diff --git a/python/cugraph-dgl/examples/dataset_from_disk_cudf.ipynb b/python/cugraph-dgl/examples/dataset_from_disk_cudf.ipynb
index dd99d78..66ee205 100644
--- a/python/cugraph-dgl/examples/dataset_from_disk_cudf.ipynb
+++ b/python/cugraph-dgl/examples/dataset_from_disk_cudf.ipynb
@@ -22,7 +22,7 @@
     "import rmm\n",
     "import torch\n",
     "from rmm.allocators.torch import rmm_torch_allocator\n",
-    "rmm.reinitialize(pool_allocator=True, initial_pool_size=15e9)\n",
+    "rmm.reinitialize(pool_allocator=True, initial_pool_size=\"14GiB\")\n",
     "#Switch to async pool in case of memory issues due to fragmentation of the pool\n",
     "#rmm.mr.set_current_device_resource(rmm.mr.CudaAsyncMemoryResource(initial_pool_size=15e9))\n",
     "torch.cuda.memory.change_current_allocator(rmm_torch_allocator)"
@@ -51,8 +51,10 @@
    "source": [
     "def load_dgl_dataset(dataset_name='ogbn-products'):\n",
     "    from ogb.nodeproppred import DglNodePropPredDataset\n",
-    "    dataset_root = '/raid/vjawa/gnn/'\n",
-    "    dataset =  DglNodePropPredDataset(name = dataset_name, root=dataset_root)\n",
+    "    from unittest.mock import patch\n",
+    "    dataset_root = '/tmp/'\n",
+    "    with patch(\"builtins.input\", return_value=\"y\"):\n",
+    "        dataset =  DglNodePropPredDataset(name = dataset_name, root=dataset_root)\n",
     "    split_idx = dataset.get_idx_split()\n",
     "    train_idx, valid_idx, test_idx = split_idx[\"train\"], split_idx[\"valid\"], split_idx[\"test\"]\n",
     "    g, label = dataset[0]\n",
@@ -111,7 +113,7 @@
     "    g,                               \n",
     "    train_idx.to('cuda'),                        # train_nid must be on GPU.\n",
     "    sampler,\n",
-    "    sampling_output_dir=\"/raid/vjawa/obgn_products_sampling/\", # Path to save sampling results to, Change to the fastest IO path available\n",
+    "    sampling_output_dir=\"/tmp/\", # Path to save sampling results to, Change to the fastest IO path available\n",
     "    device=torch.device('cuda'),    # The device argument must be GPU.\n",
     "    num_workers=0,                 # Number of workers must be 0.\n",
     "    batch_size=batch_size,\n",
@@ -254,7 +256,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.12.7"
   },
   "vscode": {
    "interpreter": {