added 2nd component

input_components/get_final_predicted_tags/README.md

+
+# Get final predicted tags
+
+## Overview
+
+- **Component Name** : Get final predicted tags
+- **Component Description** :  
+- **Component Type** : Transform type
+
+
+## Component Param
+
+Variable Name |Datatype |Required/Optional |Default Value |Type |Description |Example
+--- |--- |--- |--- |--- |--- |--- |
+input |String |Required |None |inputPath | |
+output |String |Required |None |outputPath | |
+
+> Note 1 : Available Component types are: Input, Transform, Output.
+
+> Note 2 : Available Environment types are: env, InputValues, InputPath, OutputValues, OutputPath, PipelineParm
+
+        
\ No newline at end of file

input_components/get_final_predicted_tags/build_component.py

+import kfp
+from loguru import logger
+from src import program
+import yaml
+import inspect
+import os
+
+function = \
+    [func[1] for func in inspect.getmembers(program, inspect.isfunction) if inspect.getmodule(func[1]) == program][0]
+
+
+def read_data_from_yaml(path):
+    """
+    It opens the file at the given path, reads the contents, and then parses the contents as YAML
+
+    :param path: The path to the YAML file
+    :return: A dictionary
+    """
+    with open(path, "r") as stream:
+        return yaml.load(stream, Loader=yaml.FullLoader)
+
+
+def get_component_yml():
+    """
+
+    :param file_name:
+    :return:
+    """
+    try:
+        requirements = list()
+        with open('requirements.txt', 'r') as file:
+            for line in file:
+                if "=" in line and "#" not in line:
+                    requirements.append(line.strip())
+                elif "#" in line:
+                    ...
+                else:
+                    logger.exception(f"Mentioned package does not have version {line.strip()}")
+
+        date_function_yml = kfp.components.func_to_component_text(
+            function, packages_to_install=requirements)
+
+        variables_path = "variables.yml"
+
+        if os.path.exists(variables_path):
+
+            yaml_data: dict = read_data_from_yaml(variables_path)
+            if yaml_data:
+                envs: dict = yaml_data.get("deployment", {}).get("environmentVar", [])
+                python_version: str = yaml_data.get("deployment", {}).get("pythonVersion", None)
+                if python_version is not None and python_version in ["3.7", "3.8", "3.9", "3.10"]:
+                    date_function_yml = kfp.components.func_to_component_text(
+                        function, packages_to_install=requirements, base_image=f"python:{python_version}")
+                date_function = date_function_yml + f"    env:\n"
+                for env_var in envs:
+                    date_function += f"      {env_var['name']}: '{env_var['value']}'\n"
+                with open('component.yml', 'w') as file:
+                    file.write(date_function)
+            else:
+                with open('component.yml', 'w') as file:
+                    file.write(date_function_yml)
+        else:
+            with open('component.yml', 'w') as file:
+                file.write(date_function_yml)
+
+
+
+    except Exception as e:
+        logger.exception(f"Unable to get the component yml {e}")
+
+
+def create_table(data, key):
+    """
+
+    :return:
+    """
+    try:
+        rows_list = list()
+        for each_input in data.get(key, []):
+            rows_dict = dict()
+            rows_dict['name'] = each_input.get("name", '')
+            rows_dict['data_type'] = each_input.get('type', 'String')
+            if each_input.get('optional'):
+                req_opt = "Optional"
+                default_value = each_input.get('default', '')
+            else:
+                req_opt = "Required"
+                default_value = "None"
+            rows_dict['req_opt'] = req_opt
+            rows_dict['default_value'] = default_value
+
+            for each_arg in data.get('implementation', {}).get('container', {}).get('args', []):
+                if type(each_arg) == dict and rows_dict['name'] in each_arg.values():
+                    rows_dict['Type'] = list(each_arg.keys())[0]
+                    rows_dict['Description'] = each_input.get('description', '')
+                    rows_dict['Example'] = ''
+
+            rows_list.append(list(rows_dict.values()))
+
+        if key == "inputs" and os.path.exists("variables.yml"):
+            yaml_data: dict = read_data_from_yaml("variables.yml")
+            if yaml_data:
+                env_var = yaml_data.get("deployment", {}).get("environmentVar", [])
+                for each in env_var:
+                    env_dict = dict()
+                    env_dict['name'] = each.get("name")
+                    env_dict['data_type'] = "String"
+                    env_dict['req_opt'] = "Required"
+                    env_dict['default_value'] = "None"
+                    env_dict['Type'] = "env"
+                    env_dict['description'] = ""
+                    env_dict['example'] = ""
+                    rows_list.append(list(env_dict.values()))
+
+        return rows_list
+    except Exception as e:
+        logger.exception(f"Unable to create the table for README.MD file {e}")
+
+
+def create_readme():
+    """
+    Function is to create the readme file for the given components details
+    :return: Create the README.MD file in the given path
+    """
+    try:
+
+        note_1 = "Note 1 : Available Component types are: Input, Transform, Output."
+
+        note_2 = "Note 2 : Available Environment types are: env, InputValues, InputPath, OutputValues," \
+                 " OutputPath, PipelineParm"
+
+        column_list = ["Variable Name", "Datatype", "Required/Optional", "Default Value", "Type", "Description",
+                       "Example"]
+
+        with open("component.yml", "r") as file:
+            data = yaml.safe_load(file)
+
+        if "inputs" in list(data.keys()) and "outputs" in list(data.keys()):
+            component_type = "Transform type"
+        elif "inputs" not in data:
+            component_type = "Input type"
+        else:
+            component_type = "Output type"
+
+        component_overview_json = dict()
+
+        component_overview_json['Component Name'] = data.get("name", " ")
+        component_overview_json['Component Description'] = data.get("description", " ")
+        component_overview_json['Component Type'] = component_type
+
+        rows_list_input = create_table(data, "inputs")
+        rows_list_output = create_table(data, "outputs")
+
+        rows_list = rows_list_input + rows_list_output
+
+        header = component_overview_json.get("Component Name")
+
+        table_header = " |".join(column_list) + "\n"
+        table_line = "--- |" * len(column_list) + "\n"
+        table_body = "\n".join(map(lambda x: " |".join(x), rows_list))
+        table = table_header + table_line + table_body
+        readme = f"""
+# {header}
+
+## {"Overview"}
+
+- **Component Name** : {component_overview_json.get("Component Name")}
+- **Component Description** : {component_overview_json.get("Component Description")}
+- **Component Type** : {component_overview_json.get("Component Type")}
+
+
+## Component Param
+
+{table}
+
+> {note_1}
+
+> {note_2}
+
+        """
+
+        with open('README.md', 'w') as f:
+            f.write(readme)
+
+
+    except Exception as e:
+        logger.exception(f"Unable to create the README.MD file {e}")
+
+
+if __name__ == "__main__":
+    get_component_yml()
+    create_readme()

input_components/get_final_predicted_tags/component.yml

+name: Get final predicted tags
+inputs:
+- {name: input}
+outputs:
+- {name: output}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'loguru==0.5.3' 'pandas==1.3.*' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m
+      pip install --quiet --no-warn-script-location 'loguru==0.5.3' 'pandas==1.3.*'
+      --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def get_final_predicted_tags(input_path, output_path):
+          class CommonConstants:
+              dalmia_string_level_tags = 'dalmia_string_level_tags'
+              bgrimm_string_level_tags = 'bgrimm_string_level_tags'
+              panel_id = 'panel_id'
+              sub_id = 'sub_id'
+              inv_id_mppt_id = 'inv_id_mppt_id'
+              tags_property_raw = 'raw'
+              tags_property_predicted = 'predicted'
+              tags_property_efficiency = 'efficiency'
+              bgrim_tags_property_efficiency = 'Efficiency'
+              tags_property_efficiency_inv = 'efficiency'
+              tags_property_efficiency_plant = 'efficiency_plant'
+              mppt_coefficients = 'mppt_coefficients'
+              inv_id = 'inv_id'
+              mppt_id = 'mppt_id'
+              datetime = 'datetime'
+              predicted_current_mppt = 'predicted_current_mppt'
+              predicted_current_mppt_tag = 'predicted_current_mppt_tag'
+              actual_current_mppt = 'actual_current_mppt'
+              hour = 'hour'
+              skip_time = {"morning": {"start": 0, "end": 6},
+                           "evening": {"start": 18, "end": 23}}
+              efficiency_mppt = 'efficiency_mppt'
+              efficiency_inv = 'efficiency_inv'
+              efficiency_plant = 'efficiency_plant'
+              tag_name = 'tag_name'
+              parameter_name = 'parameter_name'
+              timestamp = 'timestamp'
+              tag_id = 'tag_id'
+              efficiency_mppt_tag = 'efficiency_mppt_tag'
+              voltage = 'voltage'
+              current = 'current'
+              Potential = 'Potential'
+              Degradation = 'Degradation'
+              tilt_irradiance = 'tilt_irradiance'
+              voltage_mppt = 'voltage_mppt'
+              current_mppt = 'current_mppt'
+              date = 'date'
+              asia_kolkata_timezone = 'Asia/Kolkata'
+              asia_bangkok_timezone = 'Asia/Bangkok'
+              coefficient = 'coefficient'
+              cumulative_actual_current_mppt = 'cumulative_actual_current_mppt'
+              cumulative_predicted_current_mppt = 'cumulative_predicted_current_mppt'
+              day = "day"
+              time = "time"
+
+          import pandas as pd
+          from loguru import logger
+          import json
+
+          try:
+              with open(input_path, 'r') as f:
+                  get_tags_component_output_dict = json.load(f)
+              print(get_tags_component_output_dict)
+              df_predicted_tags = get_tags_component_output_dict.get("df_predicted_tags")
+              df_efficiency_tags = get_tags_component_output_dict.get("df_efficiency_tags")
+              df_predicted_tags = pd.DataFrame(df_predicted_tags)
+              df_efficiency_tags = pd.DataFrame(df_efficiency_tags)
+              df_predicted_tags = df_predicted_tags.copy()
+              df_efficiency_tags_copy = df_efficiency_tags.copy()
+
+              df_predicted_tags.rename(columns={CommonConstants.tag_id: CommonConstants.predicted_current_mppt_tag},
+                                       inplace=True)
+
+              df_efficiency_tags_copy.rename(columns={CommonConstants.tag_id: CommonConstants.efficiency_mppt_tag},
+                                             inplace=True)
+
+              df_predicted_tags[CommonConstants.inv_id_mppt_id] = df_predicted_tags[CommonConstants.sub_id] + '_' + \
+                                                                  df_predicted_tags[CommonConstants.inv_id] + '_' + \
+                                                                  df_predicted_tags[CommonConstants.mppt_id]
+
+              df_efficiency_tags_copy[CommonConstants.inv_id_mppt_id] = df_efficiency_tags_copy[CommonConstants.sub_id] \
+                                                                        + '_' + \
+                                                                        df_efficiency_tags_copy[CommonConstants.inv_id] \
+                                                                        + '_' + \
+                                                                        df_efficiency_tags_copy[CommonConstants.mppt_id]
+
+              df_predicted_and_efficiency_tags = pd.merge(df_predicted_tags,
+                                                          df_efficiency_tags_copy[[CommonConstants.efficiency_mppt_tag,
+                                                                                   CommonConstants.inv_id_mppt_id]],
+                                                          on=CommonConstants.inv_id_mppt_id,
+                                                          how='left')
+              df_predicted_and_efficiency_tags.reset_index(drop=True, inplace=True)
+              final_dict = {"df_predicted_and_efficiency_tags": df_predicted_and_efficiency_tags.to_dict(orient="records")}
+              with open(output_path, 'w') as f:
+                  json.dump(final_dict, f)
+              print(final_dict)
+          except Exception as e:
+              logger.exception(f'Exception - {e}')
+
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Get final predicted tags', description='')
+      _parser.add_argument("--input", dest="input_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--output", dest="output_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = get_final_predicted_tags(**_parsed_args)
+    args:
+    - --input
+    - {inputPath: input}
+    - --output
+    - {outputPath: output}
+    env:

input_components/get_final_predicted_tags/requirements.txt

+loguru==0.5.3
+pandas==1.3.*
\ No newline at end of file

input_components/get_final_predicted_tags/src/program.py

+from kfp.components import InputPath, OutputPath
+
+
+def get_final_predicted_tags(input_path: InputPath(), output_path: OutputPath()):
+    class CommonConstants:
+        dalmia_string_level_tags = 'dalmia_string_level_tags'
+        bgrimm_string_level_tags = 'bgrimm_string_level_tags'
+        panel_id = 'panel_id'
+        sub_id = 'sub_id'
+        inv_id_mppt_id = 'inv_id_mppt_id'
+        tags_property_raw = 'raw'
+        tags_property_predicted = 'predicted'
+        tags_property_efficiency = 'efficiency'
+        bgrim_tags_property_efficiency = 'Efficiency'
+        tags_property_efficiency_inv = 'efficiency'
+        tags_property_efficiency_plant = 'efficiency_plant'
+        mppt_coefficients = 'mppt_coefficients'
+        inv_id = 'inv_id'
+        mppt_id = 'mppt_id'
+        datetime = 'datetime'
+        predicted_current_mppt = 'predicted_current_mppt'
+        predicted_current_mppt_tag = 'predicted_current_mppt_tag'
+        actual_current_mppt = 'actual_current_mppt'
+        hour = 'hour'
+        skip_time = {"morning": {"start": 0, "end": 6},
+                     "evening": {"start": 18, "end": 23}}
+        efficiency_mppt = 'efficiency_mppt'
+        efficiency_inv = 'efficiency_inv'
+        efficiency_plant = 'efficiency_plant'
+        tag_name = 'tag_name'
+        parameter_name = 'parameter_name'
+        timestamp = 'timestamp'
+        tag_id = 'tag_id'
+        efficiency_mppt_tag = 'efficiency_mppt_tag'
+        voltage = 'voltage'
+        current = 'current'
+        Potential = 'Potential'
+        Degradation = 'Degradation'
+        tilt_irradiance = 'tilt_irradiance'
+        voltage_mppt = 'voltage_mppt'
+        current_mppt = 'current_mppt'
+        date = 'date'
+        asia_kolkata_timezone = 'Asia/Kolkata'
+        asia_bangkok_timezone = 'Asia/Bangkok'
+        coefficient = 'coefficient'
+        cumulative_actual_current_mppt = 'cumulative_actual_current_mppt'
+        cumulative_predicted_current_mppt = 'cumulative_predicted_current_mppt'
+        day = "day"
+        time = "time"
+
+    import pandas as pd
+    from loguru import logger
+    import json
+
+    try:
+        with open(input_path, 'r') as f:
+            get_tags_component_output_dict = json.load(f)
+        print(get_tags_component_output_dict)
+        df_predicted_tags = get_tags_component_output_dict.get("df_predicted_tags")
+        df_efficiency_tags = get_tags_component_output_dict.get("df_efficiency_tags")
+        df_predicted_tags = pd.DataFrame(df_predicted_tags)
+        df_efficiency_tags = pd.DataFrame(df_efficiency_tags)
+        df_predicted_tags = df_predicted_tags.copy()
+        df_efficiency_tags_copy = df_efficiency_tags.copy()
+
+        df_predicted_tags.rename(columns={CommonConstants.tag_id: CommonConstants.predicted_current_mppt_tag},
+                                 inplace=True)
+
+        df_efficiency_tags_copy.rename(columns={CommonConstants.tag_id: CommonConstants.efficiency_mppt_tag},
+                                       inplace=True)
+
+        df_predicted_tags[CommonConstants.inv_id_mppt_id] = df_predicted_tags[CommonConstants.sub_id] + '_' + \
+                                                            df_predicted_tags[CommonConstants.inv_id] + '_' + \
+                                                            df_predicted_tags[CommonConstants.mppt_id]
+
+        df_efficiency_tags_copy[CommonConstants.inv_id_mppt_id] = df_efficiency_tags_copy[CommonConstants.sub_id] \
+                                                                  + '_' + \
+                                                                  df_efficiency_tags_copy[CommonConstants.inv_id] \
+                                                                  + '_' + \
+                                                                  df_efficiency_tags_copy[CommonConstants.mppt_id]
+
+        df_predicted_and_efficiency_tags = pd.merge(df_predicted_tags,
+                                                    df_efficiency_tags_copy[[CommonConstants.efficiency_mppt_tag,
+                                                                             CommonConstants.inv_id_mppt_id]],
+                                                    on=CommonConstants.inv_id_mppt_id,
+                                                    how='left')
+        df_predicted_and_efficiency_tags.reset_index(drop=True, inplace=True)
+        final_dict = {"df_predicted_and_efficiency_tags": df_predicted_and_efficiency_tags.to_dict(orient="records")}
+        with open(output_path, 'w') as f:
+            json.dump(final_dict, f)
+        print(final_dict)
+    except Exception as e:
+        logger.exception(f'Exception - {e}')

input_components/get_final_predicted_tags/variables.yml

+deployment:
+  pythonVersion: "3.9"
\ No newline at end of file

input_components/get_tags_component/README.md

@@ -16,7 +16,7 @@ pipeline_param |JsonObject |Required |None |inputValue | |
 MONGO_DB |String |Required |None |env | |
 MONGO_COLLECTION |String |Required |None |env | |
 CITY |String |Required |None |env | |
-Output |JsonObject |Required |None |outputPath | |
+output_text |String |Required |None |outputPath | |
 
 > Note 1 : Available Component types are: Input, Transform, Output.
 

input_components/get_tags_component/component.yml

@@ -2,7 +2,7 @@ name: Get tags function
 inputs:
 - {name: pipeline_param, type: JsonObject}
 outputs:
-- {name: Output, type: JsonObject}
+- {name: output_text, type: String}
 implementation:
   container:
     image: python:3.9
@@ -20,7 +20,12 @@ implementation:
       printf "%s" "$0" > "$program_path"
       python3 -u "$program_path" "$@"
     - |
-      def get_tags_function(pipeline_param):
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def get_tags_function(pipeline_param, output_text_path):
           import pandas as pd
           from loguru import logger
           import warnings
@@ -144,59 +149,29 @@ implementation:
                   df_coefficients = pd.DataFrame()
                   tracemalloc.clear_traces()
                   del mongo_conn
-                  final_dict = {"raw": df_raw_tags.to_dict('records'), "predicted": df_predicted_tags.to_dict('records'),
-                                "coefficients": df_coefficients.to_dict('records'),
-                                "efficiency": df_efficiency_tags.to_dict('records')}
+                  final_dict = {"df_raw_tags": df_raw_tags.to_dict('records'),
+                                "df_predicted_tags": df_predicted_tags.to_dict('records'),
+                                "df_coefficients": df_coefficients.to_dict('records'),
+                                "df_efficiency_tags": df_efficiency_tags.to_dict('records')}
                   print(final_dict)
-                  return final_dict
+                  with open(output_text_path, 'w') as f:
+                      json.dump(final_dict, f)
           except Exception as e:
               logger.exception(f'Exception - {e}')
 
-      def _serialize_json(obj) -> str:
-          if isinstance(obj, str):
-              return obj
-          import json
-
-          def default_serializer(obj):
-              if hasattr(obj, 'to_struct'):
-                  return obj.to_struct()
-              else:
-                  raise TypeError(
-                      "Object of type '%s' is not JSON serializable and does not have .to_struct() method."
-                      % obj.__class__.__name__)
-
-          return json.dumps(obj, default=default_serializer, sort_keys=True)
-
       import json
       import argparse
       _parser = argparse.ArgumentParser(prog='Get tags function', description='')
       _parser.add_argument("--pipeline-param", dest="pipeline_param", type=json.loads, required=True, default=argparse.SUPPRESS)
-      _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)
+      _parser.add_argument("--output-text", dest="output_text_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
       _parsed_args = vars(_parser.parse_args())
-      _output_files = _parsed_args.pop("_output_paths", [])
 
       _outputs = get_tags_function(**_parsed_args)
-
-      _outputs = [_outputs]
-
-      _output_serializers = [
-          _serialize_json,
-
-      ]
-
-      import os
-      for idx, output_file in enumerate(_output_files):
-          try:
-              os.makedirs(os.path.dirname(output_file))
-          except OSError:
-              pass
-          with open(output_file, 'w') as f:
-              f.write(_output_serializers[idx](_outputs[idx]))
     args:
     - --pipeline-param
     - {inputValue: pipeline_param}
-    - '----output-paths'
-    - {outputPath: Output}
+    - --output-text
+    - {outputPath: output_text}
     env:
       MONGO_DB: 'ilens_ai'
       MONGO_COLLECTION: 'bgrimmStringTags'

input_components/get_tags_component/src/program.py

-def get_tags_function(pipeline_param: dict) -> dict:
+import json
+
+from kfp.components import OutputPath
+
+
+def get_tags_function(pipeline_param: dict, output_text_path: OutputPath(str)):
     import pandas as pd
     from loguru import logger
     import warnings
@@ -122,10 +127,12 @@ def get_tags_function(pipeline_param: dict) -> dict:
             df_coefficients = pd.DataFrame()
             tracemalloc.clear_traces()
             del mongo_conn
-            final_dict = {"raw": df_raw_tags.to_dict('records'), "predicted": df_predicted_tags.to_dict('records'),
-                          "coefficients": df_coefficients.to_dict('records'),
-                          "efficiency": df_efficiency_tags.to_dict('records')}
+            final_dict = {"df_raw_tags": df_raw_tags.to_dict('records'),
+                          "df_predicted_tags": df_predicted_tags.to_dict('records'),
+                          "df_coefficients": df_coefficients.to_dict('records'),
+                          "df_efficiency_tags": df_efficiency_tags.to_dict('records')}
             print(final_dict)
-            return final_dict
+            with open(output_text_path, 'w') as f:
+                json.dump(final_dict, f)
     except Exception as e:
         logger.exception(f'Exception - {e}')

pipeline.py

@@ -17,38 +17,17 @@ def forecast_pipeline(pipeline_param: dict, plant_info: dict):
         # Loading the component from the above yaml file
         get_tags_function_component = kfp.components.load_component_from_file(
             "input_components/get_tags_component/component.yml")
-
+        get_final_predicted_tags = kfp.components.load_component_from_file(
+            "input_components/get_final_predicted_tags/component.yml")
 
         # Calling the component
         get_tags_function_task = get_tags_function_component(pipeline_param).set_memory_request('600M').set_memory_limit('1200M').\
             set_cpu_request('700m').set_cpu_limit('1400m')
+        get_final_predicted_tags_task = get_final_predicted_tags(get_tags_function_task.output)
 
-        # get_tags_function_task.add_volume(k8s_client.V1Volume(
-        #     name="get-data-volume",
-        #     secret=k8s_client.V1SecretVolumeSource(secret_name="MONGO_URI"))
-        # )
-        #
-        # get_tags_function_task.add_env_variable(
-        #     V1EnvVar(
-        #         name="MONGO_URI",
-        #         value_from=k8s_client.V1EnvVarSource(secret_key_ref=k8s_client.V1SecretKeySelector(
-        #             name="mongo-uri",
-        #             key="MONGO_URI"
-        #         )
-        #         )
-        #     )
-        # )
 
         # Disabling cacheing for all the components
         get_tags_function_task.execution_options.caching_strategy.max_cache_staleness = "P0D"
-        # read_from_kairos_task.execution_options.caching_strategy.max_cache_staleness = "P0D"
-        # data_preprocess_task.execution_options.caching_strategy.max_cache_staleness = "P0D"
-        # get_forecast_data_task.execution_options.caching_strategy.max_cache_staleness = "P0D"
-        # write_data_processor_task_1.execution_options.caching_strategy.max_cache_staleness = "P0D"
-        # get_data_recaster_task.execution_options.caching_strategy.max_cache_staleness = "P0D"
-        # write_data_processor_task_2.execution_options.caching_strategy.max_cache_staleness = "P0D"
-        # get_rolling_avg_task.execution_options.caching_strategy.max_cache_staleness = "P0D"
-        # write_data_processor_task_3.execution_options.caching_strategy.max_cache_staleness = "P0D"
 
     except Exception as e:
         logger.exception(f"Unable to Perform the execution {e}")