diff --git a/deep_atlas/deep_atlas_tutorial.ipynb b/deep_atlas/deep_atlas_tutorial.ipynb
index 14aa7e184e..6fe6295021 100644
--- a/deep_atlas/deep_atlas_tutorial.ipynb
+++ b/deep_atlas/deep_atlas_tutorial.ipynb
@@ -28,8 +28,6 @@
     "\n",
     "This notebook works with 3D images and segmentations, and it results in two trained models: a segmentation network `seg_net` and a registration network `reg_net`. There are two training phases: a pre-training phase for the segmentation network alone, and an alternating training phase for the two networks together. Use of the trained models for inference is demonstrated at the end of the notebook.\n",
     "\n",
-    "Please note that this notebook is verified to work well on the NVIDIA H100/A100/V100 GPU. However, there is a problem in the `Train Networks` part when running on the NVIDIA L40/3090Ti/3080Ti GPU. Track this [issue](https://github.com/Project-MONAI/tutorials/issues/1336) for more detail.\n",
-    "\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/deep_atlas/deep_atlas_tutorial.ipynb)\n",
     "\n",
     "---\n",
@@ -146,9 +144,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "576b9aa6",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -215,6 +211,11 @@
     "# Set deterministic training for reproducibility\n",
     "monai.utils.set_determinism(seed=2938649572)\n",
     "\n",
+    "# Turn off the TF32 precision. Otherwise, because of the rounding error, the ssim_loss will be a weird value on some Ampere GPUs, like L40, 3090Ti and 3080Ti.\n",
+    "# Track this issue (https://github.com/Project-MONAI/tutorials/issues/1336) for more detail.\n",
+    "torch.backends.cuda.matmul.allow_tf32 = False\n",
+    "torch.backends.cudnn.allow_tf32 = False\n",
+    "\n",
     "monai.config.print_config()"
    ]
   },
@@ -718,9 +719,7 @@
    "cell_type": "code",
    "execution_count": 16,
    "id": "0a020fc2",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1233,9 +1232,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "fbbc25b5",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Training cell\n",
@@ -1605,9 +1602,7 @@
    "cell_type": "code",
    "execution_count": 39,
    "id": "11402c6e",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# The following are dictionaries that map segmentation availability labels 00,10,01,11 to MONAI dataloaders\n",
@@ -1690,9 +1685,7 @@
    "cell_type": "code",
    "execution_count": 41,
    "id": "bfdacfa6",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1718,7 +1711,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "c95101e5",
    "metadata": {},
@@ -1758,9 +1750,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "ef8af785",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Training cell\n",
@@ -1954,9 +1944,7 @@
    "cell_type": "code",
    "execution_count": 43,
    "id": "050dd7b0",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2169,9 +2157,7 @@
    "cell_type": "code",
    "execution_count": 60,
    "id": "5801a48e",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2489,7 +2475,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,