_expression` has highest priority.
        Thereafter if a `$job_name` key exists in `job.experiment["algorithm"]`
        or in `job.experiment["dataset"]` its value will be used.
        If no custom name has been provided, a default training job name is
        generated.
        """
        # user naming convention has highest priority
        if self.job_name_expression:
            return apply_trial(
                node={"$eval": self.job_name_expression},
                locals=DotDict(
                    dict(
                        __trial__=self.experiment,
                        run=run,
                        run_configuration=self.run_configuration,
                    )
                ),
            )

        # thereafter any jobnames added within the config has prio
        if "$job_name" in self.experiment["algorithm"]:
            return self.experiment["algorithm"]["$job_name"]

        # job names in the dataset has lower priority
        if "$job_name" in self.experiment["dataset"]:
            return self.experiment["dataset"]["$job_name"]

        # fallback on default naming
        return (
            f"config-{reproducible_hash(self.experiment)}"
            f"-date-{self.creation_time}"
            f"-runid-{reproducible_hash(self.run_configuration)}"
            f"-run-{run}"
        )