opt local trainer (better mem releasing) (#1116)

* opt local trainer (better mem releasing)

* Update setup.py

* Update data.py

* fix CI

qlib/config.py

@@ -75,6 +75,17 @@ class Config:
     def set_conf_from_C(self, config_c):
         self.update(**config_c.__dict__["_config"])
 
+    def register_from_C(self, config, skip_register=True):
+        from .utils import set_log_with_config  # pylint: disable=C0415
+
+        if C.registered and skip_register:
+            return
+
+        C.set_conf_from_C(config)
+        if C.logging_config:
+            set_log_with_config(C.logging_config)
+        C.register()
+
 
 # pickle.dump protocol version: https://docs.python.org/3/library/pickle.html#data-stream-format
 PROTOCOL_VERSION = 4

qlib/data/data.py

@@ -32,7 +32,6 @@ from ..utils import (
     hash_args,
     normalize_cache_fields,
     code_to_fname,
-    set_log_with_config,
     time_to_slc_point,
     read_period_data,
     get_period_list,
@@ -603,11 +602,7 @@ class DatasetProvider(abc.ABC):
         """
         # FIXME: Windows OS or MacOS using spawn: https://docs.python.org/3.8/library/multiprocessing.html?highlight=spawn#contexts-and-start-methods
         # NOTE: This place is compatible with windows, windows multi-process is spawn
-        if not C.registered:
+        C.register_from_C(g_config)
-            C.set_conf_from_C(g_config)
-            if C.logging_config:
-                set_log_with_config(C.logging_config)
-            C.register()
 
         obj = dict()
         for field in column_names:

qlib/model/trainer.py

@@ -15,13 +15,22 @@ import socket
 from typing import Callable, List
 
 from tqdm.auto import tqdm
+
+from qlib.config import C
 from qlib.data.dataset import Dataset
+from qlib.data.dataset.weight import Reweighter
+from qlib.log import get_module_logger
 from qlib.model.base import Model
-from qlib.utils import flatten_dict, init_instance_by_config, auto_filter_kwargs, fill_placeholder
+from qlib.utils import (
+    auto_filter_kwargs,
+    fill_placeholder,
+    flatten_dict,
+    init_instance_by_config,
+)
+from qlib.utils.paral import call_in_subproc
 from qlib.workflow import R
 from qlib.workflow.recorder import Recorder
 from qlib.workflow.task.manage import TaskManager, run_task
-from qlib.data.dataset.weight import Reweighter
 
 
 def _log_task_info(task_config: dict):
@@ -210,17 +219,19 @@ class TrainerR(Trainer):
     STATUS_BEGIN = "begin_task_train"
     STATUS_END = "end_task_train"
 
-    def __init__(self, experiment_name: str = None, train_func: Callable = task_train):
+    def __init__(self, experiment_name: str = None, train_func: Callable = task_train, call_in_subproc: bool = False):
         """
         Init TrainerR.
 
         Args:
             experiment_name (str, optional): the default name of experiment.
             train_func (Callable, optional): default training method. Defaults to `task_train`.
+            call_in_subproc (bool): call the process in subprocess to force memory release
         """
         super().__init__()
         self.experiment_name = experiment_name
         self.train_func = train_func
+        self._call_in_subproc = call_in_subproc
 
     def train(self, tasks: list, train_func: Callable = None, experiment_name: str = None, **kwargs) -> List[Recorder]:
         """
@@ -245,6 +256,9 @@ class TrainerR(Trainer):
             experiment_name = self.experiment_name
         recs = []
         for task in tqdm(tasks, desc="train tasks"):
+            if self._call_in_subproc:
+                get_module_logger("TrainerR").info("running models in sub process (for forcing release memroy).")
+                train_func = call_in_subproc(train_func, C)
             rec = train_func(task, experiment_name, **kwargs)
             rec.set_tags(**{self.STATUS_KEY: self.STATUS_BEGIN})
             recs.append(rec)

qlib/utils/__init__.py

@@ -949,6 +949,10 @@ def auto_filter_kwargs(func: Callable, warning=True) -> Callable:
 
     The decrated function will ignore and give warning when the parameter is not acceptable
 
+    For example, if you have a function `f` which may optionally consume the keywards `bar`.
+    then you can call it by `auto_filter_kwargs(f)(bar=3)`, which will automatically filter out
+    `bar` when f does not need bar
+
     Parameters
     ----------
     func : Callable

qlib/utils/paral.py

@@ -10,6 +10,9 @@ from joblib._parallel_backends import MultiprocessingBackend
 import pandas as pd
 
 from queue import Queue
+import concurrent
+
+from qlib.config import C, QlibConfig
 
 
 class ParallelExt(Parallel):
@@ -273,3 +276,40 @@ def complex_parallel(paral: Parallel, complex_iter):
         dt.set_res(res)
     complex_iter = _recover_dt(complex_iter)
     return complex_iter
+
+
+class call_in_subproc:
+    """
+    When we repeating run functions, it is hard to avoid memory leakage.
+    So we run it in the subprocess to ensure it is OK.
+
+    NOTE: Because local object can't be pickled. So we can't implement it via closure.
+          We have to implement it via callable Class
+    """
+
+    def __init__(self, func: Callable, qlib_config: QlibConfig = None):
+        """
+        Parameters
+        ----------
+        func : Callable
+            the function to be wrapped
+
+        qlib_config : QlibConfig
+            Qlib config for initialization in subprocess
+
+        Returns
+        -------
+        Callable
+        """
+        self.func = func
+        self.qlib_config = qlib_config
+
+    def _func_mod(self, *args, **kwargs):
+        """Modify the initial function by adding Qlib initialization"""
+        if self.qlib_config is not None:
+            C.register_from_C(self.qlib_config)
+        return self.func(*args, **kwargs)
+
+    def __call__(self, *args, **kwargs):
+        with concurrent.futures.ProcessPoolExecutor(max_workers=1) as executor:
+            return executor.submit(self._func_mod, *args, **kwargs).result()