Adding expln for decrease in batch size when tuning with Training Com…

…piler (#3484)

sagemaker-training-compiler/tensorflow/single_gpu_single_node/hyper-parameter-tuning.ipynb

@@ -329,7 +329,7 @@
    "source": [
     "### Experimenting with Optimized TensorFlow\n",
     "\n",
-    "Compilation through SageMaker Training Compiler changes the memory footprint of the model. Most commonly, this manifests as a reduction in memory utilization and a consequent increase in the largest batch size that can fit on the GPU. In the example below we will find the new batch size with SageMaker Training Compiler enabled and the resultant latency per epoch.\n",
+    "Compilation through SageMaker Training Compiler changes the memory footprint of the model. Most commonly, this manifests as a reduction in memory utilization and a consequent increase in the largest batch size that can fit on the GPU. But in some cases, the compiler intelligently promotes caching which leads to increased memory utilization and a consequent decrease in the largest batch size that can fit on the GPU. In the example below we will find the new batch size with SageMaker Training Compiler enabled and the resultant latency per epoch.\n",
     "\n",
     "**Note:** We recommend you to turn the SageMaker Debugger's profiling and debugging tools off when you use compilation to avoid additional overheads."
    ]