deactivate tensorboard by default

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -68,11 +68,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         self.unset_outlier_removal()
         self.net_arch = self.rl_config.get('net_arch', [128, 128])
         self.dd.model_type = import_str
-        if self.activate_tensorboard:
-            self.tensorboard_callback: TensorboardCallback = \
-                TensorboardCallback(verbose=1, actions=BaseActions)
-        else:
-            self.tenorboard_callback = None
+        self.tensorboard_callback: TensorboardCallback = \
+            TensorboardCallback(verbose=1, actions=BaseActions)
 
     def unset_outlier_removal(self):
         """
@@ -159,10 +156,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
                                           best_model_save_path=str(dk.data_path))
 
         actions = self.train_env.get_actions()
-        if self.activate_tensorboard:
-            self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
-        else:
-            self.tensorboard_callback = None  # type: ignore
+        self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
 
     def pack_env_dict(self, pair: str) -> Dict[str, Any]:
         """

freqtrade/freqai/freqai_interface.py

@@ -110,7 +110,7 @@ class IFreqaiModel(ABC):
         if self.ft_params.get('principal_component_analysis', False) and self.continual_learning:
             self.ft_params.update({'principal_component_analysis': False})
             logger.warning('User tried to use PCA with continual learning. Deactivating PCA.')
-        self.activate_tensorboard: bool = self.freqai_info.get('activate_tensorboard', True)
+        self.activate_tensorboard: bool = self.freqai_info.get('activate_tensorboard', False)
 
         record_params(config, self.full_path)
 

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -58,10 +58,14 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
         policy_kwargs = dict(activation_fn=th.nn.ReLU,
                              net_arch=self.net_arch)
 
+        if self.activate_tensorboard:
+            tb_path = Path(dk.full_path / "tensorboard" / dk.pair.split('/')[0])
+        else:
+            tb_path = None
+
         if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
             model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
-                                    tensorboard_log=Path(
-                                        dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
+                                    tensorboard_log=tb_path,
                                     **self.freqai_info.get('model_training_parameters', {})
                                     )
         else:
@@ -70,14 +74,9 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             model = self.dd.model_dictionary[dk.pair]
             model.set_env(self.train_env)
 
-        callbacks = [self.eval_callback]
-
-        if self.activate_tensorboard:
-            callbacks.append(self.tensorboard_callback)
-
         model.learn(
             total_timesteps=int(total_timesteps),
-            callback=callbacks,
+            callback=[self.eval_callback, self.tensorboard_callback],
             progress_bar=self.rl_config.get('progress_bar', False)
         )
 

tests/freqai/test_freqai_interface.py

@@ -55,9 +55,9 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
 
     can_run_model(model)
 
-    test_tb = True
-    if is_mac():
-        test_tb = False
+    # test_tb = True
+    # if is_mac():
+    #     test_tb = False
 
     model_save_ext = 'joblib'
     freqai_conf.update({"freqaimodel": model})
@@ -94,7 +94,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
     strategy.freqai_info = freqai_conf.get("freqai", {})
     freqai = strategy.freqai
     freqai.live = True
-    freqai.activate_tensorboard = test_tb
+    # freqai.activate_tensorboard = test_tb
     freqai.can_short = can_short
     freqai.dk = FreqaiDataKitchen(freqai_conf)
     freqai.dk.live = True
@@ -233,7 +233,7 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
         ("CatboostRegressor", 2, "freqai_test_strat"),
         ("PyTorchMLPRegressor", 2, "freqai_test_strat"),
         ("PyTorchTransformerRegressor", 2, "freqai_test_strat"),
-        ("ReinforcementLearner", 3, "freqai_rl_test_strat"),
+        ("ReinforcementLearner", 2, "freqai_rl_test_strat"),
         ("XGBoostClassifier", 2, "freqai_test_classifier"),
         ("LightGBMClassifier", 2, "freqai_test_classifier"),
         ("CatboostClassifier", 2, "freqai_test_classifier"),
@@ -242,9 +242,9 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
     )
 def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog):
     can_run_model(model)
-    test_tb = True
-    if is_mac():
-        test_tb = False
+    # test_tb = True
+    # if is_mac():
+    #     test_tb = False
 
     freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
     freqai_conf['runmode'] = RunMode.BACKTEST
@@ -277,7 +277,7 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
     strategy.freqai_info = freqai_conf.get("freqai", {})
     freqai = strategy.freqai
     freqai.live = False
-    freqai.activate_tensorboard = test_tb
+    # freqai.activate_tensorboard = test_tb
     freqai.dk = FreqaiDataKitchen(freqai_conf)
     timerange = TimeRange.parse_timerange("20180110-20180130")
     freqai.dd.load_all_pair_histories(timerange, freqai.dk)