--- a
+++ b/nnunet/paths.py
@@ -0,0 +1,58 @@
+#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+#
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
+
+import os
+from batchgenerators.utilities.file_and_folder_operations import maybe_mkdir_p, join
+
+# do not modify these unless you know what you are doing
+my_output_identifier = "nnUNet"
+default_plans_identifier = "nnUNetPlansv2.1"
+default_data_identifier = 'nnUNetData_plans_v2.1'
+default_trainer = "nnUNetTrainerV2"
+default_cascade_trainer = "nnUNetTrainerV2CascadeFullRes"
+
+"""
+PLEASE READ paths.md FOR INFORMATION TO HOW TO SET THIS UP
+"""
+
+base = os.environ['nnUNet_raw_data_base'] if "nnUNet_raw_data_base" in os.environ.keys() else None
+preprocessing_output_dir = os.environ['nnUNet_preprocessed'] if "nnUNet_preprocessed" in os.environ.keys() else None
+network_training_output_dir_base = os.path.join(os.environ['RESULTS_FOLDER']) if "RESULTS_FOLDER" in os.environ.keys() else None
+
+if base is not None:
+    nnUNet_raw_data = join(base, "nnUNet_raw_data")
+    nnUNet_cropped_data = join(base, "nnUNet_cropped_data")
+    maybe_mkdir_p(nnUNet_raw_data)
+    maybe_mkdir_p(nnUNet_cropped_data)
+else:
+    print("nnUNet_raw_data_base is not defined and nnU-Net can only be used on data for which preprocessed files "
+          "are already present on your system. nnU-Net cannot be used for experiment planning and preprocessing like "
+          "this. If this is not intended, please read documentation/setting_up_paths.md for information on how to set this up properly.")
+    nnUNet_cropped_data = nnUNet_raw_data = None
+
+if preprocessing_output_dir is not None:
+    maybe_mkdir_p(preprocessing_output_dir)
+else:
+    print("nnUNet_preprocessed is not defined and nnU-Net can not be used for preprocessing "
+          "or training. If this is not intended, please read documentation/setting_up_paths.md for information on how to set this up.")
+    preprocessing_output_dir = None
+
+if network_training_output_dir_base is not None:
+    network_training_output_dir = join(network_training_output_dir_base, my_output_identifier)
+    maybe_mkdir_p(network_training_output_dir)
+else:
+    print("RESULTS_FOLDER is not defined and nnU-Net cannot be used for training or "
+          "inference. If this is not intended behavior, please read documentation/setting_up_paths.md for information on how to set this "
+          "up.")
+    network_training_output_dir = None