Modefied the Quickstart page of Orca

docs/docs/Orca/orca-pytorch-quickstart.md

@@ -1,22 +1,26 @@
-## **Orca PyTorch Quickstart**
 
-**In this guide we’ll show you how to organize your PyTorch code into Orca in 3 steps**
+**In this guide we’ll show you how to organize your PyTorch code into Orca in 3 steps.**
 
-Organizing your code with Orca makes your code:
-* Keep all the flexibility
+Scaling your Pytorch applications with Orca makes your code:
+
+* Well-organized and flexible
 * Easier to reproduce
-* Utilize distributed training without changing your model
+* Able to perform distributed training without changing your model
 
-### **Step 0: Prepare environment**
-We recommend you to use [Anaconda](https://www.anaconda.com/distribution/#linux) to prepare the environments, especially if you want to run on a yarn cluster(yarn-client mode only).
-**Note:** You can install the latest analytics whl by following instructions ([here](https://analytics-zoo.github.io/master/#PythonUserGuide/install/#install-the-latest-nightly-build-wheels-for-pip)).
-```
-conda create -n zoo python=3.7 #zoo is conda enviroment name, you can set another name you like.
+### **Step 0: Prepare Environment**
+We recommend you to use [Anaconda](https://www.anaconda.com/distribution/#linux) to prepare the environments, especially if you want to run on a yarn cluster (yarn-client mode only).
+
+Download and install latest analytics-zoo whl by the following instructions [here](../PythonUserGuide/install/#install-the-latest-nightly-build-wheels-for-pip).  
+
+**Note:** Conda environment is required to run on Yarn, but not strictly necessary for running on local.
+
+```bash
+conda create -n zoo python=3.7 # zoo is conda enviroment name, you can set another name you like.
 conda activate zoo
-pip install analytics-zoo==0.9.0.dev0 # or above
+pip install analytics_zoo-${VERSION}-${TIMESTAMP}-py2.py3-none-${OS}_x86_64.whl 
 pip install jep==3.9.0
-conda install pytorch torchvision cpuonly -c pytorch #command for linux
-conda install pytorch torchvision -c pytorch #command for macOS
+conda install pytorch torchvision cpuonly -c pytorch # command for linux
+conda install pytorch torchvision -c pytorch # command for macOS
 ```
 
 ### **Step 1: Init Orca Context**
@@ -34,8 +38,9 @@ sc = init_orca_context(
         "spark.task.maxFailures": "1",
         "spark.driver.extraJavaOptions": "-Dbigdl.failure.retryTimes=1"})
 ```
-**Note:** you should `export HADOOP_CONF_DIR=/path/to/hadoop/conf/dir`
-* Reference: [Orca Context](https://analytics-zoo.github.io/master/#Orca/context/)
+**Note:** You should `export HADOOP_CONF_DIR=/path/to/hadoop/conf/dir`. 
+
+View [Orca Context](./context) for more details.
 
 ### **Step 2: Define PyTorch Model, Loss function and Optimizer**
 ```python
@@ -64,42 +69,51 @@ class LeNet(nn.Module):
 model = LeNet()
 model.train()
 criterion = nn.NLLLoss()
-adam = Adam(args.lr)
+adam = Adam(1e-4)
 ```
 
-### **Step 3: Fit with Orca PyTorch Estimator**
-1. Define the data in whatever way you want. Orca just needs a dataloader, a callable datacreator or an Orca SparkXShards
+### **Step 3: Fit with Orca PyTorch Estimator**  
+
+1)  Define the data in whatever way you want. Orca just needs a [Pytorch DataLoader](https://pytorch.org/docs/stable/data.html), a data creator function or [Orca SparkXShards](./data).
 ```python
-torch.manual_seed(args.seed)
+import torch
+from torchvision import datasets, transforms
 
+torch.manual_seed(0)
+dir='./dataset'
+batch_size=64
+test_batch_size=64
 train_loader = torch.utils.data.DataLoader(
-    datasets.MNIST(args.dir, train=True, download=True,
+    datasets.MNIST(dir, train=True, download=True,
                    transform=transforms.Compose([
                        transforms.ToTensor(),
                        transforms.Normalize((0.1307,), (0.3081,))
                    ])),
-    batch_size=args.batch_size, shuffle=True)
+    batch_size=batch_size, shuffle=True)
 test_loader = torch.utils.data.DataLoader(
-    datasets.MNIST(args.dir, train=False,
+    datasets.MNIST(dir, train=False,
                    transform=transforms.Compose([
                        transforms.ToTensor(),
                        transforms.Normalize((0.1307,), (0.3081,))
                    ])),
-    batch_size=args.test_batch_size, shuffle=False) 
+    batch_size=test_batch_size, shuffle=False) 
 ```
 
-2. Create an estimator
+2)  Create an Estimator
 ```python
 from zoo.orca.learn.pytorch import Estimator 
+
 zoo_estimator = Estimator.from_torch(model=model, optimizer=adam, loss=criterion, backend="bigdl") 
 ```
 
-3. Fit with estimator
+3)  Fit with Estimator
+
 ```python
 from zoo.orca.learn.metrics import Accuracy
 from zoo.orca.learn.trigger import EveryEpoch 
-zoo_estimator.fit(data=train_loader, epochs=args.epochs, validation_data=test_loader,
+
+zoo_estimator.fit(data=train_loader, epochs=10, validation_data=test_loader,
                   validation_methods=[Accuracy()], checkpoint_trigger=EveryEpoch()) 
 ```
 
-**Note:** you should call `stop_orca_context()` when your application finishes.
+**Note:** You should call `stop_orca_context()` when your application finishes.

docs/docs/Orca/orca-tf-quickstart.md

@@ -1,43 +1,47 @@
-## **Orca TensorFlow Quickstart**
 
-**In this guide we’ll show you how to organize your TensorFlow code into Orca in 3 steps**
+**In this guide we’ll show you how to organize your TensorFlow code into Orca in 3 steps.**
 
-Organizing your code with Orca makes your code:
-* Keep all the flexibility
+Scaling your TensorFlow applications with Orca makes your code:
+
+* Well-organized and flexible
 * Easier to reproduce
-* Utilize distributed training without changing your model
+* Able to perform distributed training without changing your model
+
+### **Step 0: Prepare Environment**
+We recommend you to use [Anaconda](https://www.anaconda.com/distribution/#linux) to prepare the environments, especially if you want to run on a yarn cluster (yarn-client mode only).
+
+Download and install latest analytics-zoo whl by the following instructions [here](../PythonUserGuide/install/#install-the-latest-nightly-build-wheels-for-pip).
 
-### **Step 0: Prepare environment**
-Download and install latest analytics whl by following instructions ([here](https://analytics-zoo.github.io/master/#PythonUserGuide/install/#install-the-latest-nightly-build-wheels-for-pip)).
+**Note:** Conda environment is required to run on Yarn, but not strictly necessary for running on local.
 
 ```bash
-conda create -y -n analytics-zoo python==3.7.7
-conda activate analytics-zoo
+conda create -n zoo python=3.7 # zoo is conda enviroment name, you can set another name you like.
+conda activate zoo
 pip install analytics_zoo-${VERSION}-${TIMESTAMP}-py2.py3-none-${OS}_x86_64.whl
 pip install tensorflow==1.15.0
 pip install psutil
 ```
 
-Note: conda environment is required to run on Yarn, but not strictly necessary for running on local.
-
 ### **Step 1: Init Orca Context**
 ```python
-import tensorflow as tf
 from zoo.orca import init_orca_context, stop_orca_context
-from zoo.orca.learn.tf.estimator import Estimator
 
 # run in local mode
 init_orca_context(cluster_mode="local", cores=4)
 
 # run in yarn client mode
 init_orca_context(cluster_mode="yarn-client", num_nodes=2, cores=2, driver_memory="6g")
 ```
-* Reference: [Orca Context](https://analytics-zoo.github.io/master/#Orca/context/)
+**Note:** You should `export HADOOP_CONF_DIR=/path/to/hadoop/conf/dir`.
+
+View [Orca Context](./context) for more details.
 
 ### **Step 2: Define Model, Loss Function and Metrics**
 
-#### **For Keras Users**
+* For Keras Users
 ```python
+import tensorflow as tf
+
 model = tf.keras.Sequential(
     [tf.keras.layers.Conv2D(20, kernel_size=(5, 5), strides=(1, 1), activation='tanh',
                             input_shape=(28, 28, 1), padding='valid'),
@@ -56,8 +60,10 @@ model.compile(optimizer=tf.keras.optimizers.RMSprop(),
               metrics=['accuracy'])
 ```
 
-#### **For Graph Users**
+* For Graph Users
 ```python
+import tensorflow as tf
+
 def accuracy(logits, labels):
     predictions = tf.argmax(logits, axis=1, output_type=labels.dtype)
     is_correct = tf.cast(tf.equal(predictions, labels), dtype=tf.float32)
@@ -80,14 +86,12 @@ images = tf.placeholder(dtype=tf.float32, shape=(None, 28, 28, 1))
 labels = tf.placeholder(dtype=tf.int32, shape=(None,))
 
 logits = lenet(images)
-
 loss = tf.reduce_mean(tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels))
-
 acc = accuracy(logits, labels)
 ```
 
 ### **Step 3: Fit with Orca TensorFlow Estimator**
-1. Define the dataset in whatever way you want. Orca just needs tf.data.Dataset, Spark DataFrame or Orca SparkXShards.
+1)  Define the dataset in whatever way you want. Orca supports [tf.data.Dataset](https://www.tensorflow.org/api_docs/python/tf/data/Dataset), [Spark DataFrame](https://spark.apache.org/docs/latest/sql-programming-guide.html) and [Orca SparkXShards](./data).
 ```python
 def preprocess(x, y):
     return tf.to_float(tf.reshape(x, (-1, 28, 28, 1))) / 255.0, y
@@ -103,43 +107,49 @@ val_dataset = tf.data.Dataset.from_tensor_slices((val_feature, val_label))
 val_dataset = val_dataset.map(preprocess)
 ```
 
-2. Create an estimator
+2)  Create an Estimator
+
 * For Keras Users
 ```python
-est = Estimator.from_keras(keras_model=model)
+from zoo.orca.learn.tf.estimator import Estimator
+
+zoo_estimator = Estimator.from_keras(keras_model=model)
 ```
 * For Graph Users
 ```python
-est = Estimator.from_graph(inputs=images, 
-                           outputs=logits,
-                           labels=labels,
-                           loss=loss,
-                           optimizer=tf.train.AdamOptimizer(),
-                           metrics={"acc": acc})
+from zoo.orca.learn.tf.estimator import Estimator
+
+zoo_estimator = Estimator.from_graph(inputs=images, 
+                                     outputs=logits,
+                                     labels=labels,
+                                     loss=loss,
+                                     optimizer=tf.train.AdamOptimizer(),
+                                     metrics={"acc": acc})
 ```
 
-3. Fit with estimator
+3)  Fit with Estimator
 ```python
-est.fit(data=train_dataset,
-        batch_size=320,
-        epochs=max_epoch,
-        validation_data=val_dataset)
+zoo_estimator.fit(data=train_dataset,
+                  batch_size=320,
+                  epochs=100,
+                  validation_data=val_dataset)
 ```
 
-4. Evaluate with estimator
+4)  Evaluate with Estimator
 ```python
-result = est.evaluate(val_dataset)
+result = zoo_estimator.evaluate(val_dataset)
 print(result)
 ```
 
-5. Save Model
+5)  Save Model
+
 * For Keras Users
 ```python
-est.save_keras_model("/tmp/mnist_keras.h5")
+zoo_estimator.save_keras_model("/tmp/mnist_keras.h5")
 ```
 * For Graph Users
 ```python
-est.save_tf_checkpoint("/tmp/lenet/model")
+zoo_estimator.save_tf_checkpoint("/tmp/lenet/model")
 ```
 
-**Note:** you should call `stop_orca_context()` when your application finishes.
+**Note:** You should call `stop_orca_context()` when your application finishes.

docs/mkdocs.yml

@@ -164,10 +164,10 @@ pages:
   - Overview: Orca/overview.md
   - OrcaContext: Orca/context.md
   - Data: Orca/data.md
-  - TensorFlow Estimator: Orca/orca-tf-estimator.md
   - TensorFlow Quickstart: Orca/orca-tf-quickstart.md
-  - PyTorch Estimator: Orca/orca-pytorch-estimator.md
+  - TensorFlow Estimator: Orca/orca-tf-estimator.md
   - PyTorch Quickstart: Orca/orca-pytorch-quickstart.md
+  - PyTorch Estimator: Orca/orca-pytorch-estimator.md
 - Powered by: powered-by.md
 - Presentations: presentations.md
 - Meetup & Webinar: meetup.md