Module neuralops.utils.results_saver

Unified ResultsSaver re-exported under framework.utils.results_saver Integrates persistence, structured markdown/json report generation, Django API compatibility, and Hugging Face Hub uploads.

Classes

class ResultsSaver (run_id: str,
base_dir: pathlib.Path,
config: dict[str, typing.Any] | None = None)
Expand source code 
class ResultsSaver(PrimaryResultsSaver):
    """
    Enhanced ResultsSaver compatible with Django API, experiment runners, and CLI scripts.
    Supports structured report generation and Hugging Face Hub uploads.
    """

    def __init__(self, run_id: str, base_dir: Path, config: dict[str, Any] | None = None) -> None:
        super().__init__(run_id=run_id, base_dir=base_dir, config=config)
        self.config = config or {}
        self.legacy_base_dir = Path(base_dir) / str(run_id)
        self.legacy_base_dir.mkdir(parents=True, exist_ok=True)

    def save_config(self, config: dict[str, Any]) -> None:
        """Persist config to disk in structured format."""
        self.config = config
        self._save_config(config)
        self._write_json("../config.json", config)
        logger.info(f"Saved structured config to {self.run_dir}")

    def save_history(self, history: dict[str, Any]) -> None:
        """Persist training history to disk."""
        self._save_history(history)
        self._write_json("../history.json", history)
        logger.info(f"Saved training history to {self.run_dir}")

    def save_final_metrics(self, metrics: dict[str, Any]) -> None:
        """Persist final metrics and generate run report."""
        self._save_final_metrics(metrics)
        self._write_json("../final_metrics.json", metrics)
        self._generate_run_report(metrics)
        logger.info(f"Saved final metrics and generated run report to {self.run_dir}")

    def generate_plots(self, history: dict[str, Any]) -> None:
        """Generate plots."""
        self._generate_plots(history, {})

    def should_save_model(self, metrics: dict[str, Any]) -> bool:
        """Evaluate whether heavy model artifacts should be saved."""
        return self.should_save_heavy(metrics)

    def handle_storage_backend(self) -> None:
        """Execute configured storage backends including HuggingFace Hub."""
        self.push_all()

    def push_to_hf(self, repo_id: str | None = None) -> None:
        """Upload complete run artifacts to HuggingFace Hub for resource management."""
        cfg = {"hf_options": {"repo_id": repo_id}} if repo_id else self.cfg
        hf_backend.push(self.run_id, self.run_dir, cfg)

    def _generate_run_report(self, metrics: dict[str, Any]) -> None:
        """Generate structured run summary report."""
        report = {
            "run_id": self.run_id,
            "status": "COMPLETED",
            "metrics": metrics,
            "config": self.config
            or (self.cfg.to_dict() if hasattr(self.cfg, "to_dict") else str(self.cfg)),
        }
        self._write_json("artifacts/run_report.json", report)
        self._save_model_card(self.config or {}, metrics)

Enhanced ResultsSaver compatible with Django API, experiment runners, and CLI scripts. Supports structured report generation and Hugging Face Hub uploads.

Initialize ResultsSaver directory layouts and configuration.

Args
-----=
run_id
Unique identifier string for this run.
base_dir
Base directory where run directory is created.
config
Optional configuration dictionary.

Ancestors

Methods

def generate_plots(self, history: dict[str, typing.Any]) ‑> None
Expand source code 
def generate_plots(self, history: dict[str, Any]) -> None:
    """Generate plots."""
    self._generate_plots(history, {})

Generate plots.

def handle_storage_backend(self) ‑> None
Expand source code 
def handle_storage_backend(self) -> None:
    """Execute configured storage backends including HuggingFace Hub."""
    self.push_all()

Execute configured storage backends including HuggingFace Hub.

def push_to_hf(self, repo_id: str | None = None) ‑> None
Expand source code 
def push_to_hf(self, repo_id: str | None = None) -> None:
    """Upload complete run artifacts to HuggingFace Hub for resource management."""
    cfg = {"hf_options": {"repo_id": repo_id}} if repo_id else self.cfg
    hf_backend.push(self.run_id, self.run_dir, cfg)

Upload complete run artifacts to HuggingFace Hub for resource management.

def save_config(self, config: dict[str, typing.Any]) ‑> None
Expand source code 
def save_config(self, config: dict[str, Any]) -> None:
    """Persist config to disk in structured format."""
    self.config = config
    self._save_config(config)
    self._write_json("../config.json", config)
    logger.info(f"Saved structured config to {self.run_dir}")

Persist config to disk in structured format.

def save_final_metrics(self, metrics: dict[str, typing.Any]) ‑> None
Expand source code 
def save_final_metrics(self, metrics: dict[str, Any]) -> None:
    """Persist final metrics and generate run report."""
    self._save_final_metrics(metrics)
    self._write_json("../final_metrics.json", metrics)
    self._generate_run_report(metrics)
    logger.info(f"Saved final metrics and generated run report to {self.run_dir}")

Persist final metrics and generate run report.

def save_history(self, history: dict[str, typing.Any]) ‑> None
Expand source code 
def save_history(self, history: dict[str, Any]) -> None:
    """Persist training history to disk."""
    self._save_history(history)
    self._write_json("../history.json", history)
    logger.info(f"Saved training history to {self.run_dir}")

Persist training history to disk.

def should_save_model(self, metrics: dict[str, typing.Any]) ‑> bool
Expand source code 
def should_save_model(self, metrics: dict[str, Any]) -> bool:
    """Evaluate whether heavy model artifacts should be saved."""
    return self.should_save_heavy(metrics)

Evaluate whether heavy model artifacts should be saved.

Inherited members