Staging

.github/actions/setup-ci-tools/action.yml

@@ -88,3 +88,10 @@ runs:
     - name: Ignore ci directory
       shell: bash
       run: echo "ci/" >> .gitignore
+
+    - name: Stub ipywidgets for headless kernel execution
+      shell: bash
+      run: |
+        mkdir -p ~/.ipython/profile_default/startup
+        cp ${{ github.action_path }}/stub_widgets.py ~/.ipython/profile_default/startup/00-stub-widgets.py
+        echo "Installed ipywidgets stub to IPython startup"

.github/actions/setup-ci-tools/stub_widgets.py

@@ -0,0 +1,94 @@
+# Stub ipywidgets for headless/CI execution.
+# Replaces blocking widget calls with no-ops so notebooks execute without hanging.
+# In Colab/Jupyter with a real frontend, the real ipywidgets is used instead.
+#
+# Installed into ~/.ipython/profile_default/startup/ by the setup-ci-tools action
+# so it runs automatically before any notebook cell when nbconvert spawns a kernel.
+import sys
+import types
+import inspect
+
+
+class _NoOpWidget:
+    """A no-op stand-in for any ipywidgets widget class."""
+
+    children = []
+
+    def __init__(self, *args, **kwargs):
+        # Preserve value/options so _Interact can extract call defaults
+        object.__setattr__(self, "value", kwargs.get("value", None))
+        object.__setattr__(self, "options", kwargs.get("options", []))
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        pass
+
+    def __setattr__(self, name, value):
+        object.__setattr__(self, name, value)
+
+    def __getattr__(self, name):
+        # Return a no-op callable for any unknown method/attribute
+        return lambda *args, **kwargs: None
+
+
+class _Interact:
+    """Stub for widgets.interact / widgets.interactive.
+
+    Calls the wrapped function once with default values extracted from
+    widget stubs so that matplotlib outputs are captured by nbconvert.
+    """
+
+    def __call__(self, *args, **kwargs):
+        if len(args) == 1 and callable(args[0]) and not kwargs:
+            # Bare @widgets.interact — extract defaults from widget params
+            return self._call_with_defaults(args[0])
+        # @widgets.interact(param=slider) — return decorator
+        widget_kwargs = kwargs
+
+        def decorator(f):
+            return self._call_with_defaults(f, widget_kwargs)
+
+        return decorator
+
+    def _call_with_defaults(self, f, widget_kwargs=None):
+        sig = inspect.signature(f)
+        call_kwargs = {}
+        for name, param in sig.parameters.items():
+            widget = (widget_kwargs or {}).get(name)
+            if widget is None and param.default is not inspect.Parameter.empty:
+                widget = param.default
+            if isinstance(widget, _NoOpWidget) and widget.value is not None:
+                call_kwargs[name] = widget.value
+            elif widget is not None and not isinstance(widget, _NoOpWidget):
+                call_kwargs[name] = widget
+        try:
+            f(**call_kwargs)
+        except Exception as e:
+            print(f"[stub] interact call skipped: {e}")
+        return f
+
+
+class _StubModule(types.ModuleType):
+    """ipywidgets stub module.
+
+    Any attribute access returns _NoOpWidget so that
+    'from ipywidgets import AnythingAtAll' always succeeds.
+    """
+
+    interact = _Interact()
+    interactive = _Interact()
+
+    def __getattr__(self, name):
+        if name.startswith("__"):
+            raise AttributeError(name)
+        return _NoOpWidget
+
+
+stub = _StubModule("ipywidgets")
+stub.widgets = stub  # support: from ipywidgets import widgets
+sys.modules["ipywidgets"] = stub
+sys.modules["ipywidgets.widgets"] = stub
+
+print("ipywidgets stubbed for headless CI execution")

tutorials/W2D4_Macrolearning/W2D4_Tutorial3.ipynb

@@ -2,13 +2,14 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "b96e93dc",
    "metadata": {
     "colab_type": "text",
     "execution": {},
     "id": "view-in-github"
    },
    "source": [
-    "<a href=\"https://colab.research.google.com/github/neuromatch/NeuroAI_Course/blob/main/tutorials/W2D4_Macrolearning/student/W2D4_Tutorial3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/NeuroAI_Course/main/tutorials/W2D4_Macrolearning/student/W2D4_Tutorial3.ipynb\"  target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/neuromatch/NeuroAI_Course/blob/main/tutorials/W2D4_Macrolearning/W2D4_Tutorial3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/NeuroAI_Course/main/tutorials/W2D4_Macrolearning/W2D4_Tutorial3.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
    ]
   },
   {
@@ -77,8 +78,7 @@
    },
    "source": [
     "---\n",
-    "# Setup\n",
-    "\n"
+    "# Setup"
    ]
   },
   {
@@ -160,7 +160,7 @@
     "logging.getLogger('matplotlib.font_manager').disabled = True\n",
     "\n",
     "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'retina' # perfrom high definition rendering for images and plots\n",
+    "%config InlineBackend.figure_format = 'retina' # perform high definition rendering for images and plots\n",
     "plt.style.use(\"https://raw.githubusercontent.com/NeuromatchAcademy/course-content/main/nma.mplstyle\")"
    ]
   },
@@ -444,7 +444,7 @@
     "\n",
     "    def __len__(self):\n",
     "        \"\"\"Calculate the length of the dataset. It is obligatory for PyTorch to know in advance how many samples to expect (before training),\n",
-    "        thus we enforced to icnlude number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
+    "        thus we enforced to include number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
     "\n",
     "        return self.num_epochs * self.num_tasks\n",
     "\n",
@@ -758,7 +758,7 @@
     "\n",
     "    def __len__(self):\n",
     "        \"\"\"Calculate the length of the dataset. It is obligatory for PyTorch to know in advance how many samples to expect (before training),\n",
-    "        thus we enforced to icnlude number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
+    "        thus we enforced to include number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
     "\n",
     "        return self.num_epochs * self.num_tasks\n",
     "\n",
@@ -903,7 +903,7 @@
    "source": [
     "### Coding Exercise 1 Discussion\n",
     "\n",
-    "1. Do you think these particular tasks are similar? Do you expect the model to learn their general nature?\n"
+    "1. Do you think these particular tasks are similar? Do you expect the model to learn their general nature?"
    ]
   },
   {
@@ -1416,7 +1416,7 @@
     "execution": {}
    },
    "source": [
-    "*Estimated timing to here from start of tutorial: 35 minutes*\n"
+    "*Estimated timing to here from start of tutorial: 35 minutes*"
    ]
   },
   {
@@ -1753,7 +1753,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.22"
+   "version": "3.10.19"
   }
  },
  "nbformat": 4,

tutorials/W2D4_Macrolearning/instructor/W2D4_Tutorial3.ipynb

@@ -2,13 +2,14 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "d6cf2c1d",
    "metadata": {
     "colab_type": "text",
     "execution": {},
     "id": "view-in-github"
    },
    "source": [
-    "<a href=\"https://colab.research.google.com/github/neuromatch/NeuroAI_Course/blob/main/tutorials/W2D4_Macrolearning/student/W2D4_Tutorial3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/NeuroAI_Course/main/tutorials/W2D4_Macrolearning/student/W2D4_Tutorial3.ipynb\"  target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/neuromatch/NeuroAI_Course/blob/main/tutorials/W2D4_Macrolearning/instructor/W2D4_Tutorial3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/NeuroAI_Course/main/tutorials/W2D4_Macrolearning/instructor/W2D4_Tutorial3.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
    ]
   },
   {
@@ -77,8 +78,7 @@
    },
    "source": [
     "---\n",
-    "# Setup\n",
-    "\n"
+    "# Setup"
    ]
   },
   {
@@ -160,7 +160,7 @@
     "logging.getLogger('matplotlib.font_manager').disabled = True\n",
     "\n",
     "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'retina' # perfrom high definition rendering for images and plots\n",
+    "%config InlineBackend.figure_format = 'retina' # perform high definition rendering for images and plots\n",
     "plt.style.use(\"https://raw.githubusercontent.com/NeuromatchAcademy/course-content/main/nma.mplstyle\")"
    ]
   },
@@ -444,7 +444,7 @@
     "\n",
     "    def __len__(self):\n",
     "        \"\"\"Calculate the length of the dataset. It is obligatory for PyTorch to know in advance how many samples to expect (before training),\n",
-    "        thus we enforced to icnlude number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
+    "        thus we enforced to include number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
     "\n",
     "        return self.num_epochs * self.num_tasks\n",
     "\n",
@@ -758,7 +758,7 @@
     "\n",
     "    def __len__(self):\n",
     "        \"\"\"Calculate the length of the dataset. It is obligatory for PyTorch to know in advance how many samples to expect (before training),\n",
-    "        thus we enforced to icnlude number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
+    "        thus we enforced to include number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
     "\n",
     "        return self.num_epochs * self.num_tasks\n",
     "\n",
@@ -905,7 +905,7 @@
    "source": [
     "### Coding Exercise 1 Discussion\n",
     "\n",
-    "1. Do you think these particular tasks are similar? Do you expect the model to learn their general nature?\n"
+    "1. Do you think these particular tasks are similar? Do you expect the model to learn their general nature?"
    ]
   },
   {
@@ -1420,7 +1420,7 @@
     "execution": {}
    },
    "source": [
-    "*Estimated timing to here from start of tutorial: 35 minutes*\n"
+    "*Estimated timing to here from start of tutorial: 35 minutes*"
    ]
   },
   {
@@ -1759,7 +1759,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.22"
+   "version": "3.10.19"
   }
  },
  "nbformat": 4,

tutorials/W2D4_Macrolearning/student/W2D4_Tutorial3.ipynb

@@ -2,13 +2,14 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "252fd2fe",
    "metadata": {
     "colab_type": "text",
     "execution": {},
     "id": "view-in-github"
    },
    "source": [
-    "<a href=\"https://colab.research.google.com/github/neuromatch/NeuroAI_Course/blob/main/tutorials/W2D4_Macrolearning/student/W2D4_Tutorial3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/NeuroAI_Course/main/tutorials/W2D4_Macrolearning/student/W2D4_Tutorial3.ipynb\"  target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/neuromatch/NeuroAI_Course/blob/main/tutorials/W2D4_Macrolearning/student/W2D4_Tutorial3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp; <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/neuromatch/NeuroAI_Course/main/tutorials/W2D4_Macrolearning/student/W2D4_Tutorial3.ipynb\" target=\"_parent\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\"/></a>"
    ]
   },
   {
@@ -77,8 +78,7 @@
    },
    "source": [
     "---\n",
-    "# Setup\n",
-    "\n"
+    "# Setup"
    ]
   },
   {
@@ -160,7 +160,7 @@
     "logging.getLogger('matplotlib.font_manager').disabled = True\n",
     "\n",
     "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'retina' # perfrom high definition rendering for images and plots\n",
+    "%config InlineBackend.figure_format = 'retina' # perform high definition rendering for images and plots\n",
     "plt.style.use(\"https://raw.githubusercontent.com/NeuromatchAcademy/course-content/main/nma.mplstyle\")"
    ]
   },
@@ -444,7 +444,7 @@
     "\n",
     "    def __len__(self):\n",
     "        \"\"\"Calculate the length of the dataset. It is obligatory for PyTorch to know in advance how many samples to expect (before training),\n",
-    "        thus we enforced to icnlude number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
+    "        thus we enforced to include number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
     "\n",
     "        return self.num_epochs * self.num_tasks\n",
     "\n",
@@ -758,7 +758,7 @@
     "\n",
     "    def __len__(self):\n",
     "        \"\"\"Calculate the length of the dataset. It is obligatory for PyTorch to know in advance how many samples to expect (before training),\n",
-    "        thus we enforced to icnlude number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
+    "        thus we enforced to include number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
     "\n",
     "        return self.num_epochs * self.num_tasks\n",
     "\n",
@@ -801,8 +801,7 @@
     "execution": {}
    },
    "source": [
-    "[*Click for solution*](https://github.com/neuromatch/NeuroAI_Course/tree/main/tutorials/W2D4_Macrolearning/solutions/W2D4_Tutorial3_Solution_08b01bcf.py)\n",
-    "\n"
+    "[*Click for solution*](https://github.com/neuromatch/NeuroAI_Course/tree/main/tutorials/W2D4_Macrolearning/solutions/W2D4_Tutorial3_Solution_08b01bcf.py)"
    ]
   },
   {
@@ -842,7 +841,7 @@
    "source": [
     "### Coding Exercise 1 Discussion\n",
     "\n",
-    "1. Do you think these particular tasks are similar? Do you expect the model to learn their general nature?\n"
+    "1. Do you think these particular tasks are similar? Do you expect the model to learn their general nature?"
    ]
   },
   {
@@ -852,8 +851,7 @@
     "execution": {}
    },
    "source": [
-    "[*Click for solution*](https://github.com/neuromatch/NeuroAI_Course/tree/main/tutorials/W2D4_Macrolearning/solutions/W2D4_Tutorial3_Solution_576c8d87.py)\n",
-    "\n"
+    "[*Click for solution*](https://github.com/neuromatch/NeuroAI_Course/tree/main/tutorials/W2D4_Macrolearning/solutions/W2D4_Tutorial3_Solution_576c8d87.py)"
    ]
   },
   {
@@ -1015,8 +1013,7 @@
     "execution": {}
    },
    "source": [
-    "[*Click for solution*](https://github.com/neuromatch/NeuroAI_Course/tree/main/tutorials/W2D4_Macrolearning/solutions/W2D4_Tutorial3_Solution_593cdcd4.py)\n",
-    "\n"
+    "[*Click for solution*](https://github.com/neuromatch/NeuroAI_Course/tree/main/tutorials/W2D4_Macrolearning/solutions/W2D4_Tutorial3_Solution_593cdcd4.py)"
    ]
   },
   {
@@ -1277,7 +1274,7 @@
     "execution": {}
    },
    "source": [
-    "*Estimated timing to here from start of tutorial: 35 minutes*\n"
+    "*Estimated timing to here from start of tutorial: 35 minutes*"
    ]
   },
   {
@@ -1344,8 +1341,7 @@
     "execution": {}
    },
    "source": [
-    "[*Click for solution*](https://github.com/neuromatch/NeuroAI_Course/tree/main/tutorials/W2D4_Macrolearning/solutions/W2D4_Tutorial3_Solution_2753b5eb.py)\n",
-    "\n"
+    "[*Click for solution*](https://github.com/neuromatch/NeuroAI_Course/tree/main/tutorials/W2D4_Macrolearning/solutions/W2D4_Tutorial3_Solution_2753b5eb.py)"
    ]
   },
   {
@@ -1589,7 +1585,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.22"
+   "version": "3.10.19"
   }
  },
  "nbformat": 4,