diff --git a/pytorch_lightning/root_module/model_saving.py b/pytorch_lightning/root_module/model_saving.py
index 3d242adadc..2537785175 100644
--- a/pytorch_lightning/root_module/model_saving.py
+++ b/pytorch_lightning/root_module/model_saving.py
@@ -86,7 +86,7 @@ class TrainerIO(object):
     # --------------------
     # HPC IO
     # --------------------
-    def enable_auto_hpc_walltime_manager(self):  # pragma: no cover
+    def enable_auto_hpc_walltime_manager(self):
         if self.cluster is None:
             return
 
@@ -157,6 +157,8 @@ class TrainerIO(object):
         # do the actual save
         torch.save(checkpoint_dict, filepath)
 
+        return filepath
+
     def hpc_load(self, folderpath, on_gpu):
         filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, self.max_ckpt_in_folder(folderpath))
 
diff --git a/tests/test_models.py b/tests/test_models.py
index c20c927d6a..e5781d3211 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -3,16 +3,18 @@ from pytorch_lightning import Trainer
 from pytorch_lightning.examples.new_project_templates.lightning_module_template import LightningTemplateModel
 from pytorch_lightning.testing_models.lm_test_module import LightningTestModel
 from argparse import Namespace
-from test_tube import Experiment
+from test_tube import Experiment, SlurmCluster
 from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
 from pytorch_lightning.utils.debugging import MisconfigurationException
 from pytorch_lightning.root_module import memory
 from pytorch_lightning.models.trainer import reduce_distributed_output
+from pytorch_lightning.root_module import model_saving
 import numpy as np
 import warnings
 import torch
 import os
 import shutil
+import pdb
 
 SEED = 2334
 torch.manual_seed(SEED)
@@ -22,6 +24,25 @@ np.random.seed(SEED)
 # ------------------------------------------------------------------------
 # TESTS
 # ------------------------------------------------------------------------
+def test_loading_meta_tags():
+    hparams = get_hparams()
+
+    save_dir = init_save_dir()
+
+    # save tags
+    exp = get_exp(False)
+    exp.tag({'some_str':'a_str', 'an_int': 1, 'a_float': 2.0})
+    exp.argparse(hparams)
+    exp.save()
+
+    # load tags
+    tags_path = exp.get_data_path(exp.name, exp.version) + '/meta_tags.csv'
+    tags = model_saving.load_hparams_from_tags_csv(tags_path)
+
+    assert tags.batch_size == 32 and tags.hidden_dim == 1000
+
+    clear_save_dir()
+
 def test_dp_output_reduce():
 
     # test identity when we have a single gpu
@@ -43,6 +64,68 @@ def test_dp_output_reduce():
     assert reduced['b']['c'] == out['b']['c']
 
 
+def test_cpu_slurm_saving_loading():
+    """
+    Verify model save/load/checkpoint on CPU
+    :return:
+    """
+    hparams = get_hparams()
+    model = LightningTestModel(hparams)
+
+    save_dir = init_save_dir()
+
+    # exp file to get meta
+    exp = get_exp(False)
+    exp.argparse(hparams)
+    exp.save()
+
+    trainer_options = dict(
+        max_nb_epochs=1,
+        cluster=SlurmCluster(),
+        experiment=exp,
+        checkpoint_callback=ModelCheckpoint(save_dir)
+    )
+
+    # fit model
+    trainer = Trainer(**trainer_options)
+    result = trainer.fit(model)
+    real_global_step = trainer.global_step
+
+    # traning complete
+    assert result == 1, 'amp + ddp model failed to complete'
+
+    # test saving checkpoint
+    ckpt_test = os.path.join(save_dir, 'test.ckpt')
+    trainer.save_checkpoint(ckpt_test)
+
+    # test registering a save function
+    trainer.enable_auto_hpc_walltime_manager()
+
+    # test model loading with a map_location
+    pretrained_model = load_model(exp, save_dir, True)
+
+    # test model preds
+    run_prediction(model.test_dataloader, pretrained_model)
+
+    trainer.model = pretrained_model
+    trainer.optimizers = pretrained_model.configure_optimizers()
+
+    # test HPC saving
+    saved_filepath = trainer.hpc_save(save_dir, exp)
+    assert os.path.exists(saved_filepath)
+
+    # test HPC loading
+    trainer.global_step = 20000000
+    trainer.hpc_load(save_dir, on_gpu=False)
+    assert trainer.global_step == real_global_step and trainer.global_step != 20000000
+
+    # test freeze on gpu
+    model.freeze()
+    model.unfreeze()
+
+    clear_save_dir()
+
+
 def test_amp_gpu_ddp_slurm_managed():
     """
     Make sure DDP + AMP work