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set cpu threads in config

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This commit is contained in:
robcaulk
2022-08-15 18:01:15 +02:00
parent 57c488a6f1
commit bf7ceba958
9 changed files with 159 additions and 21 deletions
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19
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@@ -56,7 +56,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary, pair)
model = self.fit_rl(data_dictionary, pair, dk)
if pair not in self.dd.historic_predictions:
self.set_initial_historic_predictions(
@@ -69,7 +69,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
return model
@abstractmethod
def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen):
"""
Agent customizations and abstract Reinforcement Learning customizations
go in here. Abstract method, so this function must be overridden by
@@ -164,6 +164,21 @@ class BaseReinforcementLearningModel(IFreqaiModel):
for return_str in dk.data['extra_returns_per_train']:
hist_preds_df[return_str] = 0
# TODO take care of this appendage. Right now it needs to be called because FreqAI enforces it.
# But FreqaiRL needs more objects passed to fit() (like DK) and we dont want to go refactor
# all the other existing fit() functions to include dk argument. For now we instantiate and
# leave it.
def fit(self, data_dictionary: Dict[str, Any], pair: str = '') -> Any:
"""
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
management will be properly handled by Freqai.
:param data_dictionary: Dict = the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
return
class MyRLEnv(Base3ActionRLEnv):

7
freqtrade/freqai/data_drawer.py
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@@ -471,11 +471,12 @@ class FreqaiDataDrawer:
elif model_type == 'keras':
from tensorflow import keras
model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
elif model_type == 'stable_baselines':
elif model_type == 'stable_baselines_ppo':
from stable_baselines3.ppo.ppo import PPO
model = PPO.load(dk.data_path / f"{dk.model_filename}_model.zip")
elif model_type == 'stable_baselines_dqn':
from stable_baselines3 import DQN
#model = PPO.load(dk.data_path / f"{dk.model_filename}_model.zip")
model = DQN.load(dk.data_path / f"best_model.zip")
model = DQN.load(dk.data_path / f"{dk.model_filename}_model.zip")
if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")

2
freqtrade/freqai/prediction_models/CatboostClassifier.py
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@@ -16,7 +16,7 @@ class CatboostClassifier(BaseClassifierModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict[str, Any], pair: str = '') -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:

2
freqtrade/freqai/prediction_models/CatboostRegressor.py
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@@ -17,7 +17,7 @@ class CatboostRegressor(BaseRegressionModel):
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
def fit(self, data_dictionary: Dict[str, Any], pair: str = '') -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold

13
freqtrade/freqai/prediction_models/ReinforcementLearningPPO.py
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@@ -9,9 +9,9 @@ import torch as th
from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.monitor import Monitor
# from stable_baselines3.common.vec_env import SubprocVecEnv
from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@@ -22,7 +22,7 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen):
agent_params = self.freqai_info['model_training_parameters']
reward_params = self.freqai_info['model_reward_parameters']
@@ -44,7 +44,7 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
eval_env = Monitor(eval, ".")
eval_env.reset()
path = self.dk.data_path
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/ppo/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
@@ -54,7 +54,8 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
net_arch=[256, 256, 128])
model = PPO('MlpPolicy', train_env, policy_kwargs=policy_kwargs,
tensorboard_log=f"{path}/ppo/tensorboard/", learning_rate=0.00025, gamma=0.9, verbose=1
tensorboard_log=f"{path}/ppo/tensorboard/", learning_rate=0.00025,
gamma=0.9, verbose=1
)
model.learn(
@@ -62,9 +63,11 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
callback=eval_callback
)
best_model = PPO.load(dk.data_path / "best_model.zip")
print('Training finished!')
return model
return best_model
class MyRLEnv(Base3ActionRLEnv):

15
freqtrade/freqai/prediction_models/ReinforcementLearningPPO_multiproc.py
View File

@@ -13,7 +13,9 @@ from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.utils import set_random_seed
from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
import gym
logger = logging.getLogger(__name__)
@@ -42,7 +44,7 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen):
agent_params = self.freqai_info['model_training_parameters']
reward_params = self.freqai_info['model_reward_parameters']
@@ -58,16 +60,15 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
len(test_df.index))
env_id = "train_env"
train_num_cpu = 6
num_cpu = int(dk.thread_count / 2)
train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, price, reward_params,
self.CONV_WIDTH) for i in range(train_num_cpu)])
eval_num_cpu = 6
eval_env_id = 'eval_env'
eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, price_test, reward_params,
self.CONV_WIDTH) for i in range(eval_num_cpu)])
self.CONV_WIDTH) for i in range(num_cpu)])
path = self.dk.data_path
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/ppo/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
@@ -85,10 +86,12 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
callback=eval_callback
)
# TODO get callback working so the best model is saved. For now we save last model
# best_model = PPO.load(dk.data_path / "best_model.zip")
print('Training finished!')
eval_env.close()
return model
return model # best_model
class MyRLEnv(Base3ActionRLEnv):

11
freqtrade/freqai/prediction_models/ReinforcementLearningTDQN.py
View File

@@ -7,9 +7,12 @@ from stable_baselines3.common.monitor import Monitor
from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.RL.TDQNagent import TDQN
from stable_baselines3 import DQN
from stable_baselines3.common.buffers import ReplayBuffer
import numpy as np
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@@ -18,7 +21,7 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen):
agent_params = self.freqai_info['model_training_parameters']
reward_params = self.freqai_info['model_reward_parameters']
@@ -40,7 +43,7 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
eval_env = Monitor(eval, ".")
eval_env.reset()
path = self.dk.data_path
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/tdqn/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
@@ -63,9 +66,11 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
callback=eval_callback
)
best_model = DQN.load(dk.data_path / "best_model.zip")
print('Training finished!')
return model
return best_model
class MyRLEnv(Base3ActionRLEnv):