diff --git a/arg_parser.py b/arg_parser.py
index 0bdea97..d8d8fde 100644
--- a/arg_parser.py
+++ b/arg_parser.py
@@ -19,7 +19,7 @@ def __init__(self):
         self.parser.add_argument('--seed', type=int, default=0, help='Random seed for reproducible outputs.')
 
         # System configurations
-        self.parser.add_argument('--gpu_ids', type=str, default='0' if torch.cuda.is_available() else '-1', help='Comma-separated list of GPU IDs. Use -1 for CPU.')
+        self.parser.add_argument('--gpu_ids', type=str, default='0,1' if torch.cuda.is_available() else '-1', help='Comma-separated list of GPU IDs. Use -1 for CPU.')
         self.parser.add_argument('--num_workers', default=4, type=int, help='Number of threads for the DataLoader.')
         self.parser.add_argument('--dataset_root', type=str, default=join(dirname(argv[0]), 'data'), help='The root of the dataset directory')
 
diff --git a/model.py b/model.py
index ef66da0..0254f3e 100644
--- a/model.py
+++ b/model.py
@@ -26,12 +26,6 @@ def get_model(model_load_path):
     }
 
     model = ResNet1d(**model_args)
-
-    # TODO because the original model authors did not register parameters,
-    # correctly with nn.ModuleList, it is impossible to be able to
-    # simultaneously load the previous model check points, and run on multiple
-    # GPUs. This should be remedied a la
-    # https://discuss.pytorch.org/t/discrepancy-between-manual-parameter-registration-vs-using-nn-modulelist-when-parallelizing/181055
     model.load_state_dict(torch.load(model_load_path, map_location='cpu')['model'])
 
     # The model originally took 12 channels as input (corresponding to a 12
@@ -175,14 +169,14 @@ def __init__(self, input_dim, blocks_dim, n_classes, kernel_size=17, dropout_rat
         self.bn1 = nn.BatchNorm1d(n_filters_out)
 
         # Residual block layers
-        self.res_blocks = []
         for i, (n_filters, n_samples) in enumerate(blocks_dim):
             n_filters_in, n_filters_out = n_filters_out, n_filters
             n_samples_in, n_samples_out = n_samples_out, n_samples
             downsample = _downsample(n_samples_in, n_samples_out)
-            resblk1d = ResBlock1d(n_filters_in, n_filters_out, downsample, kernel_size, dropout_rate)
-            self.add_module('resblock1d_{0}'.format(i), resblk1d)
-            self.res_blocks += [resblk1d]
+            setattr(self, f'resblock1d_{i}', ResBlock1d(
+                n_filters_in, n_filters_out, downsample,
+                kernel_size, dropout_rate
+            ))
 
         # Linear layer
         n_filters_last, n_samples_last = blocks_dim[-1]
@@ -198,8 +192,8 @@ def forward(self, x):
 
         # Residual blocks
         y = x
-        for blk in self.res_blocks:
-            x, y = blk(x, y)
+        for i in range(self.n_blk):
+            x, y = getattr(self, f'resblock1d_{i}')(x, y)
 
         # Flatten array
         x = x.view(x.size(0), -1)