diff --git a/notebooks/supplementary_examples/00_nearfield_ptychography.ipynb b/notebooks/supplementary_examples/00_nearfield_ptychography.ipynb
index 621ae77..643aa2a 100644
--- a/notebooks/supplementary_examples/00_nearfield_ptychography.ipynb
+++ b/notebooks/supplementary_examples/00_nearfield_ptychography.ipynb
@@ -76,7 +76,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "c7b8dac1-ec91-42b8-b609-66e8860fcb70",
    "metadata": {},
@@ -168,7 +167,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "ee3cbe0e-7393-4e07-8e6c-a0063eb55dbe",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import ptypy, h5py, os\n",
@@ -178,7 +179,7 @@
     "ptypy.load_ptyscan_module(\"hdf5_loader\")\n",
     "\n",
     "# This will import the GPU engines\n",
-    "ptypy.load_gpu_engines(\"cuda\")  \n",
+    "ptypy.load_gpu_engines(\"cupy\")  \n",
     "\n",
     "# Root directory of tutorial data\n",
     "tutorial_data_home = \"../../data/\"\n",
@@ -260,7 +261,7 @@
     "# Reconstruct using GPU-accelerated DM/ML engines\n",
     "p.engines = u.Param()\n",
     "p.engines.DM = u.Param()\n",
-    "p.engines.DM.name = 'DM_pycuda'\n",
+    "p.engines.DM.name = 'DM_cupy'\n",
     "p.engines.DM.numiter = 500\n",
     "p.engines.DM.numiter_contiguous = 10\n",
     "p.engines.DM.probe_support = None\n",
@@ -269,7 +270,7 @@
     "p.engines.DM.probe_update_start = 0\n",
     "\n",
     "p.engines.ML = u.Param()\n",
-    "p.engines.ML.name = 'ML_pycuda'\n",
+    "p.engines.ML.name = 'ML_cupy'\n",
     "p.engines.ML.numiter = 500\n",
     "p.engines.ML.numiter_contiguous = 10\n",
     "p.engines.ML.ML_type = 'Gaussian'\n",
@@ -287,9 +288,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "PtyPy",
+   "display_name": "PtyPy (cupy)",
    "language": "python",
-   "name": "ptypy_pycuda"
+   "name": "ptypy_cupy"
   },
   "language_info": {
    "codemirror_mode": {
@@ -301,7 +302,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/supplementary_examples/01_object_regularisation.ipynb b/notebooks/supplementary_examples/01_object_regularisation.ipynb
index 80883ed..c7db1ca 100644
--- a/notebooks/supplementary_examples/01_object_regularisation.ipynb
+++ b/notebooks/supplementary_examples/01_object_regularisation.ipynb
@@ -70,7 +70,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "e5286f05-4595-4d85-857a-612274f213d9",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import h5py\n",
@@ -269,7 +271,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "a495ee4f-604b-4f68-a023-032061ae43dd",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import ptypy, h5py, os\n",
@@ -279,7 +283,7 @@
     "ptypy.load_ptyscan_module(\"hdf5_loader\")\n",
     "\n",
     "# This will import the GPU engines\n",
-    "ptypy.load_gpu_engines(\"cuda\")\n",
+    "ptypy.load_gpu_engines(\"pycuda\")\n",
     "\n",
     "# Load custom engine\n",
     "from ptypy.custom.DM_pycuda_object_regul import DM_pycuda_object_regul\n",
@@ -385,7 +389,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "PtyPy",
+   "display_name": "PtyPy (pycuda)",
    "language": "python",
    "name": "ptypy_pycuda"
   },
@@ -399,7 +403,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/supplementary_examples/02_live_visualisation.ipynb b/notebooks/supplementary_examples/02_live_visualisation.ipynb
index 03782ff..ddf563d 100644
--- a/notebooks/supplementary_examples/02_live_visualisation.ipynb
+++ b/notebooks/supplementary_examples/02_live_visualisation.ipynb
@@ -65,7 +65,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "b81d242c-deda-4b5d-a577-b55b42e46ac9",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "with open(\"./ptypy_run_dls_i08_nanogold.py\", \"w\") as f:\n",
@@ -78,6 +80,18 @@
     "\"\"\")"
    ]
   },
+  {
+   "cell_type": "raw",
+   "id": "75d00b06-5dc5-46da-93a8-5b160285845b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# commands to copy into terminal\n",
+    "cd $HOME/tutorials/notebooks/supplementary_examples\n",
+    "srun -n 4 -c 2 --gpus-per-task=1 --gpu-bin=None python ptypy_run_dls_i08_nanogold.py"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6a288a19-ea65-41f6-9bb3-0337a00dde6a",
@@ -139,7 +153,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "ca168d91-e5b8-4d41-8b5d-1c37035f906a",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import ptypy\n",
@@ -160,7 +176,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "06fe8608-449e-4b66-a512-0f339ea7afe8",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Refresh this cell multiple time \n",
@@ -182,9 +200,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "PtyPy",
+   "display_name": "PtyPy (cupy)",
    "language": "python",
-   "name": "ptypy_pycuda"
+   "name": "ptypy_cupy"
   },
   "language_info": {
    "codemirror_mode": {
@@ -196,7 +214,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/supplementary_examples/03_simulating_data.ipynb b/notebooks/supplementary_examples/03_simulating_data.ipynb
index 4a99c8c..5697421 100644
--- a/notebooks/supplementary_examples/03_simulating_data.ipynb
+++ b/notebooks/supplementary_examples/03_simulating_data.ipynb
@@ -153,7 +153,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "3d920728-63e9-4070-bbab-455a97d8303b",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import ptypy\n",
@@ -262,9 +264,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel) [User Conda - cuda12.2]",
+   "display_name": "PtyPy (cupy)",
    "language": "python",
-   "name": "conda-env-User_Conda_-_cuda12.2-python3"
+   "name": "ptypy_cupy"
   },
   "language_info": {
    "codemirror_mode": {
@@ -276,7 +278,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/supplementary_examples/04_modified_initial_probe.ipynb b/notebooks/supplementary_examples/04_modified_initial_probe.ipynb
index 67e73dc..d7be21a 100644
--- a/notebooks/supplementary_examples/04_modified_initial_probe.ipynb
+++ b/notebooks/supplementary_examples/04_modified_initial_probe.ipynb
@@ -14,7 +14,7 @@
    "id": "84411552-5a4e-4a26-a702-cdf0b23feab1",
    "metadata": {},
    "source": [
-    "This example builds on the data set used in [The Hdf5Loader](../experimental_xray_data/00_data_loading.ipynb) and shows how it is possible in PtyPy to set up a reconstruction, execute with ```level=4```, then modify the model of the initial probe and run the reconstruction via ```P.run()``` using the newly generated initial probe\n",
+    "This example is using the same data and parameters as in [Loading HDF5 Data](../experimental_xray_data/00_data_loading.ipynb) and shows how it is possible in PtyPy to set up a reconstruction, execute with ```level=4```, then modify the model of the initial probe and run the reconstruction via ```P.run()``` using the newly generated initial probe\n",
     "\n",
     "![](./_assets/modified_probe.png)"
    ]
@@ -31,7 +31,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "903a1bb0-5ff1-401a-8f49-a0b5913eabc1",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import ptypy, os\n",
@@ -137,7 +139,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "47c1927b-bc49-4dad-99bf-4746c1590bd1",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from ptypy.core.illumination import _propagation, illumination_desc, aperture\n",
@@ -173,7 +177,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "715591d6-e765-498d-a7da-22e19db20a87",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -195,7 +201,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "17d9466c-1296-4fe8-bdf9-65541db9b8ef",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Run the reconstruction using the modified initial probe\n",
@@ -205,9 +213,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10 [DLS Conda]",
+   "display_name": "PtyPy (cupy)",
    "language": "python",
-   "name": "conda-env-DLS_Conda-python3.10-kernel.json"
+   "name": "ptypy_cupy"
   },
   "language_info": {
    "codemirror_mode": {
@@ -219,7 +227,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,