def run(self):
if self.num_trials >= self.total_trials:
self._exit()
has_told_study = []
for trial_idx in range(self.num_trials):
work_name = f"objective_work_{trial_idx}"
if work_name not in self.ws:
objective_work = ObjectiveWork(
script_path=self.script_path,
data_dir=self.data_dir,
cloud_compute=L.CloudCompute("cpu"),
)
self.ws[work_name] = objective_work
if not self.ws[work_name].has_started:
trial = self._study.ask(ObjectiveWork.distributions())
self.ws[work_name].run(trial_id=trial._trial_id,
**trial.params)
if self.ws[work_name].metric and not self.ws[
work_name].has_told_study:
self._study.tell(self.ws[work_name].trial_id,
self.ws[work_name].metric)
self.ws[work_name].has_told_study = True
has_told_study.append(self.ws[work_name].has_told_study)
if all(has_told_study):
self.num_trials += self.simultaneous_trials
def __init__(self):
super().__init__()
self.source_work = SourceFileWork()
self.dest_work = DestinationFileAndServeWork(
script_path=os.path.join(os.path.dirname(__file__), "scripts/serve.py"),
port=1111,
parallel=False, # runs until killed.
cloud_compute=L.CloudCompute(),
raise_exception=True,
)
def run(self):
# create dynamically the source_work at runtime
if "src_w" not in self.dict:
self.dict["src_w"] = SourceFileWork()
self.dict["src_w"].run()
if self.dict["src_w"].has_succeeded:
# create dynamically the dst_w at runtime
if "dst_w" not in self.dict:
self.dict["dst_w"] = DestinationFileAndServeWork(
script_path=os.path.join(os.path.dirname(__file__),
"scripts/serve.py"),
port=1111,
parallel=False, # runs until killed.
cloud_compute=L.CloudCompute(),
raise_exception=True,
)
# the flow passes the file from one work to another.
self.dict["dst_w"].run(self.dict["src_w"].boring_path)
self._exit("Boring App End")
import argparse
import lightning as L
class Work(L.LightningWork):
def __init__(self, cloud_compute):
super().__init__(cloud_compute=cloud_compute)
def run(self):
pass
class Flow(L.LightningFlow):
def __init__(self, cloud_compute):
super().__init__()
self.work = Work(cloud_compute)
def run(self):
assert self.work.cloud_compute.name == "gpu", self.work.cloud_compute.name
self._exit()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--use_gpu", action="store_true", default=False, help="Whether to use GPU in the cloud")
hparams = parser.parse_args()
app = L.LightningApp(Flow(L.CloudCompute("gpu" if hparams.use_gpu else "cpu")))
def __init__(self,
cloud_compute: L.CloudCompute = L.CloudCompute(),
**kwargs):
super().__init__(parallel=True, **kwargs, cloud_compute=cloud_compute)
self.boring_path = None
def __init__(self):
super().__init__()
self.runner = TracerPythonScript(
"train.py",
cloud_compute=L.CloudCompute("gpu"),
)