Module neuralops.results_saver.config

SaveConfig – dataclass that controls all aspects of result persistence.

Classes

class SaveConfig (save_model: bool = True,
save_checkpoints: bool = True,
save_optimizer: bool = False,
save_logs: bool = True,
save_plots: bool = True,
save_artifacts: bool = True,
create_model_version: bool = True,
auto_save_if_better_than: float | None = None,
plot_options: dict[str, typing.Any] = <factory>,
storage_backends: list[str] = <factory>,
git_options: dict[str, typing.Any] = <factory>,
dvc_options: dict[str, typing.Any] = <factory>,
experiment_trackers: list[str] = <factory>,
model_export_formats: list[str] = <factory>,
model_registries: list[str] = <factory>,
metadata_store: str | None = None,
docker_options: dict[str, typing.Any] = <factory>,
release_options: dict[str, typing.Any] = <factory>,
notification: dict[str, typing.Any] = <factory>,
cloud_options: dict[str, typing.Any] = <factory>)

Expand source code

@dataclass
class SaveConfig:
    """User‑facing configuration for result persistence."""

    # ── Core switches ──────────────────────────────────────────
    save_model: bool = True
    save_checkpoints: bool = True
    save_optimizer: bool = False
    save_logs: bool = True
    save_plots: bool = True
    save_artifacts: bool = True
    create_model_version: bool = True

    # ── Auto‑save threshold ────────────────────────────────────
    auto_save_if_better_than: float | None = None

    # ── Plot options (passed to framework.analysis.plotting) ──
    plot_options: dict[str, Any] = field(
        default_factory=lambda: {
            "loss_curve": True,
            "accuracy_curve": True,
            "confusion_matrix": False,
            "attention_heatmap": False,
            "gradient_flow": False,
            "lr_schedule": True,
            "gpu_memory": False,
            "training_time": True,
            "parameter_count": False,
            "rank_allocation": False,
            "pareto_front": False,
            "radar_chart": False,
            "loss_smooth": 0.6,
            "figsize": [10, 6],
            "dpi": 150,
            "class_names": None,
        }
    )

    # ── Storage backends (local, s3, gcp, azure, hf_hub, etc.) ─
    storage_backends: list[str] = field(default_factory=lambda: ["local"])

    # ── Git / DVC ──────────────────────────────────────────────
    git_options: dict[str, Any] = field(default_factory=dict)
    dvc_options: dict[str, Any] = field(default_factory=dict)

    # ── Experiment trackers (wandb, mlflow, neptune, etc.) ─────
    experiment_trackers: list[str] = field(default_factory=list)

    # ── Model export formats (onnx, torchscript, tflite, ...) ─
    model_export_formats: list[str] = field(default_factory=list)

    # ── Model registries (hf_hub, oci, ...) ────────────────────
    model_registries: list[str] = field(default_factory=list)

    # ── Metadata store (openmetadata, etc.) ────────────────────
    metadata_store: str | None = None

    # ── Docker / OCI options ───────────────────────────────────
    docker_options: dict[str, Any] = field(default_factory=dict)

    # ── GitHub Release options ─────────────────────────────────
    release_options: dict[str, Any] = field(default_factory=dict)

    # ── Notifications (slack, email, etc.) ────────────────────
    notification: dict[str, Any] = field(default_factory=dict)

    # ── Cloud credentials / endpoints (for S3, GCP, etc.) ─────
    cloud_options: dict[str, Any] = field(default_factory=dict)

    @classmethod
    def from_dict(cls, d: Any) -> "SaveConfig":
        """Build SaveConfig from dict or return if already SaveConfig."""
        if isinstance(d, cls):
            return d
        if not isinstance(d, dict):
            return cls()
        valid = {k for k in d if k in cls.__dataclass_fields__}
        return cls(**{k: d[k] for k in valid})

User‑facing configuration for result persistence.

Static methods

def from_dict(d: Any) ‑> SaveConfig

Build SaveConfig from dict or return if already SaveConfig.

Instance variables

var auto_save_if_better_than : float | None

The type of the None singleton.

var cloud_options : dict[str, typing.Any]

The type of the None singleton.

var create_model_version : bool

The type of the None singleton.

var docker_options : dict[str, typing.Any]

The type of the None singleton.

var dvc_options : dict[str, typing.Any]

The type of the None singleton.

var experiment_trackers : list[str]

The type of the None singleton.

var git_options : dict[str, typing.Any]

The type of the None singleton.

var metadata_store : str | None

The type of the None singleton.

var model_export_formats : list[str]

The type of the None singleton.

var model_registries : list[str]

The type of the None singleton.

var notification : dict[str, typing.Any]

The type of the None singleton.

var plot_options : dict[str, typing.Any]

The type of the None singleton.

var release_options : dict[str, typing.Any]

The type of the None singleton.

var save_artifacts : bool

The type of the None singleton.

var save_checkpoints : bool

The type of the None singleton.

var save_logs : bool

The type of the None singleton.

var save_model : bool

The type of the None singleton.

var save_optimizer : bool

The type of the None singleton.

var save_plots : bool

The type of the None singleton.

var storage_backends : list[str]

The type of the None singleton.