diff --git a/examples/ML+DL-Examples/Spark-DL/dl_inference/pytorch/image_classification_torch.ipynb b/examples/ML+DL-Examples/Spark-DL/dl_inference/pytorch/image_classification_torch.ipynb
index ce7abec4..ea07ce28 100644
--- a/examples/ML+DL-Examples/Spark-DL/dl_inference/pytorch/image_classification_torch.ipynb
+++ b/examples/ML+DL-Examples/Spark-DL/dl_inference/pytorch/image_classification_torch.ipynb
@@ -547,7 +547,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Optional: create a unique file for the TensorRT timing cache\n",
+    "# Optional: set the filename for the TensorRT timing cache\n",
     "timestamp = time.time()\n",
     "timing_cache = f\"/tmp/timing_cache-{timestamp}.bin\"\n",
     "with open(timing_cache, \"wb\") as f:\n",
@@ -687,7 +687,6 @@
     "# Compile the traced graph to produce an optimized module\n",
     "trt_gm = trt.dynamo.compile(exp_program, \n",
     "                            inputs=inputs, \n",
-    "                            require_full_compilation=True, \n",
     "                            timing_cache_path=timing_cache)\n",
     "\n",
     "stream = torch.cuda.Stream()\n",
diff --git a/examples/ML+DL-Examples/Spark-DL/dl_inference/pytorch/regression_torch.ipynb b/examples/ML+DL-Examples/Spark-DL/dl_inference/pytorch/regression_torch.ipynb
index d2fd9157..5ccc22ec 100644
--- a/examples/ML+DL-Examples/Spark-DL/dl_inference/pytorch/regression_torch.ipynb
+++ b/examples/ML+DL-Examples/Spark-DL/dl_inference/pytorch/regression_torch.ipynb
@@ -539,6 +539,31 @@
     "(You may see a warning about modelopt quantization. This is safe to ignore, as [implicit quantization](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#intro-quantization) is deprecated in the latest TensorRT. See [this link](https://pytorch.org/TensorRT/tutorials/_rendered_examples/dynamo/vgg16_fp8_ptq.html) for a guide to explicit quantization.)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ffb27fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch_tensorrt as trt\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0c10f90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Optional: set the filename for the TensorRT timing cache\n",
+    "timestamp = time.time()\n",
+    "timing_cache = f\"/tmp/timing_cache-{timestamp}.bin\"\n",
+    "with open(timing_cache, \"wb\") as f:\n",
+    "    pass"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 20,
@@ -599,8 +624,6 @@
     }
    ],
    "source": [
-    "import torch_tensorrt as trt\n",
-    "\n",
     "inputs_bs1 = torch.randn((10, 8), dtype=torch.float).to(\"cuda\")\n",
     "# This indicates dimension 0 of inputs_bs1 is dynamic whose range of values is [1, 50]. No recompilation will happen when the batch size changes.\n",
     "torch._dynamo.mark_dynamic(inputs_bs1, 0, min=1, max=50)\n",
@@ -609,6 +632,7 @@
     "    ir=\"torch_compile\",\n",
     "    inputs=inputs_bs1,\n",
     "    enabled_precisions={torch.float},\n",
+    "    timing_cache_path=timing_cache,\n",
     ")\n",
     "\n",
     "stream = torch.cuda.Stream()\n",
@@ -719,7 +743,9 @@
     "# Produce traced graph in the ExportedProgram format\n",
     "exp_program = trt.dynamo.trace(loaded_mlp, inputs)\n",
     "# Compile the traced graph to produce an optimized module\n",
-    "trt_gm = trt.dynamo.compile(exp_program, inputs=inputs, device='cuda:0')\n",
+    "trt_gm = trt.dynamo.compile(exp_program,\n",
+    "                            inputs=inputs,\n",
+    "                            timing_cache_path=timing_cache)\n",
     "\n",
     "stream = torch.cuda.Stream()\n",
     "with torch.no_grad(), torch.cuda.stream(stream):\n",