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"""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()