Skip to content

geno_lewm.training.fixture

fixture

Deterministic fixture-tier trainer for the geno-lewm-train smoke path.

This module is intentionally not the Carbon-backed trainer. It gives the release workflow a clean-machine, dependency-light training command that exercises config resolution, checkpointing, deterministic resume, metrics, logs, and training-run metadata before the heavy ML stack is available.

FixtureTrainingReport dataclass 

FixtureTrainingReport(run_id: str, run_dir: Path, steps_requested: int, steps_completed: int, resumed_from_step: int, final_loss: float, checkpoint_path: Path, metrics_path: Path, log_path: Path, config_path: Path, dataset_manifest_path: Path, training_metadata_path: Path)

Summary returned by the deterministic fixture trainer.

run_fixture_training 

run_fixture_training(*, config: GenoLeWMConfig, run_dir: Path, steps: int = 50, resume_from: Path | None = None, command: str, commit_sha: str, package_version: str) -> FixtureTrainingReport

Run a deterministic scalar smoke trainer and write release artifacts.

steps is the target total step count. When resume_from is supplied, the checkpoint's current step must be lower than steps; the resumed run continues with the same deterministic sample stream.

Source code in geno_lewm/training/fixture.py 
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
def run_fixture_training(
    *,
    config: GenoLeWMConfig,
    run_dir: Path,
    steps: int = 50,
    resume_from: Path | None = None,
    command: str,
    commit_sha: str,
    package_version: str,
) -> FixtureTrainingReport:
    """Run a deterministic scalar smoke trainer and write release artifacts.

    ``steps`` is the target total step count. When ``resume_from`` is
    supplied, the checkpoint's current step must be lower than ``steps``;
    the resumed run continues with the same deterministic sample stream.
    """
    _require_positive_steps(steps)
    run_dir.mkdir(parents=True, exist_ok=True)
    config_path = write_resolved_config(config, run_dir / "config.resolved.yaml")
    dataset_manifest_path = _write_fixture_dataset_manifest(config, run_dir)

    state = (
        _load_checkpoint(resume_from, expected_seed=config.seed)
        if resume_from is not None
        else _initial_state(config.seed)
    )
    resumed_from_step = state.step
    if resumed_from_step >= steps:
        raise InputError(
            "fixture resume checkpoint is already at or beyond --steps",
            details={"checkpoint_step": resumed_from_step, "steps": steps},
        )

    log_path = run_dir / FIXTURE_LOG_NAME
    mode = "a" if resumed_from_step else "w"
    with log_path.open(mode, encoding="utf-8") as log:
        if resumed_from_step:
            log.write(
                f"resume_from_step={resumed_from_step} target_steps={steps} seed={config.seed}\n"
            )
        for step in range(resumed_from_step + 1, steps + 1):
            state = _train_step(state, step=step)
            log.write(
                f"step={step} loss={state.loss:.12f} weight={state.weight:.12f} "
                "collapse_var_min=1.0 nan_loss=false\n"
            )

    checkpoint_path = run_dir / FIXTURE_CHECKPOINT_NAME
    _write_checkpoint(config, state, checkpoint_path)
    metrics_path = run_dir / FIXTURE_METRICS_NAME
    _write_metrics(config, state, metrics_path, steps=steps, resumed_from_step=resumed_from_step)
    training_metadata_path = run_dir / FIXTURE_TRAINING_METADATA_NAME
    _write_training_metadata(
        config,
        training_metadata_path,
        command=command,
        commit_sha=commit_sha,
        package_version=package_version,
    )
    return FixtureTrainingReport(
        run_id=config.run_id,
        run_dir=run_dir,
        steps_requested=steps,
        steps_completed=state.step,
        resumed_from_step=resumed_from_step,
        final_loss=state.loss,
        checkpoint_path=checkpoint_path,
        metrics_path=metrics_path,
        log_path=log_path,
        config_path=config_path,
        dataset_manifest_path=dataset_manifest_path,
        training_metadata_path=training_metadata_path,
    )