diff --git a/README.md b/README.md
index 4ca9cf78..d55f9dc4 100644
--- a/README.md
+++ b/README.md
@@ -13,20 +13,6 @@ We figure an actuary who uses python has reasonable familiarity with pandas and
 scikit-learn, so they can spend as little mental energy as possible learning yet
 another API.
 
-##### Now with GPU support
-New in version `0.5.0` - `chainladder` now supports CUDA-based GPU computations by way of [CuPY](https://github.com/cupy/cupy).  You can now swap `array_backend` between `numpy` and `cupy` to switch between CPU and GPU-based computations.
-
-Array backends can be set globally:
-```python
-import chainladder as cl
-cl.array_backend('cupy')
-```
-Alternatively, they can be set per `Triangle` instance.
-```python
-cl.Triangle(..., array_backend='cupy')
-```
-**Note** you must have a CUDA-enabled graphics card and [CuPY](https://github.com/cupy/cupy) installed to use the GPU backend.
-
 
 ## Documentation
 Please visit the [Documentation](https://chainladder-python.readthedocs.io/en/latest/) page for examples, how-tos, and source
@@ -54,3 +40,17 @@ Alternatively, install directly from github:
 
 Note: This package requires Python 3.5 and later, numpy 1.12.0 and later,
 pandas 0.23.0 and later, scikit-learn 0.18.0 and later.
+
+##### Now with GPU support
+New in version `0.5.0` - `chainladder` now supports CUDA-based GPU computations by way of [CuPY](https://github.com/cupy/cupy).  You can now swap `array_backend` between `numpy` and `cupy` to switch between CPU and GPU-based computations.
+
+Array backends can be set globally:
+```python
+import chainladder as cl
+cl.array_backend('cupy')
+```
+Alternatively, they can be set per `Triangle` instance.
+```python
+cl.Triangle(..., array_backend='cupy')
+```
+**Note** you must have a CUDA-enabled graphics card and [CuPY](https://github.com/cupy/cupy) installed to use the GPU backend.
diff --git a/chainladder/core/io.py b/chainladder/core/io.py
index 12f637ec..0f1c6ae7 100644
--- a/chainladder/core/io.py
+++ b/chainladder/core/io.py
@@ -22,7 +22,12 @@ def to_json(self):
         '''
         def sparse_out(tri):
             k, v, o, d = tri.shape
-            coo = coo_matrix(np.nan_to_num(tri.values.reshape((k*v*o, d))))
+            xp = cp.get_array_module(tri)
+            if xp == cp:
+                out = cp.asnumpy(tri)
+            else:
+                out = tri
+            coo = coo_matrix(np.nan_to_num(out.reshape((k*v*o, d))))
             return json.dumps(dict(zip([str(item) for item in zip(coo.row, coo.col)], coo.data)))
 
         json_dict = {}
@@ -38,15 +43,20 @@ def sparse_out(tri):
             json_dict[attribute] = {
                 'dtype': str(getattr(self, attribute).dtype),
                 'array': getattr(self, attribute).tolist()}
-        if np.sum(np.nan_to_num(self.values)==0) / np.prod(self.shape) > 0.40:
+        xp = cp.get_array_module(self.values)
+        if xp == cp:
+            out = cp.asnumpy(self.cum_to_incr().values)
+        else:
+            out = self.cum_to_incr().values
+        if np.sum(np.nan_to_num(out)==0) / np.prod(self.shape) > 0.40:
             json_dict['values'] = {
-                'dtype': str(self.values.dtype),
-                'array': sparse_out(self.cum_to_incr()),
+                'dtype': str(out.dtype),
+                'array': sparse_out(out),
                 'sparse': True}
         else:
             json_dict['values'] = {
-                'dtype': str(self.values.dtype),
-                'array': self.values.tolist(),
+                'dtype': str(out.dtype),
+                'array': out.tolist(),
                 'sparse': False}
         json_dict['key_labels'] = self.key_labels
         json_dict['origin_grain'] = self.origin_grain
diff --git a/chainladder/core/slice.py b/chainladder/core/slice.py
index 69673b80..981488b3 100644
--- a/chainladder/core/slice.py
+++ b/chainladder/core/slice.py
@@ -23,7 +23,7 @@ def get_idx(self, idx):
         x_0 = list(pd.Series([item[0] for item in idx.values[:, 0]]).unique())
         x_1 = list(pd.Series([item[1] for item in idx.values[0, :]]).unique())
         obj.values = \
-            obj.values[self._contig_slice(x_0)][:, self._contig_slice(x_1)]
+            obj.values[self._contig_slice(x_0), ...][:, self._contig_slice(x_1), ...]
         obj.values[obj.values == 0] = np.nan
         return obj
 
diff --git a/chainladder/utils/tests/test_utilities.py b/chainladder/utils/tests/test_utilities.py
index 39fe5675..1205da96 100644
--- a/chainladder/utils/tests/test_utilities.py
+++ b/chainladder/utils/tests/test_utilities.py
@@ -26,7 +26,7 @@ def test_triangle_json_io():
 
 def test_json_for_val():
     x = cl.load_dataset('raa').dev_to_val().to_json()
-    cl.read_json(x) == cl.load_dataset('raa').dev_to_val()
+    assert cl.read_json(x) == cl.load_dataset('raa').dev_to_val()
 
 def test_estimator_json_io():
     assert cl.read_json(cl.Development().to_json()).get_params() == \
diff --git a/chainladder/utils/utility_functions.py b/chainladder/utils/utility_functions.py
index 71746155..387ac06e 100644
--- a/chainladder/utils/utility_functions.py
+++ b/chainladder/utils/utility_functions.py
@@ -3,6 +3,7 @@
 # file, You can obtain one at https://mozilla.org/MPL/2.0/.
 import pandas as pd
 import numpy as np
+from chainladder.utils.cupy import cp
 from scipy.sparse import coo_matrix
 import joblib
 import json
@@ -48,7 +49,7 @@ def read_pickle(path):
     return joblib.load(path)
 
 
-def read_json(json_str):
+def read_json(json_str, array_backend=None):
     def sparse_in(json_str, dtype, shape):
         k, v, o, d = shape
         x = json.loads(json_str)
@@ -60,6 +61,9 @@ def sparse_in(json_str, dtype, shape):
         new[new==0] = np.nan
         return new
 
+    if array_backend is None:
+        from chainladder import ARRAY_BACKEND
+        array_backend = ARRAY_BACKEND
     json_dict = json.loads(json_str)
     if type(json_dict) is list:
         import chainladder as cl
@@ -68,7 +72,7 @@ def sparse_in(json_str, dtype, shape):
              cl.__dict__[item['__class__']]().set_params(**item['params']))
             for item in json_dict])
     elif 'kdims' in json_dict.keys():
-        tri = Triangle()
+        tri = Triangle(array_backend=array_backend)
         arrays = ['kdims', 'vdims', 'odims', 'ddims']
         for array in arrays:
             setattr(tri, array, np.array(
@@ -91,7 +95,8 @@ def sparse_in(json_str, dtype, shape):
                 tri = tri.incr_to_cum()
         else:
             tri.values = np.array(json_dict['values']['array'], dtype=json_dict['values']['dtype'])
-
+        if array_backend == 'cupy':
+            tri.values = cp.array(tri.values)
         return tri
     else:
         import chainladder as cl
diff --git a/docs/tutorials/triangle-tutorial.ipynb b/docs/tutorials/triangle-tutorial.ipynb
index 28169824..14847926 100644
--- a/docs/tutorials/triangle-tutorial.ipynb
+++ b/docs/tutorials/triangle-tutorial.ipynb
@@ -8977,7 +8977,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "x.sum().to_clipboard() # Automatically converts to a pandas dataframe and puts in the clipbard for pasting in Excel"
+    "x.sum().to_clipboard() # Automatically converts to a pandas dataframe and puts in the clipboard for pasting in Excel"
    ]
   },
   {
diff --git a/setup.py b/setup.py
index 33ad068d..8e80e802 100644
--- a/setup.py
+++ b/setup.py
@@ -17,7 +17,7 @@
     version=version,
     maintainer='John Bogaardt',
     maintainer_email='jbogaardt@gmail.com',
-    packages=['{}.{}'.format(name, p) for p in find_packages(where=name)]+['chainladder'],
+    packages=['{}.{}'.format(name, p) for p in find_packages(where=name)]+[name],
     scripts=[],
     url=url,
     download_url='{}/archive/v{}.tar.gz'.format(url, version),