diff --git a/data_loader/load.py b/data_loader/load.py
index 7a107bf..5529e75 100644
--- a/data_loader/load.py
+++ b/data_loader/load.py
@@ -7,7 +7,7 @@ from feature_extractors.types import FeatureExtractor
 from utils.helpers import drop_columns_if_exist
 from data_loader.collections import DataCollection
 from typing import Literal
-import ray
+
 import os
 from config.hashing import hash_data_config
 from .types import XDataFrame, ReturnSeries, ForwardReturnSeries
@@ -53,8 +53,8 @@ def __load_data(
     ]
     files = other_files + other_assets
 
-    target_asset_future = [
-        __load_df.remote(
+    target_asset_df = [
+        __load_df(
             data_source=data_source,
             prefix=data_source[1],
             returns="log_returns",
@@ -62,18 +62,16 @@ def __load_data(
         )
         for data_source in target_file
     ]
-    target_asset_df = ray.get(target_asset_future)
 
-    target_asset_only_returns_future = __load_df.remote(
+    df_target_asset_only_returns = __load_df(
         data_source=target_file[0],
         prefix=target_file[0][1],
         returns="returns",
         feature_extractors=[],
     )
-    df_target_asset_only_returns = ray.get(target_asset_only_returns_future)
 
-    asset_futures = [
-        __load_df.remote(
+    asset_dfs = [
+        __load_df(
             data_source=data_source,
             prefix=data_source[1],
             returns="log_returns",
@@ -81,10 +79,9 @@ def __load_data(
         )
         for data_source in files
     ]
-    asset_dfs = ray.get(asset_futures)
 
-    exogenous_futures = [
-        __load_df.remote(
+    exogenous_dfs = [
+        __load_df(
             data_source=data_source,
             prefix=data_source[1],
             returns="none",
@@ -92,7 +89,6 @@ def __load_data(
         )
         for data_source in exogenous_data
     ]
-    exogenous_dfs = ray.get(exogenous_futures)
 
     X = target_asset_df + asset_dfs + exogenous_dfs
     X = pd.concat([df.sort_index().reindex(X[0].index) for df in X], axis=1).fillna(0.0)
@@ -107,7 +103,6 @@ def __load_data(
     return X, returns
 
 
-@ray.remote
 def __load_df(
     data_source: DataSource,
     prefix: str,
@@ -162,7 +157,7 @@ def load_only_returns(
 ) -> pd.DataFrame:
 
     assets_future = [
-        __load_df.remote(
+        __load_df(
             data_source=data_source,
             prefix=data_source[1],
             returns=returns,
@@ -170,7 +165,7 @@ def load_only_returns(
         )
         for data_source in assets
     ]
-    dfs = ray.get(assets_future)
+    dfs = assets_future
 
     dfs = pd.concat(dfs, axis=1)
     dfs.index = pd.DatetimeIndex(dfs.index)
diff --git a/environment.yml b/environment.yml
index 76af740..d7879c1 100644
--- a/environment.yml
+++ b/environment.yml
@@ -7,19 +7,21 @@ channels:
   - ranaroussi
 dependencies:
   - python=3.9
-  - scikit-learn-intelex=2021.4.0
+  - numba=0.53.1
+  - xgboost
+  - scikit-learn
+  - lightgbm
+  - pydantic
+  - seaborn
+  - black
+  - ipython
+  - ipykernel
+  - scipy
+  - pip
   - pip:
     - black
-    - skorch
     - fracdiff
-    - ray
     - diskcache
-    - seaborn
-    - ipython
-    - ipykernel
-    - scipy
-    - scikit-learn
-    - numba
     - matplotlib
     - numpy
     - quantstats
@@ -29,11 +31,8 @@ dependencies:
     - tscv
     - tqdm
     - pandas-ta
-    - xgboost
-    - lightgbm
     - alphalens-reloaded
     - vectorbt
-    - pydantic
     - pandera[mypy]
     - binance_historical_data
 prefix: /usr/local/anaconda3/envs/quant
diff --git a/models/model_map.py b/models/model_map.py
index d60eec3..ad96621 100644
--- a/models/model_map.py
+++ b/models/model_map.py
@@ -1,6 +1,5 @@
 from models.sklearn import SKLearnModel
-from sklearnex.ensemble import RandomForestClassifier
-from sklearnex.ensemble import RandomForestRegressor
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
 from .base import Model
 
 default_feature_selector_classification = SKLearnModel(
@@ -24,7 +23,7 @@ def get_model(model_name: str) -> Model:
             )
         )
     elif model_name == "LogisticRegression_three_class":
-        from sklearnex.linear_model import LogisticRegression as LogisticRegression_EX
+        from sklearn.linear_model import LogisticRegression as LogisticRegression_EX
 
         return set_name(
             SKLearnModel(
diff --git a/reporting/saving.py b/reporting/saving.py
index ce414ab..048357d 100644
--- a/reporting/saving.py
+++ b/reporting/saving.py
@@ -15,7 +15,6 @@ def save_models(pipeline_outcome: PipelineOutcome, config: Config) -> None:
     date_string = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")
 
     if not os.path.exists("output/models"):
-        warnings.warn("No folder exists, creating one.")
         os.makedirs("output/models")
 
     pickle.dump(dict_for_pickle, open("output/models/{}.p".format(date_string), "wb"))
diff --git a/run_pipeline.py b/run_pipeline.py
index 170dbab..b7db9c2 100644
--- a/run_pipeline.py
+++ b/run_pipeline.py
@@ -17,10 +17,6 @@ from training.directional_training import train_directional_model
 from training.bet_sizing import bet_sizing_with_meta_model
 from training.types import PipelineOutcome
 
-import ray
-
-ray.init()
-
 
 def run_pipeline(
     project_name: str, with_wandb: bool, sweep: bool, raw_config: RawConfig