Orca: add save and load model doc to PyTorch Estimator quick-start. (#5504)

* feat: add save and load model doc to pytorch estimator quickstart.

* fix: fix typo.

* fix: fix typo

* fix: fix typo.

* feat: add doc to ray backend quickstart

* fix: fix typo.

* feat: add doc to ray backend quickstart

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

@@ -130,4 +130,18 @@ for r in result:
     print(r, ":", result[r])
 ```
 
+### **Step 5: Save and Load the Model**
+
+Save the Estimator states (including model and optimizer) to the provided model path.
+
+```python
+est.save("mnist_model")
+```
+
+Load the Estimator states (model and possibly with optimizer) from 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-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 states (including model and optimizer) to the provided model path.
+
+```python
+est.save("mnist_model")
+```
+
+Load the Estimator states (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.