diff --git a/docker/scripts/startup.sh b/docker/scripts/startup.sh
index 5c5a6aa49..527e8306a 100644
--- a/docker/scripts/startup.sh
+++ b/docker/scripts/startup.sh
@@ -132,6 +132,10 @@ while [ "$1" != "" ]; do
 	        shift
             TEST=$1
 	        ;;
+        --additional_requirements_path)
+            shift
+            ADDITIONAL_REQUIREMENTS_PATH=$1
+            ;;
         *) 
             usage 
             exit 1
@@ -140,6 +144,16 @@ while [ "$1" != "" ]; do
     shift 
 done
 
+
+# Optionally install addtional dependencies
+if [[ -n ${ADDITIONAL_REQUIREMENTS_PATH+x} ]]; then
+    echo "Installing addtional requirements..."
+    COMMAND="cd algorithmic-efficiency && pip install -r ${ADDITIONAL_REQUIREMENTS_PATH}"
+    echo $COMMAND
+    eval $COMMAND
+fi
+
+
 if [[ ${TEST} == "true" ]]; then
   cd algorithmic-efficiency
   COMMAND="python3 tests/test_traindiffs.py"
diff --git a/scoring/run_workloads.py b/scoring/run_workloads.py
index 1c33079d3..e474b6910 100644
--- a/scoring/run_workloads.py
+++ b/scoring/run_workloads.py
@@ -9,9 +9,11 @@
 --tuning_search_space <path_to_tuning_search_space_json> 
 """
 
+import datetime
 import json
 import os
 import struct
+import subprocess
 import time
 
 from absl import app
@@ -26,9 +28,11 @@
     'docker_image_url',
     'us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev',
     'URL to docker image')
-flags.DEFINE_integer('run_percentage',
-                     100,
-                     'Percentage of max num steps to run for.')
+flags.DEFINE_integer(
+    'run_percentage',
+    100,
+    'Percentage of max num steps to run for.'
+    'Must set the flag enable_step_budget to True for this to take effect.')
 flags.DEFINE_string('experiment_name',
                     'my_experiment',
                     'Name of top sub directory in experiment dir.')
@@ -83,10 +87,24 @@
     'If your algorithm has a smaller per step time than our baselines '
     'you may want to increase the number of steps per workload.')
 flags.DEFINE_string(
-    'workload',
+    'workloads',
     None,
+    'String representing a comma separated list of workload names.'
     'If not None, only run this workload, else run all workloads in workload_metadata_path.'
 )
+flags.DEFINE_string('additional_requirements_path',
+                    None,
+                    'Path to requirements.txt if any.')
+flags.DEFINE_integer(
+    'max_steps',
+    None,
+    'Maximum number of steps to run. Must set flag enable_step_budget.'
+    'This flag takes precedence over the run_percentage flag.')
+flags.DEFINE_bool(
+    'enable_step_budget',
+    False,
+    'Flag that has to be explicitly set to override time budgets to step budget percentage.'
+)
 
 FLAGS = flags.FLAGS
 
@@ -106,15 +124,40 @@ def container_running():
     return True
 
 
+def kill_containers():
+  docker_client = docker.from_env()
+  containers = docker_client.containers.list()
+  for container in containers:
+    container.kill()
+
+
+def gpu_is_active():
+  output = subprocess.check_output([
+      'nvidia-smi',
+      '--query-gpu=utilization.gpu',
+      '--format=csv,noheader,nounits'
+  ])
+  return any(int(x) > 0 for x in output.decode().splitlines())
+
+
 def wait_until_container_not_running(sleep_interval=5 * 60):
+  # check gpu util
+  # if the gpu has not been utilized for 30 minutes kill the
+  gpu_last_active = datetime.datetime.now().timestamp()
+
   while container_running():
+    # check if gpus have been inactive > 45 min and if so terminate container
+    if gpu_is_active():
+      gpu_last_active = datetime.datetime.now().timestamp()
+    if (datetime.datetime.now().timestamp() - gpu_last_active) > 45 * 60:
+      kill_containers(
+          "Killing container: GPUs have been inactive > 45 minutes...")
     time.sleep(sleep_interval)
   return
 
 
 def main(_):
   framework = FLAGS.framework
-  run_fraction = FLAGS.run_percentage / 100.
   experiment_name = FLAGS.experiment_name
   docker_image_url = FLAGS.docker_image_url
   submission_path = FLAGS.submission_path
@@ -132,7 +175,13 @@ def main(_):
     study_end_index = FLAGS.study_end_index
   else:
     study_end_index = num_studies - 1
+
+  additional_requirements_path_flag = ''
+  if FLAGS.additional_requirements_path:
+    additional_requirements_path_flag = f'--additional_requirements_path {FLAGS.additional_requirements_path} '
+
   submission_id = FLAGS.submission_id
+
   rng_seed = FLAGS.seed
 
   if not rng_seed:
@@ -144,17 +193,22 @@ def main(_):
   with open(FLAGS.workload_metadata_path) as f:
     workload_metadata = json.load(f)
 
+  # Get list of all possible workloads
   workloads = [w for w in workload_metadata.keys()]
 
-  # Read held-out workloads
+  # Read heldout workloads
   if FLAGS.held_out_workloads_config_path:
     held_out_workloads = read_held_out_workloads(
         FLAGS.held_out_workloads_config_path)
     workloads = workloads + held_out_workloads
 
-  # Filter for single workload
-  if FLAGS.workload and (FLAGS.workload in workloads):
-    workloads = [FLAGS.workload]
+  # Filter workloads if explicit workloads specified
+  if FLAGS.workloads is not None:
+    workloads = list(
+        filter(lambda x: x in FLAGS.workloads.split(','), workloads))
+    if len(workloads) != len(FLAGS.workloads.split(',')):
+      unmatched_workloads = set(FLAGS.workloads.split(',')) - set(workloads)
+      raise ValueError(f'Invalid workload name {unmatched_workloads}')
 
   rng_subkeys = prng.split(rng_key, num_studies)
 
@@ -174,14 +228,22 @@ def main(_):
           "sudo sh -c 'echo 3 > /proc/sys/vm/drop_caches'")  # clear caches
       print('=' * 100)
       dataset = workload_metadata[base_workload_name]['dataset']
-      max_steps = int(workload_metadata[base_workload_name]['max_steps'] *
-                      run_fraction)
+      max_steps_flag = ''
+      if FLAGS.enable_step_budget:
+        run_fraction = FLAGS.run_percentage / 100.
+        if FLAGS.max_steps is None:
+          max_steps = int(workload_metadata[base_workload_name]['max_steps'] *
+                          run_fraction)
+        else:
+          max_steps = FLAGS.max_steps
+        max_steps_flag = f'-m {max_steps}'
+
       mount_repo_flag = ''
       if FLAGS.local:
-        mount_repo_flag = '-v $HOME/algorithmic-efficiency:/algorithmic-efficiency '
-      command = ('docker run -t -d -v $HOME/data/:/data/ '
-                 '-v $HOME/experiment_runs/:/experiment_runs '
-                 '-v $HOME/experiment_runs/logs:/logs '
+        mount_repo_flag = '-v /home/kasimbeg/algorithmic-efficiency:/algorithmic-efficiency '
+      command = ('docker run -t -d -v /home/kasimbeg/data/:/data/ '
+                 '-v /home/kasimbeg/experiment_runs/:/experiment_runs '
+                 '-v /home/kasimbeg/experiment_runs/logs:/logs '
                  f'{mount_repo_flag}'
                  '--gpus all --ipc=host '
                  f'{docker_image_url} '
@@ -190,9 +252,10 @@ def main(_):
                  f'-s {submission_path} '
                  f'-w {workload} '
                  f'-e {study_dir} '
-                 f'-m {max_steps} '
+                 f'{max_steps_flag} '
                  f'--num_tuning_trials {num_tuning_trials} '
                  f'--rng_seed {run_seed} '
+                 f'{additional_requirements_path_flag}'
                  '-c false '
                  '-o true '
                  '-i true ')
@@ -235,4 +298,4 @@ def main(_):
 
 if __name__ == '__main__':
   flags.mark_flag_as_required('workload_metadata_path')
-  app.run(main)
\ No newline at end of file
+  app.run(main)
diff --git a/setup.cfg b/setup.cfg
index 321020ad9..eb570dafb 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -121,6 +121,8 @@ jax_core_deps =
   chex==0.1.7
   ml_dtypes==0.2.0
   protobuf==4.25.3
+  scipy==1.11.4
+
 
 # JAX CPU
 jax_cpu =
diff --git a/utils/run_workloads.py b/utils/run_workloads.py
deleted file mode 100644
index 39f6a7b6f..000000000
--- a/utils/run_workloads.py
+++ /dev/null
@@ -1,200 +0,0 @@
-"""
-Example Usage:
-python run_workloads.py \
---workload_config_path workload_config.json \
---framework jax \
---experiment_name my_first_experiment \
---docker_image_url us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev \
---run_percentage 10 \
---workload_config_path workload_config.json \
---dry_run 
-"""
-
-import json
-import os
-import struct
-import time
-
-from absl import app
-from absl import flags
-from absl import logging
-
-import docker
-
-flags.DEFINE_string(
-    'docker_image_url',
-    'us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev',
-    'URL to docker image')
-flags.DEFINE_integer('run_percentage',
-                     100,
-                     'Percentage of max num steps to run for.')
-flags.DEFINE_string('experiment_name',
-                    'my_experiment',
-                    'Name of top sub directory in experiment dir.')
-flags.DEFINE_boolean('rsync_data',
-                     True,
-                     'Whether or not to transfer the data from GCP w rsync.')
-flags.DEFINE_boolean('local', False, 'Mount local algorithmic-efficiency repo.')
-flags.DEFINE_string('framework', 'jax', 'Can be either PyTorch or JAX.')
-flags.DEFINE_boolean(
-    'dry_run',
-    False,
-    'Whether or not to actually run the docker containers. '
-    'If False, simply print the docker run commands. ')
-flags.DEFINE_integer('num_studies', 1, 'Number of studies to run')
-flags.DEFINE_integer('study_start_index', None, 'Start index for studies.')
-flags.DEFINE_integer('study_end_index', None, 'End index for studies.')
-flags.DEFINE_integer('num_tuning_trials', 1, 'Number of tuning trials.')
-flags.DEFINE_integer('hparam_start_index',
-                     None,
-                     'Start index for tuning trials.')
-flags.DEFINE_integer('hparam_end_index', None, 'End index for tuning trials.')
-flags.DEFINE_integer('seed', None, 'Random seed for evaluating a submission.')
-flags.DEFINE_integer('submission_id',
-                     0,
-                     'Submission ID to generate study and hparam seeds.')
-flags.DEFINE_string(
-    'workload_config_path',
-    'workload_confing.json',
-    'Path to config containing dataset and maximum number of steps per workload.'
-    'The default values of these are set to the full budgets as determined '
-    'via the target-setting procedure. '
-    'Note that training will be interrupted at either the set maximum number '
-    'of steps or the fixed workload maximum run time, whichever comes first. '
-    'If your algorithm has a smaller per step time than our baselines '
-    'you may want to increase the number of steps per workload.')
-
-FLAGS = flags.FLAGS
-
-
-def read_workloads(filename):
-  with open(filename, "r") as f:
-    held_out_workloads = json.load(f)
-  return held_out_workloads
-
-
-def container_running():
-  docker_client = docker.from_env()
-  containers = docker_client.containers.list()
-  if len(containers) == 0:
-    return False
-  else:
-    return True
-
-
-def wait_until_container_not_running(sleep_interval=5 * 60):
-  while container_running():
-    time.sleep(sleep_interval)
-  return
-
-
-def main(_):
-  # What Docker image to run the container with
-  docker_image_url = FLAGS.docker_image_url
-
-  # Framework
-  framework = FLAGS.framework
-
-  #
-  run_fraction = FLAGS.run_percentage / 100.
-  experiment_name = FLAGS.experiment_name
-
-  # Get study and trial interval arguments
-  num_studies = FLAGS.num_studies
-  study_start_index = FLAGS.study_start_index if FLAGS.study_start_index else 0
-  study_end_index = FLAGS.study_end_index if FLAGS.study_end_index else num_studies - 1
-
-  # Get trial arguments
-  num_tuning_trials = FLAGS.num_tuning_trials
-  hparam_start_index_flag = ''
-  hparam_end_index_flag = ''
-  if FLAGS.hparam_start_index:
-    hparam_start_index_flag = f'--hparam_start_index {FLAGS.hparam_start_index} '
-  if FLAGS.hparam_end_index:
-    hparam_end_index_flag = f'--hparam_end_index {FLAGS.hparam_end_index} '
-
-  # Generate rng keys from submission_id and seed
-  submission_id = FLAGS.submission_id
-  rng_seed = FLAGS.seed
-
-  if not rng_seed:
-    rng_seed = struct.unpack('I', os.urandom(4))[0]
-
-  logging.info('Using RNG seed %d', rng_seed)
-
-  # Read workload specifications to run
-  with open(FLAGS.workload_config_path) as f:
-    workload_config = json.load(f)
-  workloads = [w for w in workload_config.keys()]
-
-  for study_index in range(study_start_index, study_end_index + 1):
-    print('-' * 100)
-    print('*' * 40, f'Starting study {study_index + 1}/{num_studies}', '*' * 40)
-    print('-' * 100)
-    study_dir = os.path.join(experiment_name, f'study_{study_index}')
-
-    for workload in workloads:
-      # For each runnable workload check if there are any containers running
-      wait_until_container_not_running()
-
-      # Clear caches
-      os.system("sudo sh -c 'echo 3 > /proc/sys/vm/drop_caches'")
-      print('=' * 100)
-
-      # Get workload dataset, max step, algorithm path and tuning search space
-      dataset = workload_config[workload]['dataset']
-      max_steps = int(workload_config[workload]['max_steps'] * run_fraction)
-      submission_path = workload_config[workload]['submission_path']
-      tuning_search_space = workload_config[workload]['tuning_search_space']
-
-      # Optionally, define flag to mount local algorithmic-efficiency repo
-      mount_repo_flag = ''
-      if FLAGS.local:
-        mount_repo_flag = '-v $HOME/algorithmic-efficiency:/algorithmic-efficiency '
-
-      command = ('docker run -t -d -v $HOME/data/:/data/ '
-                 '-v $HOME/experiment_runs/:/experiment_runs '
-                 '-v $HOME/experiment_runs/logs:/logs '
-                 f'{mount_repo_flag}'
-                 '--gpus all --ipc=host '
-                 f'{docker_image_url} '
-                 f'-d {dataset} '
-                 f'-f {framework} '
-                 f'-s {submission_path} '
-                 f'-w {workload} '
-                 f'-t {tuning_search_space} '
-                 f'-e {study_dir} '
-                 f'-m {max_steps} '
-                 f'--num_tuning_trials {num_tuning_trials} '
-                 f'{hparam_start_index_flag} '
-                 f'{hparam_end_index_flag} '
-                 f'--rng_seed {rng_seed} '
-                 '-c false '
-                 '-o true '
-                 '-i true ')
-      if not FLAGS.dry_run:
-        print('Running docker container command')
-        print('Container ID: ')
-        return_code = os.system(command)
-      else:
-        return_code = 0
-      if return_code == 0:
-        print(
-            f'SUCCESS: container for {framework} {workload} launched successfully'
-        )
-        print(f'Command: {command}')
-        print(f'Results will be logged to {experiment_name}')
-      else:
-        print(
-            f'Failed: container for {framework} {workload} failed with exit code {return_code}.'
-        )
-        print(f'Command: {command}')
-      wait_until_container_not_running()
-      os.system(
-          "sudo sh -c 'echo 3 > /proc/sys/vm/drop_caches'")  # clear caches
-
-      print('=' * 100)
-
-
-if __name__ == '__main__':
-  app.run(main)
diff --git a/utils/target_setting_workload_config.json b/utils/target_setting_workload_config.json
deleted file mode 100644
index a8c050422..000000000
--- a/utils/target_setting_workload_config.json
+++ /dev/null
@@ -1,195 +0,0 @@
-{
-    "imagenet_resnet": {
-        "max_steps": 186666,
-        "dataset": "imagenet",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_adamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/imagenet_resnet/tuning_search_space.json"
-    },
-    "imagenet_resnet_gelu": {
-        "max_steps": 186666,
-        "dataset": "imagenet",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_momentum.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/imagenet_resnet_gelu/tuning_search_space.json"
-    },
-    "imagenet_resnet_large_bn_init": {
-        "max_steps": 186666,
-        "dataset": "imagenet",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_momentum.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/imagenet_resnet_large_bn_init/tuning_search_space.json"
-    },
-    "imagenet_resnet_silu": {
-        "max_steps": 186666,
-        "dataset": "imagenet",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/imagenet_resnet_silu/tuning_search_space.json"
-    },
-    "imagenet_vit": {
-        "max_steps": 186666,
-        "dataset": "imagenet",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_adamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/imagenet_vit/tuning_search_space.json"
-    },
-    "imagenet_vit_glu": {
-        "max_steps": 186666,
-        "dataset": "imagenet",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/imagenet_vit_glu/tuning_search_space.json"
-    },
-    "imagenet_vit_map": {
-        "max_steps": 186666,
-        "dataset": "imagenet",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/imagenet_vit_map/tuning_search_space.json"
-    },
-    "imagenet_vit_post_ln": {
-        "max_steps": 186666,
-        "dataset": "imagenet",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/imagenet_vit_post_ln/tuning_search_space.json"
-    },
-    "fastmri": {
-        "max_steps": 36189,
-        "dataset": "fastmri",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nesterov.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/fastmri/tuning_search_space.json"
-    },
-    "fastmri_layernorm": {
-        "max_steps": 36189,
-        "dataset": "fastmri",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/fastmri_layernorm/tuning_search_space.json"
-    },
-    "fastmri_model_size": {
-        "max_steps": 36189,
-        "dataset": "fastmri",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/fastmri_model_size/tuning_search_space.json"
-    },
-    "fastmri_tanh": {
-        "max_steps": 36189,
-        "dataset": "fastmri",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/fastmri_tanh/tuning_search_space.json"
-    },
-    "ogbg": {
-        "max_steps": 80000,
-        "dataset": "ogbg",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nesterov.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/ogbg/tuning_search_space.json"
-    },
-    "ogbg_gelu": {
-        "max_steps": 80000,
-        "dataset": "ogbg",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/ogbg_gelu/tuning_search_space.json"
-    },
-    "ogbg_model_size": {
-        "max_steps": 80000,
-        "dataset": "ogbg",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/ogbg_model_size/tuning_search_space.json"
-    },
-    "ogbg_silu": {
-        "max_steps": 80000,
-        "dataset": "ogbg",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/ogbg_silu/tuning_search_space.json"
-    },
-    "wmt": {
-        "max_steps": 133333,
-        "dataset": "wmt",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/wmt/tuning_search_space.json"
-    },
-    "wmt_attention_temp": {
-        "max_steps": 133333,
-        "dataset": "wmt",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/wmt_attention_temp/tuning_search_space.json"
-    },
-    "wmt_glu_tanh": {
-        "max_steps": 133333,
-        "dataset": "wmt",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/wmt_glu_tanh/tuning_search_space.json"
-    },
-    "wmt_post_ln": {
-        "max_steps": 133333,
-        "dataset": "wmt",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_adamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/wmt_post_ln/tuning_search_space.json"
-    },
-    "librispeech_deepspeech": {
-        "max_steps": 48000,
-        "dataset": "librispeech",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/librispeech_deepspeech/tuning_search_space.json"
-    },
-    "librispeech_deepspeech_no_resnet": {
-        "max_steps": 48000,
-        "dataset": "librispeech",
-        "submission_path": "reference_algorithms/target_setting_algorithms/jax_nadamw.py",
-        "tuning_search_space": "reference_algorithms/target_setting_algorithms/librispeech_deepspeech_no_resnet/tuning_search_space.json"
-    },
-    "librispeech_deepspeech_norm_and_spec_aug": {
-        "max_steps": 48000,
-        "dataset": "librispeech",
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\ No newline at end of file