diff --git a/.install.sh b/.install.sh
deleted file mode 100644
index ada87db..0000000
--- a/.install.sh
+++ /dev/null
@@ -1,23 +0,0 @@
-pip install -r requirements.txt
-
-cd d3m
-pip install -e .
-cd ..
-
-cd tods/common-primitives
-pip install -e .
-cd ../..
-
-cd tods/common-primitives/sklearn-wrap
-pip install -e .
-cd ../../..
-
-cd tods 
-pip3 install -e .
-cd ..
-
-cd axolotl
-pip3 install -e .
-pip3 install -e .[cpu]
-cd ..
-
diff --git a/examples/evaluate_default_pipeline.py b/examples/evaluate_default_pipeline.py
deleted file mode 100644
index 1c6b7b5..0000000
--- a/examples/evaluate_default_pipeline.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import pandas as pd
-
-from searcher import schemas as schemas_utils
-from searcher.utils import generate_dataset_problem, evaluate_pipeline
-
-table_path = 'datasets/yahoo_sub_5.csv'
-target_index = 6 # what column is the target
-
-#table_path = 'datasets/NAB/realTweets/labeled_Twitter_volume_IBM.csv' # The path of the dataset
-time_limit = 30 # How many seconds you wanna search
-#metric = 'F1' # F1 on label 1
-metric = 'F1_MACRO' # F1 on both label 0 and 1
-
-# Read data and generate dataset and problem
-df = pd.read_csv(table_path)
-dataset, problem_description = generate_dataset_problem(df, target_index=target_index, metric=metric)
-
-# Load the default pipeline
-pipeline = schemas_utils.load_default_pipeline()
-
-# Run the pipeline
-pipeline_result = evaluate_pipeline(problem_description, dataset, pipeline)
-print(pipeline_result)
-