diff --git a/docker_scripts/parallelrunner.py b/docker_scripts/parallelrunner.py
index 26142f7..4963e19 100755
--- a/docker_scripts/parallelrunner.py
+++ b/docker_scripts/parallelrunner.py
@@ -7,6 +7,7 @@
 import multiprocessing
 import os
 import pathlib as pl
+import pprint
 import tempfile
 import time
 import traceback
@@ -161,35 +162,42 @@ def run_input_tasks(self, input_tasks, tasks_uuid):
 
         input_batches = self.batch_input_tasks(input_tasks, n_of_batches)
 
+        output_tasks = input_tasks.copy()
+        for output_task in output_tasks:
+            output_task["status"] = "SUBMITTED"
+        output_tasks_content = json.dumps(
+            {"uuid": tasks_uuid, "tasks": output_tasks}
+        )
+        self.output_tasks_path.write_text(output_tasks_content)
+
         output_batches = self.run_batches(
             tasks_uuid, input_batches, number_of_workers
         )
 
-        output_tasks = self.unbatch_output_tasks(output_batches)
+        for output_batch in output_batches:
+            output_batch_tasks = output_batch['tasks']
 
-        output_tasks_content = json.dumps(
-            {"uuid": tasks_uuid, "tasks": output_tasks}
-        )
-        self.output_tasks_path.write_text(output_tasks_content)
+            for output_task_i, output_task in output_batch_tasks:
+                output_tasks[output_task_i] = output_task
+                # logging.info(output_task["status"])
+
+            output_tasks_content = json.dumps(
+                {"uuid": tasks_uuid, "tasks": output_tasks}
+            )
+            self.output_tasks_path.write_text(output_tasks_content)
+            logger.info(f"Finished a batch of {len(output_batch_tasks)} tasks")
         logger.info(f"Finished a set of {len(output_tasks)} tasks")
         logger.debug(f"Finished a set of tasks: {output_tasks_content}")
 
     def batch_input_tasks(self, input_tasks, n_of_batches):
-        batches = [[] for _ in range(n_of_batches)]
+        batches = [{'batch_i': None,'tasks':[]} for _ in range(n_of_batches)]
 
         for task_i, input_task in enumerate(input_tasks):
             batch_id = task_i % n_of_batches
-            batches[batch_id].append(input_task)
+            batches[batch_id]['batch_i'] = batch_id
+            batches[batch_id]['tasks'].append((task_i, input_task))
         return batches
 
-    def unbatch_output_tasks(self, batches):
-        output_tasks = []
-        n_of_tasks = sum(len(batch) for batch in batches)
-
-        for task_i in range(n_of_tasks):
-            batch_id = task_i % len(batches)
-            output_tasks.append(batches[batch_id].pop(0))
-        return output_tasks
 
     def create_job_inputs(self, input):
         """Create job inputs"""
@@ -296,10 +304,12 @@ async def run_job(self, job_inputs, input_batch):
     def process_job_outputs(self, results, batch, status):
         if self.settings.template_id == "TEST_UUID":
             logger.info("Map in test mode, just returning input")
+            for task_i, task in batch["tasks"]:
+                task["status"] = "SUCCESS"
 
             return batch
 
-        for task_i, task in enumerate(batch):
+        for task_i, task in batch["tasks"]:
             output = task["output"]
             task["status"] = status
             for probe_name, probe_output in results.items():
@@ -358,7 +368,7 @@ def process_job_outputs(self, results, batch, status):
 
     def transform_batch_to_task_input(self, batch):
         task_input = {}
-        for task in batch:
+        for task_i, task in batch["tasks"]:
             input = task["input"]
             for param_name, param_input in input.items():
                 param_type = param_input["type"]
@@ -449,6 +459,10 @@ def run_batches(self, tasks_uuid, input_batches, number_of_workers):
         def map_func(batch_with_uuid, trial_number=1):
             return asyncio.run(async_map_func(batch_with_uuid, trial_number))
 
+        def set_batch_status(batch, message):
+            for task_i, task in batch["tasks"]:
+                task["status"] = "FAILURE"
+
         async def async_map_func(batch_with_uuid, trial_number=1):
             batch_uuid, batch = batch_with_uuid
             try:
@@ -489,27 +503,28 @@ async def async_map_func(batch_with_uuid, trial_number=1):
             except ParallelRunner.FatalException as error:
                 logger.info(
                     f"Batch {batch} failed with fatal error ({error}) in "
-                    f"trial {trial_number}, not retrying, raising error"
+                    f"trial {trial_number}, not retrying"
                 )
                 self.jobs_file_write_status_change(
                     id=batch_uuid,
                     status="failed",
                 )
-
-                raise error
+                set_batch_status(batch, "FAILURE")
+                # raise error
             except Exception as error:
                 if trial_number >= self.settings.max_job_trials:
                     logger.info(
                         f"Batch {batch} failed with error ("
                         f"{traceback.format_exc()}) in "
                         f"trial {trial_number}, reach max number of trials of "
-                        f"{self.settings.max_job_trials}, not retrying, raising error"
+                        f"{self.settings.max_job_trials}, not retrying"
                     )
                     self.jobs_file_write_status_change(
                         id=batch_uuid,
                         status="failed",
                     )
-                    raise error
+                    set_batch_status(batch, "FAILURE")
+                    # raise error
                 else:
                     logger.info(
                         f"Batch {batch} failed with error ("
@@ -540,7 +555,7 @@ async def async_map_func(batch_with_uuid, trial_number=1):
 
         with pathos.pools.ThreadPool(nodes=number_of_workers) as pool:
             pool.restart()
-            output_tasks = list(pool.map(map_func, input_batches_with_uuid))
+            output_tasks = pool.uimap(map_func, input_batches_with_uuid)
             pool.close()
             pool.join()
             pool.clear()  # Pool is singleton, need to clear old pool