scikit-learn guide

FlashRuntime operates your scikit-learn job — it never rewrites your estimator. You keep the model and the scoring; FlashRuntime fans a grid out into independent tasks, runs them, collects each metrics.json, and ranks the results.

The rule that shapes this whole adapter: sklearn work is embarrassingly parallel across runs, never inside a single .fit(). FlashRuntime fans a grid into one independent task per trial — it never tries to split one .fit() call, which would change the math.

For a worked walkthrough, do the sklearn sweeps tutorial.

The contract: flags in, metrics.json out

Your script needs zero FlashRuntime imports. It reads hyperparameters from CLI flags and writes a flat metrics.json to its working directory. That is the entire contract — the same one every framework uses. examples/user_sklearn/train.py is plain sklearn end to end.

Fan a grid out

The integrations.sklearn adapter builds the workload from that script:

import flashruntime as flash
from flashruntime.integrations import sklearn as fr_sklearn

run = flash.submit(fr_sklearn.hpo(
    "train.py",
    {"model": ["logreg", "rf"], "C": [0.1, 1.0], "n_estimators": [50]},
    source="examples/user_sklearn",
))
print(f"state={run.state.value}  trials={len(run.trials)}")
print("best:", run.best_trial())     # ranks by outputs.primary_metric

• hpo(script, grid, **kwargs) expands a Cartesian grid ({"model": ["logreg", "rf"], "C": [0.1, 1]} → 4 trials) and delegates to sweep.
• sweep(script, task_params, *, source=".", metric="accuracy_mean", maximize=True, python="python") takes an explicit list of param dicts — use it when you want a hand-picked, non-Cartesian set.

Each {placeholder} in the built command is filled from the trial's params, so train.py receives --model rf --C 1.0 and friends. Because sweep sets outputs.primary_metric=metric, run.best_trial() needs no arguments — it returns the trial with the highest accuracy_mean (or lowest, when maximize=False).

Why the fan-out is correct by construction

• Sequential and isolated. flash.submit() runs one trial at a time and copies each trial's metrics.json out before the next trial can overwrite it.
• Independent trees. Each trial gets its own job-scoped checkpoint tree, so trials never cross-contaminate.
• Add fault tolerance the usual way. flash.submit(..., max_restarts=1) retries a transient trial failure and fails fast on a deterministic one (a bad flag combination that raises the same error every time). See the fault-tolerance tutorial.

Adding another framework

The sklearn adapter is a ~40-line function that builds a CommandWorkload with task_params set for fan-out. A new framework adapter follows the same pattern: a small function under flashruntime/integrations/ that returns a CommandWorkload describing what to run, then reuses the same launch/collect/rank machinery. The PyTorch adapter is the coordinated-run counterpart, and Hugging Face is a thin wrapper over it — no core change is needed to teach FlashRuntime a new framework.