Orca: add save and load model doc to PyTorch Estimator quick-start.

docs/readthedocs/source/doc/Orca/QuickStart/orca-pytorch-quickstart.md

@@ -132,4 +132,18 @@ for r in result:
     print(r, ":", result[r])
 ```
 
+### **Step 5: Save and Load the Model **
+
+Save the Estimator state (including model and optimizer) to the provided model path.
+
+```python
+est.save("mnist_model")
+```
+
+Load the Estimator state (model and possibly with optimizer) from the provided model path.
+
+```python
+est.load("mnist_model")
+```
+
 **Note:** You should call `stop_orca_context()` when your application finishes.

docs/readthedocs/source/doc/Orca/QuickStart/orca-tf2keras-quickstart.md

@@ -130,20 +130,20 @@ You could also save the model to Keras H5 format by passing `save_format='h5'`
 
 ```python
 # save model in SavedModel format
-estimator.save("/tmp/cifar10_model")
+est.save("/tmp/cifar10_model")
 
 # load model
-estimator.load("/tmp/cifar10_model")
+est.load("/tmp/cifar10_model")
 ```
 
 **2. HDF5 format**
 
 ```python
 # save model in H5 format
-estimator.save("/tmp/cifar10_model.h5", save_format='h5')
+est.save("/tmp/cifar10_model.h5", save_format='h5')
 
 # load model
-estimator.load("/tmp/cifar10_model.h5")
+est.load("/tmp/cifar10_model.h5")
 ```
 
 That's it, the same code can run seamlessly in your local laptop and to distribute K8s or Hadoop cluster.

python/orca/colab-notebook/quickstart/pytorch_lenet_mnist.ipynb

@@ -465,6 +465,36 @@
         "The accuracy of this model has reached 98%."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qhLhJ4vWc-95"
+      },
+      "source": [
+        "### **Step 5: Save the Model**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DmmtHcPodLA3"
+      },
+      "source": [
+        "Save the Estimator state (including model and optimizer) to the provided model path.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WUbsriiBdprT"
+      },
+      "source": [
+        "est.save(\"mnist_model\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "metadata": {