diff --git a/src/fairchem/core/_cli_hydra.py b/src/fairchem/core/_cli_hydra.py
index 79992616d..6ca8b7d6f 100644
--- a/src/fairchem/core/_cli_hydra.py
+++ b/src/fairchem/core/_cli_hydra.py
@@ -18,6 +18,9 @@
 
     from omegaconf import DictConfig
 
+    from fairchem.core.components.runner import Runner
+
+
 from submitit import AutoExecutor
 from submitit.helpers import Checkpointable, DelayedSubmission
 from torch.distributed.launcher.api import LaunchConfig, elastic_launch
@@ -25,7 +28,6 @@
 from fairchem.core.common import distutils
 from fairchem.core.common.flags import flags
 from fairchem.core.common.utils import get_timestamp_uid, setup_env_vars, setup_imports
-from fairchem.core.components.runner import Runner
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
@@ -40,18 +42,18 @@ def __call__(self, dict_config: DictConfig, cli_args: argparse.Namespace) -> Non
         setup_env_vars()
         try:
             distutils.setup(map_cli_args_to_dist_config(cli_args))
-            runner: Runner = hydra.utils.instantiate(dict_config.runner)
-            runner.load_state()
-            runner.run()
+            self.runner: Runner = hydra.utils.instantiate(dict_config.runner)
+            self.runner.load_state()
+            self.runner.run()
         finally:
             distutils.cleanup()
 
     def checkpoint(self, *args, **kwargs):
         logging.info("Submitit checkpointing callback is triggered")
-        new_runner = Runner()
-        new_runner.save_state()
+        new_runner = Submitit()
+        self.runner.save_state()
         logging.info("Submitit checkpointing callback is completed")
-        return DelayedSubmission(new_runner, self.config)
+        return DelayedSubmission(new_runner, self.config, self.cli_args)
 
 
 def map_cli_args_to_dist_config(cli_args: argparse.Namespace) -> dict:
@@ -111,7 +113,17 @@ def main(
         )
     else:
         if args.num_gpus > 1:
-            logger.info(f"Running in local mode with {args.num_gpus} ranks")
+            logging.info(f"Running in local mode with {args.num_gpus} ranks")
+            # HACK to disable multiprocess dataloading in local mode
+            # there is an open issue where LMDB's environment cannot be pickled and used
+            # during torch multiprocessing https://github.com/pytorch/examples/issues/526
+            # this HACK only works for a training submission where the config is passed in here
+            if "optim" in cfg and "num_workers" in cfg["optim"]:
+                cfg["optim"]["num_workers"] = 0
+                logging.info(
+                    "WARNING: running in local mode, setting dataloading num_workers to 0, see https://github.com/pytorch/examples/issues/526"
+                )
+
             launch_config = LaunchConfig(
                 min_nodes=1,
                 max_nodes=1,
diff --git a/src/fairchem/core/common/utils.py b/src/fairchem/core/common/utils.py
index 20b6e6922..8e9e3ceab 100644
--- a/src/fairchem/core/common/utils.py
+++ b/src/fairchem/core/common/utils.py
@@ -716,7 +716,8 @@ def radius_graph_pbc(
 
     # Tensor of unit cells
     cells_per_dim = [
-        torch.arange(-rep, rep + 1, device=device, dtype=torch.float) for rep in max_rep
+        torch.arange(-rep.item(), rep.item() + 1, device=device, dtype=torch.float)
+        for rep in max_rep
     ]
     unit_cell = torch.cartesian_prod(*cells_per_dim)
     num_cells = len(unit_cell)
diff --git a/tests/core/test_hydra_cli.yml b/tests/core/test_hydra_cli.yml
index 2064dd986..59e0a158a 100644
--- a/tests/core/test_hydra_cli.yml
+++ b/tests/core/test_hydra_cli.yml
@@ -1,4 +1,4 @@
 runner:
   _target_: fairchem.core.components.runner.MockRunner
   x: 10
-  y: 23
+  y: 23
\ No newline at end of file