diff --git a/docs_nnx/mnist_tutorial.ipynb b/docs_nnx/mnist_tutorial.ipynb
index df3b8373d..fb9103467 100644
--- a/docs_nnx/mnist_tutorial.ipynb
+++ b/docs_nnx/mnist_tutorial.ipynb
@@ -39,7 +39,7 @@
    "source": [
     "## 2. Load the MNIST dataset\n",
     "\n",
-    "First, you need to load the MNIST dataset and then prepare the training and testing sets via Tensorflow Datasets (TFDS). You normalize image values, shuffle the data and divide it into batches, and prefetch samples to enhance performance."
+    "First, we need to load the MNIST dataset and prepare the training and testing sets using the Hugging Face [`datasets`](https://huggingface.co/docs/datasets) package. We'll normalize the image values and shuffle the training data. The `make_batches` helper converts the dataset into batches of NumPy arrays with a channel dimension added, dropping any incomplete final batch."
    ]
   },
   {
@@ -49,37 +49,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tensorflow_datasets as tfds  # TFDS to download MNIST.\n",
-    "import tensorflow as tf  # TensorFlow / `tf.data` operations.\n",
-    "\n",
-    "tf.random.set_seed(0)  # Set the random seed for reproducibility.\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from datasets import load_dataset\n",
     "\n",
     "train_steps = 1200\n",
     "eval_every = 200\n",
     "batch_size = 32\n",
     "\n",
-    "train_ds: tf.data.Dataset = tfds.load('mnist', split='train')\n",
-    "test_ds: tf.data.Dataset = tfds.load('mnist', split='test')\n",
-    "\n",
-    "train_ds = train_ds.map(\n",
-    "  lambda sample: {\n",
-    "    'image': tf.cast(sample['image'], tf.float32) / 255,\n",
-    "    'label': sample['label'],\n",
-    "  }\n",
-    ")  # normalize train set\n",
-    "test_ds = test_ds.map(\n",
-    "  lambda sample: {\n",
-    "    'image': tf.cast(sample['image'], tf.float32) / 255,\n",
-    "    'label': sample['label'],\n",
-    "  }\n",
-    ")  # Normalize the test set.\n",
-    "\n",
-    "# Create a shuffled dataset by allocating a buffer size of 1024 to randomly draw elements from.\n",
-    "train_ds = train_ds.repeat().shuffle(1024)\n",
-    "# Group into batches of `batch_size` and skip incomplete batches, prefetch the next sample to improve latency.\n",
-    "train_ds = train_ds.batch(batch_size, drop_remainder=True).take(train_steps).prefetch(1)\n",
-    "# Group into batches of `batch_size` and skip incomplete batches, prefetch the next sample to improve latency.\n",
-    "test_ds = test_ds.batch(batch_size, drop_remainder=True).prefetch(1)"
+    "dataset = load_dataset('mnist')\n",
+    "train_ds = dataset['train'].shuffle(seed=0)\n",
+    "test_ds = dataset['test']\n",
+    "\n",
+    "def make_batches(ds, batch_size):\n",
+    "  \"\"\"Yield batches of normalized (image, label) numpy arrays.\"\"\"\n",
+    "  for i in range(0, len(ds), batch_size):\n",
+    "    batch = ds[i : i + batch_size]\n",
+    "    if len(batch['label']) < batch_size:  # drop incomplete final batch\n",
+    "      break\n",
+    "    images = np.stack([\n",
+    "      np.array(img, dtype=np.float32)[..., None] / 255.0\n",
+    "      for img in batch['image']\n",
+    "    ])\n",
+    "    yield {'image': images, 'label': np.array(batch['label'])}"
    ]
   },
   {
@@ -94,14 +86,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "6",
    "metadata": {},
    "outputs": [
     {
      "data": {
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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_8a9f6ce3f15a409e81ea64b597e463c0\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -113,7 +105,7 @@
     {
      "data": {
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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_79fdedab11444712830d108f7c0b1793\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+       "<div style=\"display:none\"> <script type=\"application/octet-stream\" 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</script> 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@@ -163,7 +155,7 @@
    "source": [
     "### Run the model\n",
     "\n",
-    "Let's put the CNN model to the test!  Here, you’ll perform a forward pass with arbitrary data and print the results."
+    "Let's put the CNN model to the test!  Here, we’ll perform a forward pass with arbitrary data and print the results."
    ]
   },
   {
@@ -175,8 +167,8 @@
     {
      "data": {
       "text/plain": [
-       "Array([[ 0.11409501,  0.4546129 , -0.6421267 , -0.12122799, -0.22859162,\n",
-       "         0.13616608,  1.0126765 , -0.03625144,  0.6132787 , -0.06018351]],      dtype=float32)"
+       "Array([[ 0.11406855,  0.45456237, -0.6421087 , -0.12120342, -0.22858061,\n",
+       "         0.13615587,  1.0126833 , -0.036236  ,  0.6132397 , -0.0601905 ]],      dtype=float32)"
       ]
      },
      "execution_count": 3,
@@ -198,7 +190,7 @@
    "source": [
     "## 4. Create the optimizer and define some metrics\n",
     "\n",
-    "In Flax NNX, you need to create an `nnx.Optimizer` object to manage the model's parameters and apply gradients during training. `nnx.Optimizer` receives the model's reference, so that it can update its parameters, and an [Optax](https://optax.readthedocs.io/) optimizer to define the update rules. Additionally, you will define an `nnx.MultiMetric` object to keep track of the `Accuracy` and the `Average` loss."
+    "In Flax NNX, you need to create an `nnx.Optimizer` object to manage the model's parameters and apply gradients during training. `nnx.Optimizer` receives the model's reference, so that it can update its parameters, and an [Optax](https://optax.readthedocs.io/) optimizer to define the update rules. Additionally, we'll define an `nnx.MultiMetric` object to keep track of the `Accuracy` and the `Average` loss."
    ]
   },
   {
@@ -210,7 +202,7 @@
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<treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_33615ade013b43ab98bc0441b27d9a9f\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -222,7 +214,7 @@
     {
      "data": {
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llWpSr1v1/Qsg+VSxGPKSO/1n2jGKsuYZtTKdJnAl535I3jU4B5L7RzE5PUtbrLvalHVX27Du6hasu1rNurxy1fc1rKuqXuOdTDjqEcV8ygmlX+21HIG/U9qtzPT9dT9/GoP1BkxqpKhxqp35keLTDLSP49oZoCfNUoccLLdlfeluGCXhxTl0vHG4EDwVf5gK+Ws4yFAH5TEq9XOsDiLKvd+VaD41qDweUew+1NDPFGdLfNUe3fg1pr5SaeU6qbhMtbLmVPNVVHg7HNBIFbFjkMh3XKuzKf+Rb8zW/MSVzx7UTzm9ySbUVaPaKkgxc/5QbYCvu5xXLV117s/rTJKXWdde833+kGEL/7x2HKC2ub+1Ozzf3d/cGd5aX872xKgTIC+DZVeO5Ut9Y0KZkF+uSKhetgrc3VAHGX/baxmtZsg3tKstx5Ihcsu93LzdZE73+n2uQKoN8N8WJFccb6qgq9y7MtfdioW7FVN2DXNM7NH+ptXRyiQfPpUH+J5V5/LWOsPz9TJ5SKhDdCazjibcRs6yw0fn9YM66yplju0NWmRFC+/J6qgWliXNfq1xwjzpON0Byvs3zROUNbeuGoDBpmyMwNp5i5LE04O6hVAfiL9YPVDvN0uYmxDVfFJTR6QEZmOT2dhTcVXhX63Gz4exauo35LHqqYDReSEPLb2vrZ/kWkGGWz7tRde+1gwESK8U56OSX8dtoSscjQeaNpwPAMbv1fqN8dz4zW+q1z30Onay1z0Ijbllix3uT1rrdpqMajLY/TVTpqK3fBaKV8nFLK3L3k2VqlwjKmQL9JuntcYtFRz5tNJzFFqhEOm4vVKtKWy6+FYjQr0chwc1RU0uF7fWYRs/dd60q6FXs8E7vex7a8sOXVMrXZOW5BlQGfYwbbQ6fc/Cc+NnAPrWm/6l4+vGcNuQnYOSHZ37ERq5tfKqzxvGk/auDW0cuOwX25sYUcr+WSGycoh6elDZNL3y2iutl6ulVbLzUpfVy1Wyqv24XCOnl6uktFdGLztl9LJfxiSTpIR2c2m0KnGneG4sLpr6etkUystVQvmkN4e+yVr/0zGEj6eTmfjP3Cgh+ysQl+kieSN6FvP7KL/ic4m8/PsFX4i12P+eKFVrcC4XeAf3OsU+WTmx5ZXt2nOSLaSXR8X2pEq22zH6rcMp335Rl0FybPz+91JNlXsTVqSojat9Sfqm1K55lPTOowTnUZxHV8yjh3c3jz7ZbPIkPZMnwcnzk548D+9g8lT/1p1hVUQVkWp+iuFMvCu+6/uVai/khN7l5BjlS9lFEfo8bRWlLb1kcsvJes9Y3XPXWqRtegRLTmRbE76Xy1oy3sFV3zEHZSfXIhIp54NywBQTmYzyQfY7TtjXU131pNIZIvc5y/A1Mn3+VT49rHfkysdfxVE6Bswn+nB7qE1RNe92LVVHNJy6L3O1FlYcnFteTNOaz/vOPCvF4nBGpPL/QwfdyJ1i5GXTXeKK3v6W7hst0bXuRZHSLt/n8aZasRQmuv+m4r4c5w8zY2lvr13xVYtLJY4qrLF+mM+dekl0Mb1Y6vgq+lOZRv2qp2vWr/r5u4Lefuu4a7KIGmG86gmf5eVubdOv4pBJAk871nyKjiLf5/0jS3RYLRbpw2WHWNfH3/sXEeh5dRbCz04Rubq5iFxtJCJr9dWmjNQTrBSSdg1vJiRawk9HSIo4a03/6K7R6ebczYtTOS2PVy5QViExpT+/Fieo2oIlXZez6BUYnFHvMmA0eTsY6bEUJzNFtbYu11xRyTLenr6irCXLVvyKBb6KtArgWa5GDmqBUQf7najlSuQGuDI08NcqMrCy5ovYwD3Y6YLPlnKz+XeLyWnmAEiTOYwBcR99mLi+XyRzsUivhoPJOT8VewshpX8yO5UhXtSeG2BSNBhtQGBvrwhAuvdLkpxLAmTDhDLa2J4KhLwEzUSlZPPLQcHurDl0Vv8U8mk4fMsXw0a+Umv+7H19mfh6flmcCqxTKltiM1IZeg+tIjytOkggeQbqwaAQliz9xo1UR9f4pOIkS+Z0FXXQXnxuMj2P8fsnyKus9WfvWwP/tQrgOLbFuRxnob5FhXvo/SWZ18hdbkSutYthOv0TD8S0vrkjX1VSz79vHH3I5xa5/n4O/ULhKJeaWjqfyp/a+rl6UkiQDH71WnYkyf15zZ9Wx8oiJX+pom1LPHNMoAa1mMmdqaQMZ6GPakPRQLFGhm0jall2aI4dObi2G3EkJS57oXeJUXEc5fr+9wyp+jROVa8eNbs4Ue8RRcjmTpZN6+LY3wlzq6XW/5rtlbdURoiY5u9A3j57P5Hyp8SvO10+ntRqu64kTdW18lB2FE56QV9lg3xhRKgXXUWRuC9lBHbJs9qIYHQKei7lpUXbmrL092UPy3YX1V9e3/teqg8sUtqouUhSIC+HzT3fjMTpoJN2x7icS9RCDvud2Sy0+WNjieuS1D3j5pK/tQS3FeuVItwhnDpPNpDOJ7oGDaL6pxuLRJV8be+qoW4gQi3soBr9+5EKKViFA2N/sTY0kO3dj6mJbimUe4mSSinEmlQ2RLne+yvKNx0iOijUJuMM571Cel6rx648aSUfKdrXo74dgMrx8mVyKZb9GvytbIQqg7G6UORPk2U6Bo0FNN1ZnEhW5tcwlJrTNu6hHC2Lcrhym2huBWW7il/nhWoIfmMloVb0TaotryUZ1KI4N/LKaQx/+u/ZZ++rPnJ99FNjA4+8AKNVtL5M1c0ZtV6ZJc69qMYgu0xj0HhbbMVpcylHyPaI9b/mlzq7SQNhmYp5fW2jmxUZO7Mk67g2ODYGDT5lcnNDPmWJSz6pe1EGjZf9bMoR+thUvO5lU47QZFP5uG1hSX2VmeGZjK6yzCeIaoZYxd+MaJt3oXScwptXMN7Ii0rUdRXibaop7C1eAMIYqnoq0uxR5fpoyFY/4hN9pbp/23IeYqJJSeveNb9Qj/+/2eQw9vxBBm6QA5GA4bKQjd0upjT71U0Stwdd6cWZAoU7HHJzitX4UzwZq410g1L/OM8OxA1aZ9dlqG8YauVBfhnUZmyrM/VEBqGprxDoNfhunk/jWvnLvDcpeqJI1AdOnXgpUWXc8Pp5GalOtQeCDYeBKnHnQFB7nfX0Ael4ozr5gHa8yrv3gM+u2i+LWnUcqKqTEOnLNF1MApjMhwPVwrv1pm044uJE3rR1dw6+nGLnzN1AWW3UF1jFaP4/E3l51l6TSP66YkD3+8FTZc+rk8lxMmr7Mcprmno7WFsJ24ghBeGMI3+GgUrWs3g62G+WoTGR310Z5GDULoOMh9VA2s5d1zY/GoQ29ta1KeUaWHmJ0x36oQuafSpmiaC752ph1i+/Vfe6wcTRGOmkA0hunFK+ftleMA99Ka/0msxOX00nUJgftEPBtSWfGZhFPxTs+ux9QSk3VfZK0sr3Aoz6qXNFqPAB1HT+np0gpYpbuOlqafQlnhxlrGyEhgFVlroDv+5r7TaRJLfrB7VlLZSem/OwQWoNM7MlnDJ9IX2HFeuyJ43gW+uK29Dz9dJnjoqhclKMDPVKOt+He+bYlvoXGVNx3ggScZsaGrqc5P7blrBUTMilRR6cCs/qcfY69qLcmLSpO06au0o0OY8vpkXlC9qrJLmyb9fJcU60TCC386zlZ600+qmpOH1e3dxRYKkq7xaZqV/ahRxpMu9KBY+rRPBDS6P4+rw6PVumUi+qdOqnlrK4VbKdNHtTpc1+1xJfd9zPsmrZoSYHyvW7Z2gsqQ9HqxYcWu6u7EJM5YErM5BLKzX68FPzb/XQrw2dOgfrQ+ciXw1aR6xcQahTy504NXLZk4redeNUupyYgJpUX9SWoWW1ryWT2os5TDNCvv96kZy/zjXT7mOE2u4AGe0k+v4yC5+Rr4erp8N3k1mUvBtH4i1YGCpXhaQF5+/BkbddaZt7mtkQ41k9K/lzXXb6/T/zEuFl9PPFMj3PfICNjHqp9t+4Ey6X31/+RZl96pKSxTI7zz9s6tAaH+qpGnXXCZJmdWRqLfkXjUqMjN/VrtQ4aAU4ud2SbncIzK1o8os0GfQ0k1qzlHJYr2F9E9oXnS3Z16nWLDhDH1MZ1gJX1TUsbQktK987noZnNblt9ph8U4qIJnqg4VyszmXyb+Xb4U/DhVAOZXVL6WfvewTvOprLIajhFpnmZn0rfyPLO48a03ABhGfKaNwtk9dmo67xoD5bNSpe81sUX7KM246HvlyvW0Zkv9NCr+SKwa0MIDeNsnXmGvksmhM8LlBX1/mJ1nLLcJFMp19qOtl7Nel25SCvM1Ql2DUCccbfTuRFrgMZYonP0sF1M4+6Ia3y+WaWJn+biHfD9x3JgeY0Cd/Ak5ngIEQVwWv90Hubn+fJxVJJhuRp04dWBD+S+0yBc/UNp9K1lVXuv3aN6seP2phWpOw4AV2ao1m3zC/rVY7fZCZqESQbVlwfYl8w6PI05MnbRpjEcvNRWbNNMlWM7i9ethataelrzE21aVYdiO5ZQ2vlkTa0l/VZqAMf67OQzaEviHVHntTy7mfwipA5o/0NBKHJ6ppzAlQjwHCa76TGJKJioayknIlm9vhpSaA78R8LL0Ejdf68K7kKKVZ1B+jp9XIcGqbx+ecay+rITxvImelVK7FuIDa4lW99GGjxbhs4uXXcsBw3WzrWq1VUvSE7HcZbZzE7S7BR1k/7su6rrMaR656W+rFsqT8WxvmKpvqxbKoSu95Wf+yw5htlU/12dVsV68W3bKwfb9NY7eFli7b6cYu2qhbHB71HQDaavpJMI2jOXhtOMRtNMGsLovx06+bQH4qjBjecSYv07em0fSpGItx8cjypZsUs0/1mRNWTt+0IsKtL0R3Zo3SA6jPP4JV8IaLn0lnekMY18ZCblWmFO+mCzSYrQ78xq/tIkX6iah1Peg5itZI17wXojhCykay+OxNi2iWrxfjIpynk23ZsRWKa8h+liZGlHY2zJ1WpZOo8vPxXIuYgOE2XaHUXdUtpH8m7F6r3Ge3GvTcrNf06T9qIcl97o7i5aQCzhsT7E5R7uqHJXsanqyfUCO03D0K3S3RosHW12zswWFP+tFxf9BRXRrNphbPRi6v9fKqfYOw66tdbjxfG3tqKPF1XkcO+ikxmW1Vkb31FOhYz6yQ2t4HbX2629NI2Kt83PbmqY4Yq7Y91v5904RrPGjL8RWPMGzY4pGFrftzM2VzL7L+ayz13mVuXLVu1V0e42jlfLidvxfPsApdrbU2saxfFqgbsdGC0AtZmjqrvZuKr/NqeuwtXq0cXqgca7dj8mjkvcqTiSQde6S6uo2YP9/XwTO0QepsH0WuF0dOD4LXD5rXeZ0VIm7vF+gPjteMiqQslsnsb8slfxpoAKZFbIp7XLloCycln3cbJ2PJg0Q12kfYe4SEhnw/6sKrTOyvRzpXHMWvzgTl2bXHei1vTWCczGb1jT7ecW+sK3UdNupGlchxP1WHgwdtMpHtxV5826uBY7zmKJqasVWmZD+jYHmzk313nvu6r80UqM1SNNL9sHNl4supsWHnIS+sEK+WjjtInHHWcng3ITxqLzn+R00IrjLl884dpyxbfUOL7al2QrStH+aNm3Wl973ILrfPwiyGd8rILgKlZLmcStc8LZqKiskWkgWtx/lMv/ZWbt1vYhXNlz15V5sqKJWQVnrb9PRSzemCddtaavJMuRN1cak4I1015yGaAT0cksvr+alKR+3p+dZEoJn5dKvq2LJVt2Y7hn5/xfZ9fFtDQiZ63H2V6XEc4/eddDyX29f6T65HSwtKziVIBfkiS9M9JJIaj8VmyTMHajGfLceGAKiIywtf9F89Ac57M08MXz9KFEMsQpoC9xcVs70wsxOELucHdUDusDnbiZBrJ2zxOZkB55/CF4tPhC7WwZEjN4WAnPBPhG6jDTmeakzQ5PZ3KpM9UIp28ivZxwoMArOCdw8+n6X799WCWpOrl4PBnfjlWTDCg8IChkZE2+qxCLFDy4PFDi+6Oesga//rn/zLHpvH//q/89+hf//w/6pbff/3zf8PfY+MXsUieW3RNlsXr4k/Oo3o9Fba4hCeRiHb0d/KUOIhhdBJKsQLRWPkeXsq9n/m7oklPSmFsJG7crbFz+EMxv2fiMB6Pi3J3ioKSk7yhQfink1B1hGdJmIp0bwlp+PnOYXlhVtaZlERmv6RA7pcOkVhdErBSYPcztDGU4XWSzIpLNXIS6Tx3OcixVrtKZDhIxflcKiuSjlgsgGGgFsrjJpUCm92J8O+vv/vzWJnPQ0lwnJ8Kb9LL6j4YaSMFUJNJyltFDL2XjbtuxMiKW12nIUeKNV2wqEvRjFqVdla0y8/LBOTn/Y7cEbnz3Nj5KnO6lWF1wGaQrkKwkg2ulv9HY+OPUm19lj2XG7myuxCUm3Fn19ip3YAgKb58OHBQFl9d/CBLn48J8kXjkgVVt//xhy83/CjatcgPkPz9DjfhDwEDaqc68AtPjt7vyLMOMgfAgLdqhFA/AcswnxtZidRJB3gMI9AOdNMd+fz6GH5cadTkk8ZJqeKxHj1JlamoeRVnSWYFOoDMSA52O+f8svyeO2nhd2Yd7pQhrUuUMrJ1+aS41lmW4sh3stMElrpvllgs+8mI+um48MaRH/XTg6+uvDk3++l7gGzKK/6ya3lN+dvy4R+S3cJL5ANX3iNpZw8oPKCmBQ/87L5bJs81yByolWGYVJ5sUP9kmTgyuS+vHTRN9YDI8w+uqZ7KB64tiyxz8lQKS36lVvlbEvNZiS5zdGSlHXWjrgXUmKRIyPGxFAVt5z+wyM1kr8+BnslL0Wr5pYE7RRolNFKid6SDZyXakY6igpGoDgFCU4lidQ2YErfiSSl+63I5Xl+Q/54ZMOrqeNnL6+Pr+kCYD3rwFaa41rTamoMb2sOqCfcfxldqY9Bz49X3fzXMnNjnp+l+dy5ZAeREq/RJGIAzt8FzQy5z7RiTSKo5i5NyXI58yw4IDV3X95hnE8/3OHEE8yPqEdelefHuSbkyFJqINlGyOl6Fybk0V05WaSnqHaBP+XwpXypdraNNGjR+26mvPSy9biUb+kXontVAxbXhkTmWFwzc6lPYRjcnAeNeZiopoc3bZ9TDl9t1NJswM/K4MB3fZTan3BO2TWI/8AWhxBf329HQikErBq0YtGLQikErBq0YtGIemRXDo9iijPPYFhZzbOqZXuB7oeeEoWmGkY1WDFoxaMXcQUezoG0iR5jUtF3mhxSMF9v2maC2z2kUWo/WinHYraTdYWjFoBWDVszDs2IeksF1nxbXFp+Du7XOoKid1pnD0DpD6+yDWWegAWjWGYgfWmcbKY0iCk1K3ShgoCpG3PUdSlwWiUAElHucoHX2cPVVtM4eFikYhT+AmRi4tmfFVhy7lDLLdj1G3NAUQUAp514s0ExEMxHNRDQT0UxEMxHNRDQT0UxEM/GE2MK0ScxYEFmMA4Sx5dnEiajJI5fjIh6aiWgmPiozkcUOozyIQivwGLO47/oWpcJyhAg4sX3cE4l7ItFMRDMR90Tinkg0p3BPJO6J3MacCiw39jyHEI8zZjLuBPJvGHPP4YShOYV7InFP5F10NJ+zkFqCxSwKGBG2Z8I32w4DLizi2OHjtWJg0oRZBf6lt5F66nlozqA5g+YMrnohICDcMdzS5fBytdvh5Wq3xMuVLosHQ7vTtWJJ14pkLoUv0hrf4aT8Jv0ujr/S8WK1HS9Wv+OFWh2Olxo12qZGoXzdxIgyfcu0pJ2WQEl6PEB62h73UV9adBWhq+ieXEUNT9HaPHaNdZmQD5EJbWVC9EzIHWRitTNp5EIJetY2M/gp2PfM9k0/YB6zbeb50uJ3iRk7gaA2/fUN/kfkUfvQPoaP2rUGcAdetdttLmhROT7efVI+xALeqICyhE/KEiIPsZGRhw+0kT/M1ikR2ILHnITUp4yxyHcDlzoRcwIa+TxgeMIGT9jgWgOuNeBaA56wwRM26OfDEzbouDrxWWRZPnMtLlwWM+Exx4xZwJlnW7HAQAx4wgZP2DyuEzaeHwovEMQNmcmE7QaOyc1AOKZPIhbbziPfmyZ3DzvsNv2PeCcMj9ugzYg2I9qMCAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAifONzqrOzLreP7vdw+nuCGtHrhVy7Xg6PVy0jk13a0+kUS+YV9G/s29m2UVezb2Lexb9+S1vr42MQv42PLwCNZFBqXrY5D0x3Tui9CNrF+1ajWV9vF+e6LpkN8jJGNsXMwRvaaGNlOI3q1cy9Bsn09F9vGUEObBR6xTGGbnAeO4xBGhe15lmn7MbGcIPYtGmGM7Ace6+Qhxckejxuhd6q4rXeNJ1/dH/WVeP2xcbH+n0T9V8SXRgnA+mMPQAnA+j/2HvCBws9TNwQjnNkBixm3mGcKx7dDQbzQtyyfPtq4gtR2bhXPE9JjMEEMJojBBDGYIMInAx/LhQAbXZz6cV06ABNm32qPj9cO4NLJh7t2oLEWQDwXFwM2sxa4E1Bf+IHv2CZjnggCwfzI4l7kRiEjLt478JANFbx5AEkhKST1KZP6MF43asVmaNux7VDCYt/irukJL/S5y2zH5NHjvc2DKK3VdvpmtD0jTRJjyhenQjojlhfn53wx+UWM0dmGzjZ0tqGzDQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQHhRnA3l0q83OJyB8RFXMRFXMRFXMRFXMR9dLjbAPIMcREXcREXcREXcREXcRufzstgibw/c9fIboW1rJXXwpL2tbBkxbWwQK19K+zaW2blBVM918yS7JpZvFQWL5W9+0tl7calspbprr9VdtdYlxFpZUQcPSPKTLy9drNb92zuh5bnCDcMHRaGsRdR5nkmix3TIUHo4+21D/DCv4/60lrt1trxWLt08ri8dfIuseSL+6C7EuvDXJrpCyosFvsx4SHzLcFNFnqeG7rc9ZhHnEd7aSYxb3UBNDHx7ky8OxPvzrzV3ZkHB7fbo5Glb9lP0mwiK62mbjOHmD1GjtlhNKHVg1bP/Vg9xNSNEYK2yIbKDGcOE47wiOtZLIq455jc8pkdhyT07cDrVGba2kapVgyPNrvqvKWqddVpa82MW8IkoRO4sWDMIZzbsRlbVLie50Nve7SaGdg1cgC6jXZGT2wHFTRU0FBBw8vNERAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQHgfcdoMZfvCDn4/iowHyAz/4+fg+PRuytQOtajvrtgdat9qavXajd+/xWGLhcVbc2H1fG7udxnFW4t3TaVbcQH7jw6wOSDQjxPIYI6Ytz8TZzA8j4bLQNSkeZn3w273xaOuncrTVihhzuBP5JgmZGzge/GdZIg5cEVsuCR7tAQqL3upoq0Xx5ASenMCTEw/v5MR9ejvv9qhtVqKO+EQUj9qiRfbBLDKYKfUAQxQtpc2UK1Cs3IBEthuFJgstxxMhie3YZDFlJrXQUnrAet1HbSJtdiJ75aewUW5OAsY9BR/Aigl96FCMEeFQi7EoCKjtE24T0xPCpMRCKwatGLRi0IpBKwatGLRi0IpBK2YL5Sp21epOYNmRzyzf8SLfNoMw9qnvmTZFKwatGLRi7qKjCcuKSGD6lhNbjLuWzzzLdCLKqRvbYOA8WivGYbeSdoehFYNWDFoxGMXqE9glf8fhXKGondaZw9A6Q+vsw+36Y7p1BuKH1tlGSiOz7YD5nhWK2GaWF3NbEJvaIjZtxzM5Qevs4eqraJ09LFIfZsteQGkUg3noctNmLqEgM0EYOl5IQ9/3IopmIpqJaCaimYhmIpqJaCaimYhmIpqJJ6HnB0RYNOS+zZhPAyuOWSzMyI1t4UUmmoloJqKZ+JjMROZ6tktDM6AhY4KY3PQ9S1gOoYHv+JTjnkjcE4lmIpqJuCcS90SiOYV7InFP5DZ7ImPfdW3TAwEPmWM6gc8JY7ZjEpv4jOKF7rgnEvdE3snV60Fsu6HjxJZjQUfzAhO6HjGZHRKPuCx6vFYMTJq7MiCY5Owtor54HpozaM6gOYOrXggICHcMt3Q5vFztdni5JtzvSpfFg6Hd6VqxinCoFL5IazyLkJp9M1WgyJWOF6vteLH6HS/UWh0ilbapUShfT4BUZfquCdZq9XiA9LQ97qO+tOgqQlfRPbmKGp6ie4nJeg+Z0Hbg10bc1zvIxGpn0siFEvSsbRhdlgbQP+zA9s2IOXEUkCAgsRd4HvVt2/wIli0fkUftQ/sYPu64skdHd+BVu93mghaV4+PdJ+VDLOCNCihLWIbcRR5iIyMPH2ojf6Cg2CQ2eeQLboYxs2zGPdP1rYj5wosiN+Z4wgZP2OBaA6414FoDnrDBEzbo58MTNui4OqHMdxweEssklFE/9qkXUpvDew++hzFuCcMTNnjC5jGdsBEmsUIvdCJix8yOacBMJxQO9V1fBCanj3xvmtw97LDb9D/inTA8boM2I9qMaDMiICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIHzicKuzsi+3ju/3cvt4ghvS6oVfuVwPjlYvI5Ff29HqF0nkF/Zt7NvYt1FWsW9j38a+fUta6+NjE7+Mjy0Dj2RRaFy2Og5Nd0zrvgjZxPpVo1pfbRfnuy+aDvExRjbGzsEY2WtiZDuN6NXOvQTJ9vVcbBtDDW0YaijiwvEE8S3LZkHk8ziMLdeNbT+mjLkBxsh+4LFOHlKc7PG4EXqnitt613jy1f1RX4nXHxsX6/9J1H9FfGmUAKw/9gCUAKz/Y+8BHyauoO8RL3RjEblRyCw79pnwQzfyrcCPAk4f75231HZuFc8T0mMwQQwmiMEEMZggwicDH8uFABtdnPpxXToAE2bfao+P1w7g0smHu3agsRZAPBcXAza0FggPvMgOXJ9EjFhhEEV+EJrUJzSK7YDgvQMP2VDBmweQFJJCUp8yqQ/jdaOhT2PmU5PEFgtsi8d2yJwwZKbHKCh2j/c2D6K0Vtvpm9H2jDRJjClfnArpjFhenJ/zxeQXMUZnGzrb0NmGzjYEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEhBvB3Vwq8XKLyx0QF3ERF3ERF3ERF3ER99HhbgPIM8RFXMRFXMRFXMRFXMRtfDovgyXy/sxdI7sV1rJWXgtL2tfCkhXXwgK19q2wa2+ZlRdM9VwzS7JrZvFSWbxU9u4vlbUbl8paprv+VtldY11GpJURcfSMKDPx9trNbt1zLC5MEN2Imj5zHc8PQo+ajIYk8i0ibLy99gFe+PdRX1qr3Vo7HmuXTh6Xt07eJZZ8cR90V2J9mEszvcByPNeOhBP5LPJNjwrbpI4f+bEd2dx9tJdmEvNWF0ATE+/OxLsz8e7MW92deXBwuz0aWfqW/STNJrLSauo2c4jZY+SYHUYTWj1o9dyP1UNM3RghaItsqMxwy/c9EVuCBhETZhg4VAQhC6nrOi6lpFOZaWsbpVoxPNrsqvOWqtZVp20rEwUkpD6x3ZBGjJHQZ2boO3EEepopaOQ/Ws0M7Bo5AN1GO6MntoMKGipoqKDh5eYICAgICAgICAgICAgICAgICAgICAgICAgICAiPA267wQw/+MHPR/HRAPmBH/x8fJ+eDdnagVa1nXXbA61bbc1eu9G793gssfA4K27svq+N3U7jOCvx7uk0K24gv+Gea2GZwokji1jCZQ4R3I85c30ndj3fM4mPh1kf/HZvPNr6oI+23nq7eyoPRaffRHJL+1G1Q9v1Ix5ahPuRGzLquX5ABXEsKmLX9ljMlF5TIFPLcoIAkGhsMhbHnsuI73LLjkzPjxjVkC2bWTxyXCpszhzBueUQl1k0DiLhU5dryJFv2QGhoev6HvNsAsMOJ45gfkQ94ro6ZZswM/K4MB3fZTan3BO2TWI/8AWhxBcaMo9iizLOY1tYzLGpZ3qB74WeE4amGUa2XmboM5Ejz/XbLvNDCgRt22eC2j6nUWhpyCIKTUrdKGCAEnEYLilUMBKBCKBI2fxUIgfATiu24tillFm26zHihqYIAko592K9zMQWJlSIsSCyoOich7EFTHEiavLI5XqZWewwygMoXeAxYLnv+halwnKECDixfZ1yYLmx5zmEeJwxk3EnkH/DmHsOJ0yn7HMWUkuAFEAdibA9E77ZdhhwYRHHDnXZgHfACNMPmMdsm3m+xHaJGTsB8E9vQRHYgsdcHvShjLHIdwOXOhFzAhr5PNClDphrWT5zLQ6TU8yExxwgDeX2bCsWDT57fii8QABzoXbCdgPH5GYgHNMnEYsz/bdqblPYJueB4ziEgZx6nmXafkxAymNgYqSXmbrADYfZARSCW8yDKdO3Q0G80Ify6RUEvoLwgEQ6NvQUD9pZyrLFvciNQmj6RreKzdC2oXSUMMiYu6YngCoH6Ybi68WwuR9aniPcMHRYGMZeRJnnmSAFpkOCUG9uX4AgAMmY8JD5luAmCz3PDV0OAugRnRvQS5lwBPQ3z2JRBBJhcuC7HYck9O3Aa8iGJUwSOoEbCwYqA+d2bEJPE67n+aA6N8rsgMLMCLFAQolpy8LY0L8iaNDQNRvjRgT0uBP5QJ5B+3nwn2WJOHBFbLkk0JAB0Q1IZANXoWqW44mQxFASFlNmUkunHIKwQWsQ4VALhA76nu0TbhPgNnR5opcZxj9ZwgAGN59ZvuNFvm0GYexT0Ihs2pBny4pIYPqWE0OHdYFrIEnQXTl1Yxsy1TusDSLke1YoYhgivZjbMMpQW8Sm7Xhmc9ygNIqBostNm7kEOnoQQMt7IQ19H9per6DnB0RYNOTAXubTAMYbFgszglIIL2oUw/Vsl4ZmQEPGBIFuAmWCYYNQkFqf6uNzHPuua5sgMtCxQNICH0YLKZ3EJj6jDakLYtsNHeCFA0MujLcmJCcms0PiyRFSlw0agE1jBzB0QP+Po4AEAYm9wPOob0Pv1GWDxNAffJDjMIZRlHHPhNEuYj7UDiqpI1PmOw4PiWUSCkWElgOu2Rzee/A9jPUWNIkVeqETETsGkacBM50QBAVGUxGYXOczjbgAUSPQ720Yo30ewwDtApN9EDuQWZ0bHowQ0EtUz7fsGKYTP3Rhwgv8KOCNQQbkMfAiO3DlcAUlCqLID2Ca8QmIAcyQejFAoGNoZpOA1AW2BXMcNE4II5/HKLX03r02Xo02iq6LjqGNG+tOn2oT/brTnXq3WmOWHO/nZ/WScJEkaedpveJAndSgcqQcvXGEbmdcKlkn8vVOK+mrKZhPMpP89ziUD1Q20q8hT9sVbxYCDK5Q/OckPRtqyUui8YKfnmcnEKE8F/LrOASNLRVf5T+/zjFkHQrsMeh5YN2+kupzSVi+TxbGcCpSYwL0zH3486JS+cZTMTtNz+Dp06cj470h8bKXgFxShp9/mAr59curb4B4kfpocgwZyDTL5GIRiq/Aruzl4W+lXbljPDUaybOfGltKauN4slgWeauaQYLqbXV68VpviU4mrzus+P8BVmo5Ag==</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_920e157205b44214a73cd99be5592f27\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
+       "<div style=\"display:none\"> <script type=\"application/octet-stream\" >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</script> 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@@ -265,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "14",
    "metadata": {},
    "outputs": [],
@@ -300,136 +292,124 @@
     "\n",
     "> **Note:** The code shows how to perform several in-place updates to the model, the optimizer, the RNG streams, and the metrics, but _state updates_ were not explicitly returned. This is because Flax NNX transformations respect _reference semantics_ for Flax NNX objects, and will propagate the state updates of the objects passed as input arguments. This is a key feature of Flax NNX that allows for a more concise and readable code. You can learn more in [Why Flax NNX](https://flax.readthedocs.io/en/latest/why.html).\n",
     "\n",
+    "## 6. Define test set inference functions\n",
     "\n",
-    "## 6. Train and evaluate the model\n",
-    "\n",
-    "Now, you can train the CNN model. Before the training loop, we use [`nnx.view`](https://flax.readthedocs.io/en/latest/guides/view.html) to create a `train_model` (with dropout enabled and batch norm in training mode) and an `eval_model` (with dropout disabled and batch norm using running statistics). These views share the same underlying weights, so updates during training are automatically reflected during evaluation."
+    "We'll also create a `jit`-compiled model inference function (with `nnx.jit`) - `pred_step` - to generate predictions on the test set using the learned model parameters. Before the training loop, we use [`nnx.view`](https://flax.readthedocs.io/en/latest/guides/view.html) to create a `train_model` (with dropout enabled and batch norm in training mode) and an `eval_model` (with dropout disabled and batch norm using running statistics). These views share the same underlying weights, so updates during training are automatically reflected during evaluation. We can use the `eval_model` for inference. This will enable us to visualize test images alongside their predicted labels for a qualitative assessment of model performance."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "22",
+   "execution_count": 6,
+   "id": "68c7ed78-bdd6-4d64-b2c6-faa390a2fb51",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from IPython.display import clear_output\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "metrics_history = {\n",
-    "  'train_loss': [],\n",
-    "  'train_accuracy': [],\n",
-    "  'test_loss': [],\n",
-    "  'test_accuracy': [],\n",
-    "}\n",
-    "\n",
-    "rngs = nnx.Rngs(0)\n",
     "train_model = nnx.view(model, deterministic=False, use_running_average=False)\n",
     "eval_model = nnx.view(model, deterministic=True, use_running_average=True)\n",
     "\n",
-    "for step, batch in enumerate(train_ds.as_numpy_iterator()):\n",
-    "  # Run the optimization for one step and make a stateful update to the following:\n",
-    "  # - The train state's model parameters\n",
-    "  # - The optimizer state\n",
-    "  # - The training loss and accuracy batch metrics\n",
-    "  train_step(train_model, optimizer, metrics, rngs, batch)\n",
-    "\n",
-    "  if step > 0 and (step % eval_every == 0 or step == train_steps - 1):  # One training epoch has passed.\n",
-    "    # Log the training metrics.\n",
-    "    for metric, value in metrics.compute().items():  # Compute the metrics.\n",
-    "      metrics_history[f'train_{metric}'].append(value)  # Record the metrics.\n",
-    "    metrics.reset()  # Reset the metrics for the test set.\n",
-    "\n",
-    "    # Compute the metrics on the test set after each training epoch.\n",
-    "    for test_batch in test_ds.as_numpy_iterator():\n",
-    "      eval_step(eval_model, metrics, test_batch)\n",
-    "\n",
-    "    # Log the test metrics.\n",
-    "    for metric, value in metrics.compute().items():\n",
-    "      metrics_history[f'test_{metric}'].append(value)\n",
-    "    metrics.reset()  # Reset the metrics for the next training epoch.\n",
-    "\n",
-    "    clear_output(wait=True)\n",
-    "    # Plot loss and accuracy in subplots\n",
-    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
-    "    ax1.set_title('Loss')\n",
-    "    ax2.set_title('Accuracy')\n",
-    "    for dataset in ('train', 'test'):\n",
-    "      ax1.plot(metrics_history[f'{dataset}_loss'], label=f'{dataset}_loss')\n",
-    "      ax2.plot(metrics_history[f'{dataset}_accuracy'], label=f'{dataset}_accuracy')\n",
-    "    ax1.legend()\n",
-    "    ax2.legend()\n",
-    "    plt.show()"
+    "@nnx.jit\n",
+    "def pred_step(model: CNN, batch):\n",
+    "  logits = model(batch['image'], None)\n",
+    "  return logits.argmax(axis=1)\n",
+    "\n",
+    "def plot_predictions(test_batch, pred):\n",
+    "    fig, axs = plt.subplots(5, 5, figsize=(6, 6))\n",
+    "    for i, ax in enumerate(axs.flatten()):\n",
+    "      ax.imshow(test_batch['image'][i, ..., 0], cmap='binary')\n",
+    "      # ax.set_title(f'label={pred[i]}')\n",
+    "      color = 'green' if test_batch['label'][i] == pred[i] else 'red'\n",
+    "      ax.text(0.05, 0.05, str(pred[i]), transform=ax.transAxes, color=color)\n",
+    "      ax.axis('off')\n",
+    "    return fig"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "25",
+   "id": "7c0371b5-f820-4e88-8f96-26efafea72c2",
    "metadata": {},
    "source": [
-    "## 7. Perform inference on the test set\n",
+    "## 7. Train and evaluate the model\n",
     "\n",
-    "Create a `jit`-compiled model inference function (with `nnx.jit`) - `pred_step` - to generate predictions on the test set using the learned model parameters. Since we already have `eval_model` (an `nnx.view` with `deterministic=True` and `use_running_average=True`), we can use it directly for inference. This will enable you to visualize test images alongside their predicted labels for a qualitative assessment of model performance."
+    "Now, we can train the CNN model. We'll also set up [TensorBoard](https://www.tensorflow.org/tensorboard) logging. TensorBoard is a visualization toolkit that displays interactive charts of metrics — like loss and accuracy — as training progresses, letting us monitor convergence and compare runs. We use the `tensorboardX` library, which provides a TensorBoard-compatible `SummaryWriter` interface without requiring TensorFlow as a dependency. Each call to `writer.add_scalar` records a scalar value (e.g. `train_loss`) at a given training step; TensorBoard reads these from the `runs/` directory and plots them in real time. We also use `writer.add_figure` to visualize the plots created with our `plot_predictions` function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "26",
+   "execution_count": 7,
+   "id": "7b945a9a-8d5c-45b3-a984-4573863c0c37",
    "metadata": {},
    "outputs": [],
    "source": [
-    "@nnx.jit\n",
-    "def pred_step(model: CNN, batch):\n",
-    "  logits = model(batch['image'], None)\n",
-    "  return logits.argmax(axis=1)"
+    "from tensorboardX import SummaryWriter\n",
+    "import tensorboard\n",
+    "writer = SummaryWriter()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1d6cb81f",
+   "id": "b2ad03f3-abf6-4e3d-9f7f-ce973fef83ef",
    "metadata": {},
    "source": [
-    "We reuse the `eval_model` view created earlier so that `Dropout` is disabled and `BatchNorm` uses stored running stats during inference."
+    "You can open tensorboard in a separate browser window at `localhost:6006`, but there's also a Jupyter extension to view its progress from with a notebook"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "27",
+   "execution_count": 8,
+   "id": "0651b72c-3fe7-403a-b4cf-ad0c42952aad",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1200 with 25 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "# %load_ext tensorboard\n",
+    "# %tensorboard --logdir runs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bff6756-ac92-41bf-83b4-800a23f3ac6d",
+   "metadata": {},
+   "source": [
+    "![](./tensorboard_screenshot.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "22",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "test_batch = test_ds.as_numpy_iterator().next()\n",
-    "pred = pred_step(eval_model, test_batch)\n",
-    "\n",
-    "fig, axs = plt.subplots(5, 5, figsize=(12, 12))\n",
-    "for i, ax in enumerate(axs.flatten()):\n",
-    "  ax.imshow(test_batch['image'][i, ..., 0], cmap='gray')\n",
-    "  ax.set_title(f'label={pred[i]}')\n",
-    "  ax.axis('off')"
+    "rngs = nnx.Rngs(0)\n",
+    "\n",
+    "for step, batch in enumerate(make_batches(train_ds, batch_size)):\n",
+    "  if step >= train_steps:\n",
+    "    break\n",
+    "  # Run the optimization for one step and make a stateful update to the following:\n",
+    "  # - The train state's model parameters\n",
+    "  # - The optimizer state\n",
+    "  # - The training loss and accuracy batch metrics\n",
+    "  train_step(train_model, optimizer, metrics, rngs, batch)\n",
+    "\n",
+    "  if step > 0 and (step % eval_every == 0 or step == train_steps - 1):  # Evaluation period passed.\n",
+    "    # Log the training metrics.\n",
+    "    for metric, value in metrics.compute().items():  # Compute the metrics.\n",
+    "      writer.add_scalar(f'train_{metric}', value, step) # Record the metrics.\n",
+    "    metrics.reset()  # Reset the metrics for the test set.\n",
+    "\n",
+    "    # Compute the metrics on the test set after each training epoch.\n",
+    "    for test_batch in make_batches(test_ds, batch_size):\n",
+    "      eval_step(eval_model, metrics, test_batch)\n",
+    "\n",
+    "    # Show predicted labels on a single test batch\n",
+    "    pred = pred_step(eval_model, test_batch)\n",
+    "    fig = plot_predictions(test_batch, pred)\n",
+    "    writer.add_figure('inference', fig, step)\n",
+    "\n",
+    "    # Log the test metrics.\n",
+    "    for metric, value in metrics.compute().items():\n",
+    "      writer.add_scalar(f'test_{metric}', value, step) # Record the metrics.\n",
+    "\n",
+    "    metrics.reset()  # Reset the metrics for the next training epoch."
    ]
   },
   {
@@ -444,17 +424,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "49cace09",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from orbax.export import JaxModule, ExportManager, ServingConfig"
+    "# !pip install -U \"orbax-export[all]\"\n",
+    "from orbax.export import JaxModule, ExportManager, ServingConfig\n",
+    "import tensorflow as tf"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "421309d4",
    "metadata": {},
    "outputs": [],
@@ -475,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "9f2ad72e",
    "metadata": {},
    "outputs": [],
@@ -533,7 +515,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/docs_nnx/mnist_tutorial.md b/docs_nnx/mnist_tutorial.md
index cb04156ad..591810d9f 100644
--- a/docs_nnx/mnist_tutorial.md
+++ b/docs_nnx/mnist_tutorial.md
@@ -31,40 +31,32 @@ If `flax` is not installed in your Python environment, use `pip` to install the
 
 ## 2. Load the MNIST dataset
 
-First, you need to load the MNIST dataset and then prepare the training and testing sets via Tensorflow Datasets (TFDS). You normalize image values, shuffle the data and divide it into batches, and prefetch samples to enhance performance.
+First, we need to load the MNIST dataset and prepare the training and testing sets using the Hugging Face [`datasets`](https://huggingface.co/docs/datasets) package. We'll normalize the image values and shuffle the training data. The `make_batches` helper converts the dataset into batches of NumPy arrays with a channel dimension added, dropping any incomplete final batch.
 
 ```{code-cell} ipython3
-import tensorflow_datasets as tfds  # TFDS to download MNIST.
-import tensorflow as tf  # TensorFlow / `tf.data` operations.
-
-tf.random.set_seed(0)  # Set the random seed for reproducibility.
+import numpy as np
+import matplotlib.pyplot as plt
+from datasets import load_dataset
 
 train_steps = 1200
 eval_every = 200
 batch_size = 32
 
-train_ds: tf.data.Dataset = tfds.load('mnist', split='train')
-test_ds: tf.data.Dataset = tfds.load('mnist', split='test')
-
-train_ds = train_ds.map(
-  lambda sample: {
-    'image': tf.cast(sample['image'], tf.float32) / 255,
-    'label': sample['label'],
-  }
-)  # normalize train set
-test_ds = test_ds.map(
-  lambda sample: {
-    'image': tf.cast(sample['image'], tf.float32) / 255,
-    'label': sample['label'],
-  }
-)  # Normalize the test set.
-
-# Create a shuffled dataset by allocating a buffer size of 1024 to randomly draw elements from.
-train_ds = train_ds.repeat().shuffle(1024)
-# Group into batches of `batch_size` and skip incomplete batches, prefetch the next sample to improve latency.
-train_ds = train_ds.batch(batch_size, drop_remainder=True).take(train_steps).prefetch(1)
-# Group into batches of `batch_size` and skip incomplete batches, prefetch the next sample to improve latency.
-test_ds = test_ds.batch(batch_size, drop_remainder=True).prefetch(1)
+dataset = load_dataset('mnist')
+train_ds = dataset['train'].shuffle(seed=0)
+test_ds = dataset['test']
+
+def make_batches(ds, batch_size):
+  """Yield batches of normalized (image, label) numpy arrays."""
+  for i in range(0, len(ds), batch_size):
+    batch = ds[i : i + batch_size]
+    if len(batch['label']) < batch_size:  # drop incomplete final batch
+      break
+    images = np.stack([
+      np.array(img, dtype=np.float32)[..., None] / 255.0
+      for img in batch['image']
+    ])
+    yield {'image': images, 'label': np.array(batch['label'])}
 ```
 
 ## 3. Define the model with Flax NNX
@@ -106,7 +98,7 @@ nnx.display(model)
 
 ### Run the model
 
-Let's put the CNN model to the test!  Here, you’ll perform a forward pass with arbitrary data and print the results.
+Let's put the CNN model to the test!  Here, we’ll perform a forward pass with arbitrary data and print the results.
 
 ```{code-cell} ipython3
 import jax.numpy as jnp  # JAX NumPy
@@ -117,7 +109,7 @@ y
 
 ## 4. Create the optimizer and define some metrics
 
-In Flax NNX, you need to create an `nnx.Optimizer` object to manage the model's parameters and apply gradients during training. `nnx.Optimizer` receives the model's reference, so that it can update its parameters, and an [Optax](https://optax.readthedocs.io/) optimizer to define the update rules. Additionally, you will define an `nnx.MultiMetric` object to keep track of the `Accuracy` and the `Average` loss.
+In Flax NNX, you need to create an `nnx.Optimizer` object to manage the model's parameters and apply gradients during training. `nnx.Optimizer` receives the model's reference, so that it can update its parameters, and an [Optax](https://optax.readthedocs.io/) optimizer to define the update rules. Additionally, we'll define an `nnx.MultiMetric` object to keep track of the `Accuracy` and the `Average` loss.
 
 ```{code-cell} ipython3
 import optax
@@ -170,83 +162,81 @@ In the code above, the [`nnx.jit`](https://flax.readthedocs.io/en/latest/api_ref
 
 > **Note:** The code shows how to perform several in-place updates to the model, the optimizer, the RNG streams, and the metrics, but _state updates_ were not explicitly returned. This is because Flax NNX transformations respect _reference semantics_ for Flax NNX objects, and will propagate the state updates of the objects passed as input arguments. This is a key feature of Flax NNX that allows for a more concise and readable code. You can learn more in [Why Flax NNX](https://flax.readthedocs.io/en/latest/why.html).
 
+## 6. Define test set inference functions
+
+We'll also create a `jit`-compiled model inference function (with `nnx.jit`) - `pred_step` - to generate predictions on the test set using the learned model parameters. Before the training loop, we use [`nnx.view`](https://flax.readthedocs.io/en/latest/guides/view.html) to create a `train_model` (with dropout enabled and batch norm in training mode) and an `eval_model` (with dropout disabled and batch norm using running statistics). These views share the same underlying weights, so updates during training are automatically reflected during evaluation. We can use the `eval_model` for inference. This will enable us to visualize test images alongside their predicted labels for a qualitative assessment of model performance.
+
+```{code-cell} ipython3
+train_model = nnx.view(model, deterministic=False, use_running_average=False)
+eval_model = nnx.view(model, deterministic=True, use_running_average=True)
+
+@nnx.jit
+def pred_step(model: CNN, batch):
+  logits = model(batch['image'], None)
+  return logits.argmax(axis=1)
+
+def plot_predictions(test_batch, pred):
+    fig, axs = plt.subplots(5, 5, figsize=(6, 6))
+    for i, ax in enumerate(axs.flatten()):
+      ax.imshow(test_batch['image'][i, ..., 0], cmap='binary')
+      # ax.set_title(f'label={pred[i]}')
+      color = 'green' if test_batch['label'][i] == pred[i] else 'red'
+      ax.text(0.05, 0.05, str(pred[i]), transform=ax.transAxes, color=color)
+      ax.axis('off')
+    return fig
+```
 
-## 6. Train and evaluate the model
+## 7. Train and evaluate the model
 
-Now, you can train the CNN model. Before the training loop, we use [`nnx.view`](https://flax.readthedocs.io/en/latest/guides/view.html) to create a `train_model` (with dropout enabled and batch norm in training mode) and an `eval_model` (with dropout disabled and batch norm using running statistics). These views share the same underlying weights, so updates during training are automatically reflected during evaluation.
+Now, we can train the CNN model. We'll also set up [TensorBoard](https://www.tensorflow.org/tensorboard) logging. TensorBoard is a visualization toolkit that displays interactive charts of metrics — like loss and accuracy — as training progresses, letting us monitor convergence and compare runs. We use the `tensorboardX` library, which provides a TensorBoard-compatible `SummaryWriter` interface without requiring TensorFlow as a dependency. Each call to `writer.add_scalar` records a scalar value (e.g. `train_loss`) at a given training step; TensorBoard reads these from the `runs/` directory and plots them in real time. We also use `writer.add_figure` to visualize the plots created with our `plot_predictions` function.
 
 ```{code-cell} ipython3
-from IPython.display import clear_output
-import matplotlib.pyplot as plt
+from tensorboardX import SummaryWriter
+import tensorboard
+writer = SummaryWriter()
+```
+
+You can open tensorboard in a separate browser window at `localhost:6006`, but there's also a Jupyter extension to view its progress from with a notebook
+
+```{code-cell} ipython3
+# %load_ext tensorboard
+# %tensorboard --logdir runs
+```
 
-metrics_history = {
-  'train_loss': [],
-  'train_accuracy': [],
-  'test_loss': [],
-  'test_accuracy': [],
-}
+![](./tensorboard_screenshot.png)
 
+```{code-cell} ipython3
 rngs = nnx.Rngs(0)
-train_model = nnx.view(model, deterministic=False, use_running_average=False)
-eval_model = nnx.view(model, deterministic=True, use_running_average=True)
 
-for step, batch in enumerate(train_ds.as_numpy_iterator()):
+for step, batch in enumerate(make_batches(train_ds, batch_size)):
+  if step >= train_steps:
+    break
   # Run the optimization for one step and make a stateful update to the following:
   # - The train state's model parameters
   # - The optimizer state
   # - The training loss and accuracy batch metrics
   train_step(train_model, optimizer, metrics, rngs, batch)
 
-  if step > 0 and (step % eval_every == 0 or step == train_steps - 1):  # One training epoch has passed.
+  if step > 0 and (step % eval_every == 0 or step == train_steps - 1):  # Evaluation period passed.
     # Log the training metrics.
     for metric, value in metrics.compute().items():  # Compute the metrics.
-      metrics_history[f'train_{metric}'].append(value)  # Record the metrics.
+      writer.add_scalar(f'train_{metric}', value, step) # Record the metrics.
     metrics.reset()  # Reset the metrics for the test set.
 
     # Compute the metrics on the test set after each training epoch.
-    for test_batch in test_ds.as_numpy_iterator():
+    for test_batch in make_batches(test_ds, batch_size):
       eval_step(eval_model, metrics, test_batch)
 
+    # Show predicted labels on a single test batch
+    pred = pred_step(eval_model, test_batch)
+    fig = plot_predictions(test_batch, pred)
+    writer.add_figure('inference', fig, step)
+
     # Log the test metrics.
     for metric, value in metrics.compute().items():
-      metrics_history[f'test_{metric}'].append(value)
-    metrics.reset()  # Reset the metrics for the next training epoch.
+      writer.add_scalar(f'test_{metric}', value, step) # Record the metrics.
 
-    clear_output(wait=True)
-    # Plot loss and accuracy in subplots
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
-    ax1.set_title('Loss')
-    ax2.set_title('Accuracy')
-    for dataset in ('train', 'test'):
-      ax1.plot(metrics_history[f'{dataset}_loss'], label=f'{dataset}_loss')
-      ax2.plot(metrics_history[f'{dataset}_accuracy'], label=f'{dataset}_accuracy')
-    ax1.legend()
-    ax2.legend()
-    plt.show()
-```
-
-## 7. Perform inference on the test set
-
-Create a `jit`-compiled model inference function (with `nnx.jit`) - `pred_step` - to generate predictions on the test set using the learned model parameters. Since we already have `eval_model` (an `nnx.view` with `deterministic=True` and `use_running_average=True`), we can use it directly for inference. This will enable you to visualize test images alongside their predicted labels for a qualitative assessment of model performance.
-
-```{code-cell} ipython3
-@nnx.jit
-def pred_step(model: CNN, batch):
-  logits = model(batch['image'], None)
-  return logits.argmax(axis=1)
-```
-
-We reuse the `eval_model` view created earlier so that `Dropout` is disabled and `BatchNorm` uses stored running stats during inference.
-
-```{code-cell} ipython3
-test_batch = test_ds.as_numpy_iterator().next()
-pred = pred_step(eval_model, test_batch)
-
-fig, axs = plt.subplots(5, 5, figsize=(12, 12))
-for i, ax in enumerate(axs.flatten()):
-  ax.imshow(test_batch['image'][i, ..., 0], cmap='gray')
-  ax.set_title(f'label={pred[i]}')
-  ax.axis('off')
+    metrics.reset()  # Reset the metrics for the next training epoch.
 ```
 
 # 8. Export the model
@@ -254,7 +244,9 @@ for i, ax in enumerate(axs.flatten()):
 Flax models are great for research, but aren't meant to be deployed directly. Instead, high performance inference runtimes like LiteRT or TensorFlow Serving operate on a special [SavedModel](https://www.tensorflow.org/guide/saved_model) format. The [Orbax](https://orbax.readthedocs.io/en/latest/guides/export/orbax_export_101.html) library makes it easy to export Flax models to this format. First, we must create a `JaxModule` object wrapping a model and its prediction method.
 
 ```{code-cell} ipython3
+# !pip install -U "orbax-export[all]"
 from orbax.export import JaxModule, ExportManager, ServingConfig
+import tensorflow as tf
 ```
 
 ```{code-cell} ipython3
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