Revert "Use slimmer version of flytekit (#583)"

This reverts commit 346c22f.

cookbook/case_studies/ml_training/house_price_prediction/house_price_predictor.py

@@ -63,8 +63,8 @@
 SPLIT_RATIOS = [0.6, 0.3, 0.1]
 
 # %%
-# Data Generation 
-# ==================
+# Data Generation
+# =====================
 #
 # We define a function to compute the price of a house based on multiple factors (such as ``number of bedrooms``, ``number of bathrooms``, ``area``, ``garage space`` and ``year built``).
 def gen_price(house) -> int:
@@ -119,7 +119,7 @@ def gen_houses(num_houses) -> pd.DataFrame:
 #
 # We create two columns from the dataset- `feature` and `target`. 
 #
-# Next, we split these columns into train and test subsets. We also split the input DataFrame into train, validation, and test datasets.
+#Next, we split these columns into train and test subsets. We also split the input DataFrame into train, validation, and test datasets.
 def split_data(
     df: pd.DataFrame, seed: int, split: typing.List[float]
 ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
@@ -258,9 +258,6 @@ def house_price_predictor_trainer(
 
 
 # %%
-# Running the Model Locally
-# ==========================
-#
 # We can run the workflow locally provided the required libraries are installed. The output would be a list of house prices, generated using the XGBoost model.
 if __name__ == "__main__":
     print(house_price_predictor_trainer())

cookbook/case_studies/ml_training/house_price_prediction/multiregion_house_price_predictor.py

@@ -72,8 +72,8 @@
 # %%
 # Data Generation and Preprocessing 
 # ====================================
-#
 # We call the :ref:`data generation <Data Generation>` and :ref:`data preprocessing <Data Preprocessing and Splitting>` to generate and split the data. We return the result as DataFrames.
+#
 # Now, let's create a ``NamedTuple`` that maps variable names to their respective data type.
 dataset = typing.NamedTuple(
     "GenerateSplitDataOutputs",
@@ -158,9 +158,6 @@ def multi_region_house_price_prediction_model_trainer(
 
 
 # %%
-# Running the Model Locally
-# ==========================
-#
 # We can run the workflow locally provided the required libraries are installed. The output would be a list of lists of house prices based on region, generated using the XGBoost model.
 if __name__ == "__main__":
     print(multi_region_house_price_prediction_model_trainer())