diff --git a/compressions/2/pvanalytics-submission/requirements.txt b/compressions/2/pvanalytics-submission/requirements.txt
deleted file mode 100644
index bd124a40..00000000
--- a/compressions/2/pvanalytics-submission/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-pandas==2.0.3
-pvanalytics==0.2.0
-pvlib==0.10.2
-matplotlib==3.7.2
-ruptures==1.1.8
\ No newline at end of file
diff --git a/compressions/2/pvanalytics-submission.zip b/compressions/2/pvanalytics_submission.zip
similarity index 75%
rename from compressions/2/pvanalytics-submission.zip
rename to compressions/2/pvanalytics_submission.zip
index 57501d3f..535b1744 100644
Binary files a/compressions/2/pvanalytics-submission.zip and b/compressions/2/pvanalytics_submission.zip differ
diff --git a/compressions/2/pvanalytics_submission/requirements.txt b/compressions/2/pvanalytics_submission/requirements.txt
new file mode 100644
index 00000000..519c3aeb
--- /dev/null
+++ b/compressions/2/pvanalytics_submission/requirements.txt
@@ -0,0 +1,6 @@
+matplotlib==3.9.1
+numpy==1.26.4
+pandas==2.2.2
+pvanalytics==0.2.0
+pvlib==0.10.5
+ruptures==1.1.8
\ No newline at end of file
diff --git a/compressions/2/pvanalytics-submission/pvanalytics-cpd-module.py b/compressions/2/pvanalytics_submission/submission_wrapper.py
similarity index 50%
rename from compressions/2/pvanalytics-submission/pvanalytics-cpd-module.py
rename to compressions/2/pvanalytics_submission/submission_wrapper.py
index f31ac56f..1652c28d 100644
--- a/compressions/2/pvanalytics-submission/pvanalytics-cpd-module.py
+++ b/compressions/2/pvanalytics_submission/submission_wrapper.py
@@ -1,151 +1,128 @@
-"""
-PVAnalytics-CPD based module. This module will be uploaded by the user
-and tested using the data sets accordingly. 
-"""
-
-import pandas as pd
-from pvanalytics.quality.time import shifts_ruptures
-from pvanalytics.features import daytime
-from pvanalytics.quality.outliers import zscore
-from pvanalytics.quality import gaps
-import pvlib
-import matplotlib.pyplot as plt
-import pvanalytics
-import ruptures
-
-
-def detect_time_shifts(
-    time_series, latitude, longitude, data_sampling_frequency
-):
-    """
-    Master function for testing for time shifts in a series and returning
-    time-shifted periods
-
-    """
-    # Save the dates of the time series index for reindexing at the end
-    date_index = pd.Series(time_series.index.date).drop_duplicates()
-    # Data pre-processing:
-    # 1) Removal of frozen/stuck data
-    # 2) Removal of data periods with low data 'completeness'
-    # 3) Removal of negative data
-    # 4) Removal of outliers via Hampel + outlier filter
-    # Trim based on frozen data values
-    # REMOVE STALE DATA (that isn't during nighttime periods)
-    # Day/night mask
-    daytime_mask = daytime.power_or_irradiance(time_series)
-    # Stale data mask
-    stale_data_mask = gaps.stale_values_round(
-        time_series, window=3, decimals=2
-    )
-    stale_data_mask = stale_data_mask & daytime_mask
-
-    # REMOVE NEGATIVE DATA
-    negative_mask = time_series < 0
-
-    # FIND ABNORMAL PERIODS
-    daily_min = time_series.resample("D").min()
-    series_min = 0.1 * time_series.mean()
-    erroneous_mask = daily_min >= series_min
-    erroneous_mask = erroneous_mask.reindex(
-        index=time_series.index, method="ffill", fill_value=False
-    )
-    # FIND OUTLIERS (Z-SCORE FILTER)
-    zscore_outlier_mask = zscore(time_series, zmax=3.5, nan_policy="omit")
-
-    # Filter the time series, taking out all of the issues
-    issue_mask = (
-        (~stale_data_mask)
-        & (~negative_mask)
-        & (~erroneous_mask)
-        & (~zscore_outlier_mask)
-    )
-
-    time_series = time_series[issue_mask]
-    time_series = time_series.asfreq(str(data_sampling_frequency) + "T")
-    # Data completeness
-    # Trim the series based on daily completeness score
-    trim_series_mask = pvanalytics.quality.gaps.trim_incomplete(
-        time_series,
-        minimum_completeness=0.25,
-        freq=str(data_sampling_frequency) + "T",
-    )
-
-    time_series = time_series[trim_series_mask]
-    if len(time_series) > 0:
-        # Calculate a nighttime offset
-        # Mask daytime periods for the time series
-        daytime_mask = daytime.power_or_irradiance(
-            time_series,
-            freq=str(data_sampling_frequency) + "T",
-            low_value_threshold=0.005,
-        )
-        # Get the modeled sunrise and sunset time series based on the system's
-        # latitude-longitude coordinates
-        modeled_sunrise_sunset_df = (
-            pvlib.solarposition.sun_rise_set_transit_spa(
-                time_series.index, latitude, longitude
-            )
-        )
-
-        # Calculate the midday point between sunrise and sunset for each day
-        # in the modeled irradiance series
-        modeled_midday_series = (
-            modeled_sunrise_sunset_df["sunrise"]
-            + (
-                modeled_sunrise_sunset_df["sunset"]
-                - modeled_sunrise_sunset_df["sunrise"]
-            )
-            / 2
-        )
-
-        # Generate the sunrise, sunset, and halfway pts for the data stream
-        sunrise_series = daytime.get_sunrise(daytime_mask)
-        sunset_series = daytime.get_sunset(daytime_mask)
-        midday_series = sunrise_series + ((sunset_series - sunrise_series) / 2)
-        # Convert the midday and modeled midday series to daily values
-        midday_series_daily, modeled_midday_series_daily = (
-            midday_series.resample("D").mean(),
-            modeled_midday_series.resample("D").mean(),
-        )
-
-        # Set midday value series as minutes since midnight, from midday datetime
-        # values
-        midday_series_daily = (
-            midday_series_daily.dt.hour * 60
-            + midday_series_daily.dt.minute
-            + midday_series_daily.dt.second / 60
-        )
-        modeled_midday_series_daily = (
-            modeled_midday_series_daily.dt.hour * 60
-            + modeled_midday_series_daily.dt.minute
-            + modeled_midday_series_daily.dt.second / 60
-        )
-
-        # Estimate the time shifts by comparing the modelled midday point to the
-        # measured midday point.
-        is_shifted, time_shift_series = shifts_ruptures(
-            midday_series_daily,
-            modeled_midday_series_daily,
-            period_min=15,
-            shift_min=15,
-            zscore_cutoff=0.75,
-        )
-        time_shift_series = -1 * time_shift_series
-
-        # Create a midday difference series between modeled and measured midday, to
-        # visualize time shifts. First, resample each time series to daily frequency,
-        # and compare the data stream's daily halfway point to the modeled halfway
-        # point
-        midday_diff_series = (
-            modeled_midday_series.resample("D").mean()
-            - midday_series.resample("D").mean()
-        ).dt.total_seconds() / 60
-
-        midday_diff_series.plot()
-        time_shift_series.plot()
-        plt.show()
-        plt.close()
-        time_shift_series.index = time_shift_series.index.date
-        return time_shift_series
-    else:
-        return pd.Series(0, index=date_index)
+"""
+PVAnalytics-CPD based module. This module will be uploaded by the user
+and tested using the data sets accordingly. 
+"""
+
+import pandas as pd
+from pvanalytics.quality.time import shifts_ruptures
+from pvanalytics.features import daytime
+from pvanalytics.quality.outliers import zscore
+from pvanalytics.quality import gaps
+import pvlib
+import matplotlib.pyplot as plt
+import pvanalytics
+import ruptures
+    
+def detect_time_shifts(time_series,
+                       latitude, longitude,
+                       data_sampling_frequency):
+    """
+    Master function for testing for time shifts in a series and returning
+    time-shifted periods
+    
+    """
+    # Save the dates of the time series index for reindexing at the end
+    date_index = pd.Series(time_series.index.date).drop_duplicates()
+    # Data pre-processing:
+    # 1) Removal of frozen/stuck data
+    # 2) Removal of data periods with low data 'completeness'
+    # 3) Removal of negative data
+    # 4) Removal of outliers via Hampel + outlier filter
+    # Trim based on frozen data values
+    # REMOVE STALE DATA (that isn't during nighttime periods)
+    # Day/night mask
+    daytime_mask = daytime.power_or_irradiance(time_series)
+    # Stale data mask
+    stale_data_mask = gaps.stale_values_round(time_series,
+                                              window=3,
+                                              decimals=2)
+    stale_data_mask = stale_data_mask & daytime_mask
+    
+    # REMOVE NEGATIVE DATA
+    negative_mask = (time_series < 0)
+    
+    # FIND ABNORMAL PERIODS
+    daily_min = time_series.resample('D').min()
+    series_min = 0.1 * time_series.mean()
+    erroneous_mask = (daily_min >= series_min)
+    erroneous_mask = erroneous_mask.reindex(index=time_series.index,
+                                            method='ffill',
+                                            fill_value=False)
+    # FIND OUTLIERS (Z-SCORE FILTER)
+    zscore_outlier_mask = zscore(time_series, zmax=3.5,
+                                 nan_policy='omit')
+    
+    # Filter the time series, taking out all of the issues
+    issue_mask = ((~stale_data_mask) & (~negative_mask) &
+                  (~erroneous_mask) & (~zscore_outlier_mask))
+    
+    time_series = time_series[issue_mask]
+    time_series = time_series.asfreq(str(data_sampling_frequency) + 'T')
+    # Data completeness
+    # Trim the series based on daily completeness score
+    trim_series_mask = pvanalytics.quality.gaps.trim_incomplete(time_series,
+                                                                minimum_completeness=.25,
+                                                                freq=str(data_sampling_frequency) + 'T')
+
+    time_series = time_series[trim_series_mask]
+    if len(time_series) > 0:
+        # Calculate a nighttime offset 
+        # Mask daytime periods for the time series
+        daytime_mask = daytime.power_or_irradiance(time_series,
+                                                   freq=str(data_sampling_frequency) + 'T',
+                                                   low_value_threshold=.005)
+        # Get the modeled sunrise and sunset time series based on the system's
+        # latitude-longitude coordinates
+        modeled_sunrise_sunset_df = pvlib.solarposition.sun_rise_set_transit_spa(
+            time_series.index, latitude, longitude)
+        
+        # Calculate the midday point between sunrise and sunset for each day
+        # in the modeled irradiance series
+        modeled_midday_series = modeled_sunrise_sunset_df['sunrise'] + \
+            (modeled_sunrise_sunset_df['sunset'] -
+             modeled_sunrise_sunset_df['sunrise']) / 2
+
+        #Generate the sunrise, sunset, and halfway pts for the data stream
+        sunrise_series = daytime.get_sunrise(daytime_mask)
+        sunset_series = daytime.get_sunset(daytime_mask)
+        midday_series = sunrise_series + ((sunset_series - sunrise_series)/2)
+        # Convert the midday and modeled midday series to daily values
+        midday_series_daily, modeled_midday_series_daily = (
+            midday_series.resample('D').mean(),
+            modeled_midday_series.resample('D').mean())
+        
+        # Set midday value series as minutes since midnight, from midday datetime
+        # values
+        midday_series_daily = (midday_series_daily.dt.hour * 60 +
+                               midday_series_daily.dt.minute +
+                               midday_series_daily.dt.second / 60)
+        modeled_midday_series_daily = \
+            (modeled_midday_series_daily.dt.hour * 60 +
+             modeled_midday_series_daily.dt.minute +
+             modeled_midday_series_daily.dt.second / 60)
+        
+        # Estimate the time shifts by comparing the modelled midday point to the
+        # measured midday point.
+        is_shifted, time_shift_series = shifts_ruptures(midday_series_daily,
+                                                        modeled_midday_series_daily,
+                                                        period_min=15,
+                                                        shift_min=15,
+                                                        zscore_cutoff=.75)
+        time_shift_series = -1 * time_shift_series
+        
+        # Create a midday difference series between modeled and measured midday, to
+        # visualize time shifts. First, resample each time series to daily frequency,
+        # and compare the data stream's daily halfway point to the modeled halfway
+        # point
+        midday_diff_series = (modeled_midday_series.resample('D').mean() -
+                              midday_series.resample('D').mean()
+                              ).dt.total_seconds() / 60
+        
+        midday_diff_series.plot()
+        time_shift_series.plot()
+        plt.show()
+        plt.close()
+        time_shift_series.index = time_shift_series.index.date
+        return time_shift_series
+    else:
+        return pd.Series(0, index=date_index)
\ No newline at end of file
diff --git a/valhub/Dockerfile b/valhub/Dockerfile
index 4abf6638..27724bc5 100644
--- a/valhub/Dockerfile
+++ b/valhub/Dockerfile
@@ -1,4 +1,4 @@
-FROM python:3.11-slim as base
+FROM python:3.11-slim
 
 # docker build --progress=plain -t "hmm:Dockerfile" -f valhub/Dockerfile .
 WORKDIR /root
@@ -13,16 +13,7 @@ COPY . .
 
 
 # Set Django admin username, password, and other environment variables
-ARG admin_username
-ARG admin_password
-ARG admin_email
-ARG djangosk
-
-ENV DJANGO_SUPERUSER_USERNAME=$admin_username
-ENV DJANGO_SUPERUSER_PASSWORD=$admin_password
-ENV DJANGO_SUPERUSER_EMAIL=$admin_email
-ENV DJANGO_SECRET_KEY=${djangosk}
-ENV PORT 8005
+ENV PORT=8005
 
 RUN apt-get update -qq
 RUN apt-get install -y libpq-dev python3-psycopg2
@@ -34,6 +25,6 @@ WORKDIR /root/valhub
 RUN python3 -m pip install -r requirements.txt
 
 # RUN cd ./valhub
-RUN python3 manage.py makemigrations
-RUN python3 manage.py collectstatic --noinput
+# RUN python3 manage.py makemigrations
+# RUN python3 manage.py collectstatic --noinput
 CMD ["/usr/bin/supervisord", "-c", "/root/valhub/supervisord.conf"]
diff --git a/valhub/supervisord.conf b/valhub/supervisord.conf
index 078fbfcc..29ded7d1 100644
--- a/valhub/supervisord.conf
+++ b/valhub/supervisord.conf
@@ -1,12 +1,26 @@
 [supervisord]
 nodaemon = true
 
+[program:valhub_makemigrations]
+directory=/root/valhub
+command=python3 /root/valhub/manage.py makemigrations
+autostart=true
+autorestart=false
+
+[program:valhub_collectstatic]
+directory=/root/valhub
+command=python3 /root/valhub/manage.py collectstatic --noinput
+autostart=true
+autorestart=false
+depends_on=valhub_makemigrations
+
 [program:valhub_webserver]
 directory=/root/valhub
 command=python3 /root/valhub/manage.py runserver 0.0.0.0:8005
 stdout_logfile=/dev/fd/1
 stdout_logfile_maxbytes=0
 redirect_stderr=true
+depends_on=valhub_collectstatic
 autostart=true
 
 [program:valhub_migrate]
diff --git a/workers/src/docker/Dockerfile b/workers/src/docker/Dockerfile
index a2ea8946..450690fb 100644
--- a/workers/src/docker/Dockerfile
+++ b/workers/src/docker/Dockerfile
@@ -12,9 +12,10 @@ RUN apt-get update && apt-get install -y \
     libhdf5-dev \
     python3-dev \
     cmake \
-    pkg-config
+    pkg-config \
+    unzip
 
-COPY unzip.py .
+# COPY unzip.py .
 COPY requirements.txt .
 
 # Install the Python dependencies for the submission wrapper
@@ -27,12 +28,14 @@ COPY $zip_file .
 
 # Unzip the submission package
 
-RUN python -m unzip $zip_file submission
+RUN unzip -j $zip_file -d submission
 
 WORKDIR /app/submission
 
 # Install the Python dependencies
+RUN pip install --upgrade pip
 RUN pip install -r requirements.txt
+RUN pip install typing-extensions
 
 
 # Set the working directory in the container
diff --git a/workers/src/docker/submission/requirements.txt b/workers/src/docker/submission/requirements.txt
deleted file mode 100644
index ede8c739..00000000
--- a/workers/src/docker/submission/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-numpy
-solar-data-tools
diff --git a/workers/src/docker/submission/submission_wrapper.py b/workers/src/docker/submission/submission_wrapper.py
deleted file mode 100644
index 78112f51..00000000
--- a/workers/src/docker/submission/submission_wrapper.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import numpy as np
-from solardatatools import DataHandler
-
-
-def detect_time_shifts(
-    time_series, latitude=None, longitude=None, data_sampling_frequency=None
-):
-    dh = DataHandler(time_series.to_frame())
-    dh.run_pipeline(fix_shifts=True, verbose=False, round_shifts_to_hour=False)
-    return dh.time_shift_analysis.correction_estimate
diff --git a/workers/src/docker/submission_wrapper.py b/workers/src/docker/submission_wrapper.py
index 3a6ce613..cd37f237 100644
--- a/workers/src/docker/submission_wrapper.py
+++ b/workers/src/docker/submission_wrapper.py
@@ -1,23 +1,27 @@
 from importlib import import_module
 import inspect
 import pathlib
-from re import sub
 import sys
+import json
 import pandas as pd
 import numpy as np
 from time import perf_counter
-from functools import wraps
-from typing import Any, ParamSpec, Union, Tuple, TypeVar, Callable, cast
+from typing import Any, Union, Tuple, TypeVar, Callable, cast, Optional
 from logging import Logger
 import logging
 
+if sys.version_info < (3, 10):
+    from typing_extensions import ParamSpec
+else:
+    from typing import ParamSpec
+
 T = TypeVar("T")
 
 P = ParamSpec("P")
 
 
 def logger_if_able(
-    message: object, logger: Logger | None = None, level: str = "INFO"
+    message: object, logger: Optional[Logger] = None, level: str = "INFO"
 ):
     if logger is not None:
         levels_dict = {
@@ -116,7 +120,7 @@ def import_submission_function(submission_file_name: str, function_name: str):
 
     try:
         submission_function: Callable[
-            [pd.Series, Any], np.ndarray | tuple[float, float]
+            [pd.Series, Any], Union[np.ndarray, tuple[float, float]]
         ] = getattr(submission_module, function_name)
         function_parameters = list(
             inspect.signature(submission_function).parameters.keys()
@@ -141,6 +145,8 @@ def main():
     function_name = args[1]
     data_file_name = args[2]
 
+    print(args)
+
     print("Getting submission function...")
 
     try:
@@ -170,17 +176,30 @@ def main():
 
     print(f"Execution time: {execution_time}")
 
-    # save results to csv file
+    # Save information about the submission function for later use with worker
+    submission_info_file = pathlib.Path(
+        f"{results_dir}/submission_function_info.json"
+    )
+    if not submission_info_file.exists():
+        submission_function_info = {
+            "data_file_name": data_file_name,
+            "function_name": function_name,
+            "function_parameters": function_parameters,
+        }
+
+        with open(f"{results_dir}/submission_function_info.json", "w") as fp:
+            json.dump(submission_function_info, fp)
 
-    print(f"Saving results to {results_dir}/{data_file_name}")
+    # save results to csv file
+    results_file_path = f"{results_dir}/files/{data_file_name}"
+    print(f"Saving results to {results_file_path}")
     if isinstance(results, tuple):
 
         results_df = pd.DataFrame([results])
     else:
         results_df = pd.DataFrame(results)
     print(f"Results: {results_df}")
-    results_file = f"{results_dir}/{data_file_name}"
-    results_df.to_csv(results_file, header=True)
+    results_df.to_csv(results_file_path, header=True)
 
     columns = ["file_name", "execution_time"]
 
diff --git a/workers/src/logging_config.json b/workers/src/logging_config.json
index c4949fae..93bbcfbd 100644
--- a/workers/src/logging_config.json
+++ b/workers/src/logging_config.json
@@ -33,13 +33,13 @@
         },
         "file": {
             "class": "logging.handlers.RotatingFileHandler",
-            "level": "DEBUG",
+            "level": "INFO",
             "formatter": "detailed",
             "filename": "logs/submission.log"
         },
         "json_file": {
             "class": "logging.handlers.RotatingFileHandler",
-            "level": "DEBUG",
+            "level": "INFO",
             "formatter": "json",
             "filename": "logs/submission.log.jsonl"
         },
diff --git a/workers/src/pvinsight-validation-runner.py b/workers/src/pvinsight-validation-runner.py
index 91837a0e..0b1114a7 100644
--- a/workers/src/pvinsight-validation-runner.py
+++ b/workers/src/pvinsight-validation-runner.py
@@ -16,7 +16,16 @@
       This section will be dependent on the type of analysis being run.
 """
 
-from typing import Any, Callable, Sequence, Tuple, TypeVar, cast, ParamSpec
+from typing import (
+    Any,
+    Callable,
+    Sequence,
+    Tuple,
+    TypeVar,
+    TypedDict,
+    cast,
+    ParamSpec,
+)
 import pandas as pd
 import os
 from importlib import import_module
@@ -345,6 +354,7 @@ def run(  # noqa: C901
 
     # Ensure results directory exists
     os.makedirs(results_dir, exist_ok=True)
+    os.makedirs(os.path.join(results_dir, "files"), exist_ok=True)
 
     # Ensure results directory exists
     os.makedirs(data_dir, exist_ok=True)
@@ -559,6 +569,28 @@ def run(  # noqa: C901
 
     # raise Exception("Finished Successfully")
 
+    # Get information about the submission function
+
+    class SubmissionFunctionInfo(TypedDict):
+        data_file_name: str
+        function_name: str
+        function_parameters: list[str]
+
+    try:
+        with open(f"{results_dir}/submission_function_info.json", "r") as fp:
+            submission_function_info: SubmissionFunctionInfo = json.load(fp)
+    except FileNotFoundError as e:
+        logger.error("submission_function_info.json not found")
+        logger.exception(e)
+
+        # logger.info(f"update submission status to {FAILED}")
+        # update_submission_status(submission_id, FAILED)
+        # TODO: add error code
+        error_code = 500
+        raise RunnerException(
+            *get_error_by_code(error_code, runner_error_codes, logger)
+        )
+
     # Convert the results to a pandas dataframe and perform all of the
     # post-processing in the script
     results_df = pd.DataFrame(results_list)
@@ -576,9 +608,11 @@ def run(  # noqa: C901
     # Get the mean and median run times
     public_metrics_dict["mean_runtime"] = results_df["runtime"].mean()
     public_metrics_dict["median_runtime"] = results_df["runtime"].median()
-    public_metrics_dict["function_parameters"] = [
-        "time_series",
-        *config_data["allowable_kwargs"],
+    logger.info(
+        f'function_parameters: {submission_function_info["function_parameters"]}'
+    )
+    public_metrics_dict["function_parameters"] = submission_function_info[
+        "function_parameters"
     ]
     public_metrics_dict["data_requirements"] = results_df[
         "data_requirements"
@@ -721,15 +755,15 @@ def m_median(df: pd.DataFrame, column: str):
 
     logger.info(f"number_of_errors: {number_of_errors}")
 
-    success_rate = (
-        (total_number_of_files - number_of_errors) / total_number_of_files
-    ) * 100
-    logger.info(f"success_rate: {success_rate}%")
-    logger.info(
-        f"{total_number_of_files - number_of_errors} out of {total_number_of_files} files processed successfully"
-    )
+    # success_rate = (
+    #     (total_number_of_files - number_of_errors) / total_number_of_files
+    # ) * 100
+    # logger.info(f"success_rate: {success_rate}%")
+    # logger.info(
+    #     f"{total_number_of_files - number_of_errors} out of {total_number_of_files} files processed successfully"
+    # )
 
-    public_metrics_dict["success_rate"] = success_rate
+    # public_metrics_dict["success_rate"] = success_rate
     return public_metrics_dict
 
 
@@ -988,8 +1022,8 @@ def loop_over_files_and_generate_results(
         logger=logger,
     )
 
-    errors = [error for error, error_code in test_errors]
-    number_of_errors += sum(errors)
+    is_errors_list = [error for error, error_code in test_errors]
+    number_of_errors += sum(is_errors_list)
 
     if number_of_errors == NUM_FILES_TO_TEST:
         logger.error(
@@ -1029,8 +1063,9 @@ def loop_over_files_and_generate_results(
         raise RunnerException(
             *get_error_by_code(500, runner_error_codes, logger)
         )
-    errors = [error for error, error_code in rest_errors]
-    number_of_errors += sum(errors)
+    is_errors_list = [error for error, error_code in rest_errors]
+
+    number_of_errors += sum(is_errors_list)
 
     # test_errors = [result for result, _ in test_errors if result is not None]
     # rest_errors = [result for result, _ in rest_errors if result is not None]
@@ -1049,6 +1084,8 @@ def loop_over_results_and_generate_metrics(
     all_results: list[dict[str, Any]] = []
     number_of_errors = 0
 
+    result_files_dir = os.path.join(results_dir, "files")
+
     file_metadata_df: pd.DataFrame = pd.read_csv(
         os.path.join(data_dir, "metadata", "file_metadata.csv")
     )
@@ -1096,7 +1133,7 @@ def loop_over_results_and_generate_metrics(
                 file_name,
                 config_data,
                 system_metadata_dict,
-                results_dir,
+                result_files_dir,
                 data_dir,
                 submission_runtime,
                 function_parameters,
diff --git a/workers/src/utility.py b/workers/src/utility.py
index 6e0be477..2fe366cd 100644
--- a/workers/src/utility.py
+++ b/workers/src/utility.py
@@ -998,6 +998,27 @@ def submission_task(
             error_code = 500
             logger_if_able(f"Error: {e}", None, "ERROR")
 
+    # Finished a file for submission
+    # TODO: Add timestamp to API
+    # +1 file completed to API
+    # json = {
+    #     "timestamp": datetime.now().isoformat(),
+    #     "file": submission_file_name,
+    # }
+
+    # TODO: Send to API
+    # Create an error report for all non breaking errors and send to API
+
+    file_error_report = {
+        "error_code": error_code,
+        "error_type": SubmissionException,
+        "error_message": error,
+        "file_name": submission_file_name,
+    }
+
+    # # Send error report to API
+    # send_error_report_to_API(json_errors, error_rate)
+
     return error, error_code
 
 
@@ -1058,6 +1079,7 @@ def create_docker_image(
                 path=dir_path,
                 tag=tag,
                 rm=True,
+                nocache=True,
                 dockerfile="Dockerfile",
                 buildargs={
                     "zip_file": f"{submission_file_name}",
@@ -1108,8 +1130,8 @@ def __exit__(self, exc_type, exc_value, exc_traceback):
 def initialize_docker_client():
     base_url = os.environ.get("DOCKER_HOST", None)
 
-    if not base_url:
-        logger_if_able("Docker host not set", None, "WARNING")
+    # if not base_url:
+    #     logger_if_able("Docker host not set", None, "WARNING")
 
     # cert_path = os.environ.get("DOCKER_CERT_PATH")
     # if not cert_path: