control number of threads, update doc

docs/freqai.md

@@ -131,7 +131,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 |  |  *Reinforcement Learning Parameters**
 | `rl_config` | A dictionary containing the control parameters for a Reinforcement Learning model. <br> **Datatype:** Dictionary.
 | `train_cycles` | Training time steps will be set based on the `train_cycles * number of training data points. <br> **Datatype:** Integer.
-| `thread_count` | Number of threads to dedicate to the Reinforcement Learning training process. <br> **Datatype:** int.
+| `cpu_count` | Number of processors to dedicate to the Reinforcement Learning training process. <br> **Datatype:** int.
 | `max_trade_duration_candles`| Guides the agent training to keep trades below desired length. Example usage shown in `prediction_models/ReinforcementLearner.py` within the user customizable `calculate_reward()` <br> **Datatype:** int.
 | `model_type` | Model string from stable_baselines3 or SBcontrib. Available strings include: `'TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO', 'PPO', 'A2C', 'DQN'`. User should ensure that `model_training_parameters` match those available to the corresponding stable_baselines3 model by visiting their documentaiton. [PPO doc](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) (external website) <br> **Datatype:** string.
 | `policy_type` | One of the available policy types from stable_baselines3 <br> **Datatype:** string.

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -39,7 +39,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
 
     def __init__(self, **kwargs):
         super().__init__(config=kwargs['config'])
-        th.set_num_threads(self.freqai_info['rl_config'].get('thread_count', 4))
+        self.max_threads = max(self.freqai_info['rl_config'].get(
+            'cpu_count', 0), int(self.max_system_threads / 2))
+        th.set_num_threads(self.max_threads)
         self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
         self.train_env: Union[SubprocVecEnv, gym.Env] = None
         self.eval_env: Union[SubprocVecEnv, gym.Env] = None

freqtrade/freqai/data_kitchen.py

@@ -9,6 +9,7 @@ from typing import Any, Dict, List, Tuple
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
+import psutil
 from pandas import DataFrame
 from scipy import stats
 from sklearn import linear_model
@@ -95,7 +96,10 @@ class FreqaiDataKitchen:
             )
 
         self.data['extra_returns_per_train'] = self.freqai_config.get('extra_returns_per_train', {})
-        self.thread_count = self.freqai_config.get("data_kitchen_thread_count", -1)
+        if not self.freqai_config.get("data_kitchen_thread_count", 0):
+            self.thread_count = int(psutil.cpu_count() * 2 - 2)
+        else:
+            self.thread_count = self.freqai_config["data_kitchen_thread_count"]
         self.train_dates: DataFrame = pd.DataFrame()
         self.unique_classes: Dict[str, list] = {}
         self.unique_class_list: list = []

freqtrade/freqai/freqai_interface.py

@@ -11,6 +11,7 @@ from typing import Any, Dict, List, Optional, Tuple
 
 import numpy as np
 import pandas as pd
+import psutil
 from numpy.typing import NDArray
 from pandas import DataFrame
 
@@ -96,6 +97,7 @@ class IFreqaiModel(ABC):
         self._threads: List[threading.Thread] = []
         self._stop_event = threading.Event()
         self.strategy: Optional[IStrategy] = None
+        self.max_system_threads = int(psutil.cpu_count() * 2 - 2)
 
     def __getstate__(self):
         """

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -73,18 +73,17 @@ class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
         test_df = data_dictionary["test_features"]
 
         env_id = "train_env"
-        num_cpu = int(self.freqai_info["rl_config"].get("cpu_count", 2))
         self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1, train_df, prices_train,
                                         self.reward_params, self.CONV_WIDTH, monitor=True,
                                         config=self.config) for i
-                                        in range(num_cpu)])
+                                        in range(self.max_threads)])
 
         eval_env_id = 'eval_env'
         self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
                                                 test_df, prices_test,
                                                 self.reward_params, self.CONV_WIDTH, monitor=True,
                                                 config=self.config) for i
-                                       in range(num_cpu)])
+                                       in range(self.max_threads)])
         self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
                                           render=False, eval_freq=len(train_df),
                                           best_model_save_path=str(dk.data_path))