Reference: integrations (flashruntime.integrations)

Framework adapters that build a CommandWorkload for you. Each is a small, framework-neutral function — the recipe axis of the four-axis architecture. No adapter imports its framework at the top level (the import is paid only in your training process).

Signatures below are exact; each entry says, in one line, why it exists.

integrations.pytorch (fr_torch)

def ddp(
    script,
    *,
    source=".",
    nproc_per_node=2,
    nnodes=1,
    script_args="",
    env=None,
) -> CommandWorkload: ...

Builds the torchrun launch command for a PyTorch script — the launch conventions only; DDP is wired by your code (or flashruntime.torch.prepare). Emits torchrun --nproc-per-node=N --nnodes=1 --standalone --local-addr=127.0.0.1 <script> <args>. nnodes > 1 raises NotImplementedError (multi-node rendezvous is a later slice). See the PyTorch guide.

integrations.sklearn (fr_sklearn)

def sweep(
    script,
    task_params,
    *,
    source=".",
    metric="accuracy_mean",
    maximize=True,
    python="python",
) -> CommandWorkload: ...

def hpo(script, grid, **kwargs) -> CommandWorkload: ...

• sweep — one independent task per params dict; sets outputs.primary_metric=metric so run.best_trial() needs no arguments.
• hpo — Cartesian-grid sugar over sweep ({"model": ["logreg", "rf"], "C": [0.1, 1]} → 4 trials).

Distributes across runs, never inside a single .fit(). See the scikit-learn guide.

integrations.huggingface (fr_hf)

def trainer(script, *, source=".", nproc_per_node=1, script_args="") -> CommandWorkload: ...

def latest_checkpoint(output_dir) -> str | None: ...

def flashruntime_callback(): ...             # returns a transformers TrainerCallback

• trainer — a thin wrapper over pytorch.ddp(); launching an HF Trainer job is just the PyTorch path (Trainer wraps DDP/FSDP internally under torchrun).
• latest_checkpoint — newest checkpoint-* dir with a valid manifest (or None); pass straight to trainer.train(resume_from_checkpoint=...).
• flashruntime_callback — a TrainerCallback whose on_save commits a verified manifest and whose on_log relays metrics through flashruntime.torch.log_metrics. The transformers import is paid inside this factory, in your process.

See the Hugging Face guide.

The extensibility pattern

Every adapter returns a CommandWorkload and reuses the same launch/collect/recover machinery. To teach FlashRuntime a new framework, write a function under flashruntime/integrations/ that returns a CommandWorkload describing what to run (and, if the framework has hooks, maps them onto write_manifest / log_metrics). No core change is required — that is the four-axis payoff. The SDK reference documents the CommandWorkload shape you build.