"""RunPod Flash provider: managed, serverless GPUs (no Docker) for Flash. Fine-tuning runs on a dedicated RunPod GPU provisioned by Flash. A decorated Python handler (``train._train_body``) executes ``flash.engine.worker`` on the GPU; Flash handles provisioning, dependency install, execution, and scale-to-zero teardown. Serving exposes an OpenAI-compatible endpoint for a trained LoRA adapter. ``PROVIDER`` is the ``base.Provider`` implementation the registry hands out; the orchestrator/allocator only talk to its interface, never these modules directly. """ from __future__ import annotations from typing import Any from flash.providers.base import GpuClass, JobHandle, PollResult, Provider class RunpodProvider: """``base.Provider`` for the RunPod Flash substrate.""" name = "runpod" def is_configured(self) -> bool: # RunPod is the ALWAYS-ON default substrate, so it is always "available" for # allocation (offline pricing degrades to the static snapshot, and a missing # RUNPOD_API_KEY surfaces at provision time via ensure_auth / the preflight — # never as a silent empty candidate list). This matches the historical # ``available_providers()`` which listed runpod unconditionally. return True def preflight(self, require_hf: bool = True) -> list[str]: from flash.providers.runpod.preflight import missing_credentials return missing_credentials(require_hf=require_hf) def gpu_classes(self) -> list[GpuClass]: from flash.providers.runpod.gpus import gpu_classes return gpu_classes() def hourly_rate(self, gpu: str) -> float: from flash.providers.runpod.pricing import hourly_rate return hourly_rate(gpu) def submit_run( self, spec, seed: int, *, log: Any = None, on_handle: Any = None, attempt: int = 0, offers: Any = None, exclude_machine_ids: Any = frozenset(), ) -> PollResult: # ``offers``/``exclude_machine_ids`` are Vast live-market concerns; RunPod # provisions a fresh serverless endpoint and never re-searches a market, so it # ignores both (kept in the signature for cross-provider symmetry). from flash.providers.runpod.jobs import submit_run return submit_run(spec, seed, log=log, on_handle=on_handle, attempt=attempt) def poll(self, handle: JobHandle, spec, seed: int, *, log: Any = None) -> PollResult: from flash.providers.runpod.jobs import JobHandle as RunpodJobHandle from flash.providers.runpod.jobs import ( make_hf_heartbeat_reader, poll_job, stall_kwargs, ) hf_repo = spec.train.hf_repo prefix = f"{spec.phase}/{spec.run_id}/seed{seed}" reader = make_hf_heartbeat_reader(hf_repo, prefix) if hf_repo else None rh = RunpodJobHandle.from_dict(handle.to_dict()) if log is not None: print(f"attaching: job={rh.job_id} endpoint={rh.endpoint_name}", file=log, flush=True) # Same stall tuning as the submit path so a reattached run isn't judged differently. return poll_job(rh, log=log, heartbeat_reader=reader, **stall_kwargs()) def cancel(self, handle: JobHandle) -> None: from flash.providers.runpod import api as runpod_api d = handle.to_dict() if d.get("endpoint_id") and d.get("job_id"): runpod_api.cancel_job(d["endpoint_id"], d["job_id"]) def destroy(self, handle: JobHandle) -> None: from flash.providers.runpod import api as runpod_api d = handle.to_dict() if d.get("endpoint_id"): runpod_api.delete_endpoint(d["endpoint_id"]) def gc(self, spec) -> None: from flash.providers.runpod.train import terminate_endpoint terminate_endpoint(spec.gpu.type, spec.run_id) def sweep_orphans(self, active_labels: set[str] | None = None) -> list[int]: # No-op: RunPod serverless endpoints have no standing per-run billing to reap on # crash recovery (a failed-before-submit endpoint is GC'd by reconstructed name in # recover_runs). Present for ``base.Provider`` symmetry with Vast's instance sweep. return [] PROVIDER: Provider = RunpodProvider()