test notebooks

Author: atqy

aws_sagemaker_studio/frameworks/mxnet_onnx_ei/mxnet_onnx_ei.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Hosting ONNX models with Amazon Elastic Inference\n",
+    "# Hosting ONNX models with Amazon Elastic Inference (Testing)\n",
     "\n",
     "*(This notebook was tested with the \"Python 3 (MXNet CPU Optimized)\" kernel.)*\n",
     "\n",

sagemaker-python-sdk/mxnet_gluon_mnist/mxnet_mnist_with_gluon.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## MNIST Training with MXNet and Gluon\n",
+    "## MNIST Training with MXNet and Gluon (Testing)\n",
     "\n",
     "MNIST is a widely used dataset for handwritten digit classification. It consists of 70,000 labeled 28x28 pixel grayscale images of hand-written digits. The dataset is split into 60,000 training images and 10,000 test images. There are 10 classes (one for each of the 10 digits). This tutorial will show how to train and test an MNIST model on SageMaker using MXNet and the Gluon API.\n",
     "\n"