[backport 0.8] Modify py35 to py37 in doc (#2228) (#2229)

* Modify py35 to py37 in doc (#2228)

* update

apps/anomaly-detection-hd/README.md

@@ -5,7 +5,7 @@ Since outliers are rare and different, that the auto-encoder will not learn to m
 (Retrieved from https://edouardfouche.com/Neural-based-Outlier-Discovery/)
 
 ## Environment
-* Python 3.5/3.6
+* Python 3.6/3.7
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 
 ## Run with Jupyter

apps/anomaly-detection/README.md

@@ -2,7 +2,7 @@
 This is a simple example of unsupervised anomaly detection using Analytics Zoo Keras-Style API. We use RNN to predict following data values based on previous sequence (in order) and measure the distance between predicted values and actual values. If the distance is above some threshold, we report those values as anomaly.
 
 ## Requirement
-* Python 3.5/3.6 (pandas 1.0+)
+* Python 3.6/3.7 (pandas 1.0+)
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 
 ## Install

apps/dogs-vs-cats/README.md

@@ -2,7 +2,7 @@
 In this notebook, we will use a pre-trained Inception_V1 model. But we will operate on the pre-trained model to freeze first few layers, replace the classifier on the top, then fine tune the whole model. And we use the fine-tuned model to solve the dogs-vs-cats classification problem,
 
 ## Environment
-* Python 3.5/3.6
+* Python 3.6/3.7
 * JDK 8
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 * Jupyter Notebook 4.1

apps/image-augmentation-3d/README.md

@@ -2,7 +2,7 @@
 This is a simple example of image augmentation for 3D images using Analytics ZOO API. We use various ways to transform images to augment the dataset.
 
 ## Environment
-* Python 3.5/3.6 (numpy 1.11.1)
+* Python 3.6/3.7 (numpy 1.11.1)
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 
 ## Install or download Analytics Zoo

apps/image-augmentation/README.md

@@ -2,7 +2,7 @@
 This is a simple example of image augmentation using Analytics ZOO API. We use various ways to transform images to augment the dataset.
 
 ## Environment
-* Python 3.5/3.6 (numpy 1.11.1)
+* Python 3.6/3.7 (numpy 1.11.1)
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 
 ## Install or download Analytics Zoo

apps/image-similarity/README.md

@@ -4,7 +4,7 @@ introduced. A real estate example was used to recommend similar houses based on
 provided by users.
 
 ## Environment
-* Python 3.5/3.6
+* Python 3.6/3.7
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 
 ## Install or download Analytics Zoo

apps/object-detection/README.md

@@ -4,7 +4,7 @@ In object-detection.ipynb we use SSD-MobileNet to predict instances of target cl
 In messi.ipynb we use a pretrained detect messi model to detect messi in a video. Proposed areas are labeled with boxes and class scores.
 
 ## Environment
-* Python 3.5/3.6 (Need moviepy)
+* Python 3.6/3.7 (Need moviepy)
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 
 ## Install or download Analytics Zoo

apps/recommendation-ncf/README.md

@@ -3,7 +3,7 @@ This notebook demonstrates how to build a neural network recommendation system (
 
 
 ## Environment
-* Python 3.5/3.6
+* Python 3.6/3.7
 * JDK 8
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 * Jupyter Notebook 4.1

apps/recommendation-wide-n-deep/README.md

@@ -3,7 +3,7 @@ This notebook demonstrates how to build a neural network recommendation system (
 
 
 ## Environment
-* Python 3.5/3.6
+* Python 3.6/3.7
 * JDK 8
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 * Jupyter Notebook 4.1

apps/sentiment-analysis/README.md

@@ -10,7 +10,7 @@ In this example, you will learn how to use Analytics Zoo to develop deep learnin
 * How to train deep learning models
 
 ## Environment
-* Python 3.5/3.6
+* Python 3.6/3.7
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 * Numpy >= 1.16.0
 

docs/docs/ProgrammingGuide/AnalyticsZoo-on-Databricks.md

@@ -64,9 +64,9 @@ sc = init_nncontext()
 Output on Databricks:
 
 ```
-Prepending /databricks/python/lib/python3.5/site-packages/bigdl/share/conf/spark-bigdl.conf to sys.path
-Adding /databricks/python/lib/python3.5/site-packages/zoo/share/lib/analytics-zoo-bigdl_0.9.1-spark_2.4.3-0.6.0-jar-with-dependencies.jar to BIGDL_JARS
-Prepending /databricks/python/lib/python3.5/site-packages/zoo/share/conf/spark-analytics-zoo.conf to sys.path
+Prepending /databricks/python/lib/python3.6/site-packages/bigdl/share/conf/spark-bigdl.conf to sys.path
+Adding /databricks/python/lib/python3.6/site-packages/zoo/share/lib/analytics-zoo-bigdl_0.9.1-spark_2.4.3-0.6.0-jar-with-dependencies.jar to BIGDL_JARS
+Prepending /databricks/python/lib/python3.6/site-packages/zoo/share/conf/spark-analytics-zoo.conf to sys.path
 ```
 
 If you would like to run a completed Analytics Zoo notebook, you can import an Analytics Zoo notebook from a URL directly. 

docs/docs/ProgrammingGuide/visualization.md

@@ -109,7 +109,7 @@ With the summary info generated, we can then use [TensorBoard](https://pypi.pyth
 
 Prerequisites:
 
-1. Python version: 3.5 or 3.6
+1. Python version: 3.6 or 3.7
 2. Pip version >= 9.0.1
 3. TensorFlow 1.13.1
 

docs/docs/PythonUserGuide/install.md

@@ -1,6 +1,6 @@
 For Python users, Analytics Zoo can be installed either [from pip](#install-from-pip-for-local-usage) or [without pip](#install-without-pip).
 
-**NOTE**: Only __Python 3.5__ and __Python 3.6__ are supported for now. We have removed our support and test for Python 2.7 due to its end of life.
+**NOTE**: We have tested on __Python 3.6__ and __Python 3.7__. Support for Python 2.7 has been removed due to its end of life.
 
 ---
 ## **Install from pip for local usage**
@@ -10,7 +10,7 @@ You can use the following command to install the latest release version of __ana
 pip install analytics-zoo
 ```
 
-* You are strongly recommended to use Python 3.5 or 3.6. You might need to run `pip3 install analytics-zoo` instead.
+* You are strongly recommended to use Python 3.6 or 3.7. You might need to run `pip3 install analytics-zoo` instead.
 * You might need to add `sudo` if you don't have the permission for installation.
 
 **Important:**

docs/docs/PythonUserGuide/python-faq.md

@@ -7,8 +7,8 @@ This page lists solutions to some common questions.
     ``` export PYTHONPATH=${ANALYTICS_ZOO_PY_ZIP}:$PYTHONPATH```
 
 2. Python in worker has a different version than that in driver
-    - ```export PYSPARK_PYTHON=/usr/local/bin/python3.5```  This path should be valid on every worker node.
-    - ```export PYSPARK_DRIVER_PYTHON=/usr/local/bin/python3.5```  This path should be valid on every driver node.
+    - ```export PYSPARK_PYTHON=/usr/local/bin/python3.6```  This path should be valid on every worker node.
+    - ```export PYSPARK_DRIVER_PYTHON=/usr/local/bin/python3.6```  This path should be valid on every driver node.
 
 3. __TypeError__: 'JavaPackage' object is not callable
     - Check if every path within the launch script is valid especially the path that ends with jar.

docs/docs/PythonUserGuide/run.md

@@ -1,6 +1,6 @@
 You need to first [install](install.md) analytics-zoo, either [from pip](install/#install-from-pip-for-local-usage) or [without pip](install/#install-without-pip).
 
-**NOTE**: Only __Python 3.5__ and __Python 3.6__ are supported for now. We have removed our support and test for Python 2.7 due to its end of life.
+**NOTE**: We have tested on __Python 3.6__ and __Python 3.7__. Support for Python 2.7 has been removed due to its end of life.
 
 ---
 

pyzoo/zoo/examples/streaming/README.md

@@ -8,7 +8,7 @@ Quick example about integrating analytics-zoo inference/predict service into str
 2. [Streaming Text Classification](https://github.com/intel-analytics/analytics-zoo/tree/master/pyzoo/zoo/examples/streaming/textclassification) uses pre-trained CNN/LSTM model to classify text from network input.
 
 ## Environment
-* Python 3.5/3.6
+* Python 3.6/3.7
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 * Analytics Zoo ([install analytics-zoo]((https://analytics-zoo.github.io/master/#PythonUserGuide/install/) ) via __pip__ or __download the prebuilt package__.)
 

pyzoo/zoo/examples/streaming/objectdetection/README.md

@@ -4,7 +4,7 @@ Imagining we have pre-trained model and image files in file system, and we want
 So, there are two applications in this example: image_path_writer and streaming_object_detection. image_path_writer will package image paths into text files. Meanwhile, streaming_object_detection read image path from those text files, then read image content and make prediction.
 
 ## Environment
-* Python 3.5/3.6
+* Python 3.6/3.7
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 * Analytics Zoo ([install analytics-zoo]((https://analytics-zoo.github.io/master/#PythonUserGuide/install/) ) via __pip__ or __download the prebuilt package__.)
 

pyzoo/zoo/examples/streaming/textclassification/README.md

@@ -2,7 +2,7 @@
 Based on Streaming example NetworkWordCount and Zoo text classification example. Network inputs (Strings) are pre-processed and classified by zoo. We applied a simple text classification model based on zoo example.
 
 ## Environment
-* Python 3.5/3.6
+* Python 3.6/3.7
 * Apache Spark 2.x (This version needs to be same with the version you use to build Analytics Zoo)
 * Analytics Zoo ([install analytics-zoo]((https://analytics-zoo.github.io/master/#PythonUserGuide/install/) ) via __pip__ or __download the prebuilt package__.)