diff --git a/jupyter/activation_histograms.ipynb b/jupyter/activation_histograms.ipynb
index d59ef93512d1770f0e1427d3111cb9a30ad7a9d7..d8c1c7ef1d6c6f9b32ebc97d560cfbda49700da1 100644
--- a/jupyter/activation_histograms.ipynb
+++ b/jupyter/activation_histograms.ipynb
@@ -23,6 +23,8 @@
    },
    "outputs": [],
    "source": [
+    "%matplotlib inline\n",
+    "\n",
     "import torch\n",
     "import matplotlib.pyplot as plt\n",
     "import os\n",
@@ -104,13 +106,15 @@
     "# same subset for both phases of histogram collection - more on that below\n",
     "\n",
     "dataset = 'imagenet'\n",
-    "dataset_path = '~/datasets/imagenet'\n",
+    "dataset_path = '/datasets/imagenet'\n",
+    "arch = 'resnet18'\n",
     "batch_size = 256\n",
     "num_workers = 10\n",
     "subset_size = 0.01\n",
     "\n",
     "_, _, test_loader, _ = distiller.apputils.load_data(\n",
-    "    dataset, os.path.expanduser(dataset_path), batch_size, num_workers,\n",
+    "    dataset, arch, os.path.expanduser(dataset_path), \n",
+    "    batch_size, num_workers,\n",
     "    effective_test_size=subset_size, fixed_subset=True)"
    ]
   },
@@ -359,7 +363,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/jupyter/performance.ipynb b/jupyter/performance.ipynb
index cf262f0b85b1743fa3b03d54874a8b1f31ed9c9b..580daea49e06af335c83271ac1d0d2443246dc64 100644
--- a/jupyter/performance.ipynb
+++ b/jupyter/performance.ipynb
@@ -6,6 +6,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%matplotlib inline\n",
+    "\n",
     "import torch\n",
     "import torchvision\n",
     "import torch.nn as nn\n",
@@ -216,7 +218,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/jupyter/post_train_quant_convert_pytorch.ipynb b/jupyter/post_train_quant_convert_pytorch.ipynb
index 74e4d0a11383431c849c8ea91b5970dd99c11f1d..799c056523c3d78756f7d883cca1e69b8cc43dc1 100644
--- a/jupyter/post_train_quant_convert_pytorch.ipynb
+++ b/jupyter/post_train_quant_convert_pytorch.ipynb
@@ -84,12 +84,14 @@
     "\n",
     "subset_size = 1.0 # To save time, can set to value < 1.0\n",
     "dataset = 'imagenet'\n",
-    "dataset_path = os.path.expanduser('/data2/datasets/imagenet')\n",
+    "dataset_path = os.path.expanduser('/datasets/imagenet')\n",
+    "arch = 'resnet18'\n",
     "\n",
     "batch_size_gpu = 256\n",
     "num_workers_gpu = 10\n",
     "_, _, test_loader_gpu, _ = distiller.apputils.load_data(\n",
-    "    dataset, dataset_path, batch_size_gpu, num_workers_gpu,\n",
+    "    dataset, arch, dataset_path, \n",
+    "    batch_size_gpu, num_workers_gpu,\n",
     "    effective_test_size=subset_size, fixed_subset=True, test_only=True)"
    ]
   },
@@ -103,7 +105,8 @@
     "batch_size_cpu = 44\n",
     "num_workers_cpu = 10\n",
     "_, _, test_loader_cpu, _ = distiller.apputils.load_data(\n",
-    "    dataset, dataset_path, batch_size_cpu, num_workers_cpu,\n",
+    "    dataset, arch, dataset_path, \n",
+    "    batch_size_cpu, num_workers_cpu,\n",
     "    effective_test_size=subset_size, fixed_subset=True, test_only=True)"
    ]
   },
@@ -473,7 +476,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,