[doc] Fixes to RayDP docs (#14309)

* minor fix to raydp docs

* fix pytorch and tensorflow samples

* fix: minor fixes

doc/source/raydp.rst

@@ -35,13 +35,15 @@ For example,
 
 .. code-block:: python
 
+  import ray
   import raydp
 
+  ray.init()
   spark = raydp.init_spark(
     app_name = "example",
     num_executors = 10,
     executor_cores = 64,
-    memory_per_executor = "256GB"
+    executor_memory = "256GB"
   )
 
 ====================================
@@ -56,24 +58,49 @@ Training a Spark DataFrame with TensorFlow
 
 .. code-block:: python
 
-  d = [{'age': 17 , 'grade': 12}]
-  df = spark.createDataFrame(d).collect()
-
+  from pyspark.sql.functions import col
+  df = spark.range(1, 1000)
+  # calculate z = x + 2y + 1000
+  df = df.withColumn("x", col("id")*2)\
+    .withColumn("y", col("id") + 200)\
+    .withColumn("z", col("x") + 2*col("y") + 1000)
+  
+  from raydp.utils import random_split
+  train_df, test_df = random_split(df, [0.7, 0.3])
 
+  # TensorFlow code
   from tensorflow import keras
-  model = keras.Sequential([])
+  input_1 = keras.Input(shape=(1,))
+  input_2 = keras.Input(shape=(1,))
 
-  estimator = raydp.tf.TFEstimator(
-    model = model,
-    num_worker = 10,
-    feature_columns = ["age"],
-    label_column = ["grade"]
-  )
+  concatenated = keras.layers.concatenate([input_1, input_2])
+  output = keras.layers.Dense(1, activation='sigmoid')(concatenated)
+  model = keras.Model(inputs=[input_1, input_2],
+                      outputs=output)
 
-  estimator.fit_on_spark(df, test_df=None)
+  optimizer = keras.optimizers.Adam(0.01)
+  loss = keras.losses.MeanSquaredError()
+
+  from raydp.tf import TFEstimator
+  estimator = TFEstimator(
+    num_workers=2,
+    model=model,
+    optimizer=optimizer,
+    loss=loss,
+    metrics=["accuracy", "mse"],
+    feature_columns=["x", "y"],
+    label_column="z",
+    batch_size=1000,
+    num_epochs=2,
+    use_gpu=False,
+    config={"fit_config": {"steps_per_epoch": 2}})
+
+  estimator.fit_on_spark(train_df, test_df)
 
   tensorflow_model = estimator.get_model()
 
+  estimator.shutdown()
+
 
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 Training a Spark DataFrame with PyTorch
@@ -84,21 +111,52 @@ PyTorch.
 
 .. code-block:: python
 
-  d = [{'age': 17 , 'grade': 12}]
-  df = spark.createDataFrame(d).collect()
-
+  from pyspark.sql.functions import col
+  df = spark.range(1, 1000)
+  # calculate z = x + 2y + 1000
+  df = df.withColumn("x", col("id")*2)\
+    .withColumn("y", col("id") + 200)\
+    .withColumn("z", col("x") + 2*col("y") + 1000)
+  
+  from raydp.utils import random_split
+  train_df, test_df = random_split(df, [0.7, 0.3])
 
+  # PyTorch Code 
   import torch
-  model = torch.nn.Sequential()
+  class LinearModel(torch.nn.Module):
+      def __init__(self):
+          super(LinearModel, self).__init__()
+          self.linear = torch.nn.Linear(2, 1)
 
-  estimator = raydp.tf.TFEstimator(
+      def forward(self, x, y):
+          x = torch.cat([x, y], dim=1)
+          return self.linear(x)
+
+  model = LinearModel()
+  optimizer = torch.optim.Adam(model.parameters())
+  loss_fn = torch.nn.MSELoss()
+
+  def lr_scheduler_creator(optimizer, config):
+      return torch.optim.lr_scheduler.MultiStepLR(
+        optimizer, milestones=[150, 250, 350], gamma=0.1)
+
+  # You can use the RayDP Estimator API or libraries like RaySGD for distributed training.
+  from raydp.torch import TorchEstimator
+  estimator = TorchEstimator(
+    num_workers = 2,
     model = model,
-    num_worker = 10,
-    feature_columns = ["age"],
-    label_column = ["grade"]
+    optimizer = optimizer,
+    loss = loss_fn,
+    lr_scheduler_creator=lr_scheduler_creator,
+    feature_columns = ["x", "y"],
+    label_column = ["z"],
+    batch_size = 1000,
+    num_epochs = 2
   )
 
-  estimator.fit_on_spark(df, test_df=None)
+  estimator.fit_on_spark(train_df, test_df)
 
   pytorch_model = estimator.get_model()
+
+  estimator.shutdown()