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multiprocessing support

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lzh222333
2021-05-24 05:07:38 +00:00
parent 8c3a08b18d
commit b24af7fff6
6 changed files with 145 additions and 26 deletions
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10
examples/model_rolling/task_manager_rolling.py
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@@ -4,6 +4,7 @@
"""
This example shows how a TrainerRM works based on TaskManager with rolling tasks.
After training, how to collect the rolling results will be shown in task_collecting.
Based on the ability of TaskManager, `worker` method offer a simple way for multiprocessing.
"""
from pprint import pprint
@@ -13,10 +14,10 @@ import qlib
from qlib.config import REG_CN
from qlib.workflow import R
from qlib.workflow.task.gen import RollingGen, task_generator
from qlib.workflow.task.manage import TaskManager
from qlib.workflow.task.manage import TaskManager, run_task
from qlib.workflow.task.collect import RecorderCollector
from qlib.model.ens.group import RollingGroup
from qlib.model.trainer import TrainerRM
from qlib.model.trainer import TrainerRM, task_train
data_handler_config = {
@@ -122,6 +123,11 @@ class RollingTaskExample:
trainer = TrainerRM(self.experiment_name, self.task_pool)
trainer.train(tasks)
def worker(self):
# train tasks by other progress or machines for multiprocessing. It is same as TrainerRM.worker.
print("========== worker ==========")
run_task(task_train, self.task_pool, experiment_name=self.experiment_name)
def task_collecting(self):
print("========== task_collecting ==========")