add dllib quickstart (#3416)

* add dllib quickstart
* fix init_nncontext issue

docs/readthedocs/source/doc/DLlib/QuickStart/dllib-quickstart.md

@@ -0,0 +1,70 @@
+# DLlib Quickstarts
+
+---
+
+![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/dllib/colab-notebook/dllib_keras_api.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/dllib/colab-notebook/dllib_keras_api.ipynb)
+
+---
+
+**In this guide we will demonstrate how to use _DLlib keras style api_ and _DLlib NNClassifier_ for classification.**
+
+### **Step 0: Prepare Environment**
+
+We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) to prepare the environment. Please refer to the [install guide](../Overview/chronos.html#install) for more details.
+
+```bash
+conda create -n my_env python=3.7 # "my_env" is conda environment name, you can use any name you like.
+conda activate my_env
+pip install bigdl-dllib
+```
+
+### Step 1: Data loading and processing using Spark DataFrame
+
+```python
+df = spark.read.csv(path, sep=',', inferSchema=True).toDF("num_times_pregrant", "plasma_glucose", "blood_pressure", "skin_fold_thickness", "2-hour_insulin", "body_mass_index", "diabetes_pedigree_function", "age", "class")
+```
+
+We process the data using Spark API and split the data into train and test set.
+
+```python
+vecAssembler = VectorAssembler(outputCol="features")
+vecAssembler.setInputCols(["num_times_pregrant", "plasma_glucose", "blood_pressure", "skin_fold_thickness", "2-hour_insulin", "body_mass_index", "diabetes_pedigree_function", "age"])
+train_df = vecAssembler.transform(df)
+
+changedTypedf = train_df.withColumn("label", train_df["class"].cast(DoubleType())+lit(1))\
+    .select("features", "label")
+(trainingDF, validationDF) = changedTypedf.randomSplit([0.9, 0.1])
+```
+
+### Step 3: Define classification model using DLlib keras style api
+
+```python
+x1 = Input(shape=(8,))
+dense1 = Dense(12, activation='relu')(x1)
+dense2 = Dense(8, activation='relu')(dense1)
+dense3 = Dense(2)(dense2)
+model = Model(x1, dense3)
+```
+
+### Step 4: Create NNClassifier and Fit NNClassifier
+
+```python
+classifier = NNClassifier(model, CrossEntropyCriterion(), [8]) \
+    .setOptimMethod(Adam()) \
+    .setBatchSize(32) \
+    .setMaxEpoch(150)
+
+nnModel = classifier.fit(trainingDF)
+```
+
+### Step 5: Evaluate the trained model
+
+```python
+predictionDF = nnModel.transform(validationDF).cache()
+predictionDF.sample(False, 0.1).show()
+
+
+evaluator = MulticlassClassificationEvaluator(
+    labelCol="label", predictionCol="prediction", metricName="accuracy")
+accuracy = evaluator.evaluate(predictionDF)
+```