Merge pull request #506 from zkaoudi/main

some cleaning up

README.md

@@ -40,12 +40,13 @@ Apache Wayang (incubating) aims at freeing data engineers and software developer
 - [Postgres](http://www.postgresql.org)
 - [SQLite](https://www.sqlite.org/)
 - [Apache Kafka](https://kafka.apache.org)
+- [Tensorflow](https://www.tensorflow.org/)
 
 Apache Wayang (incubating) can be used via the following APIs:
-- Java native
 - Java scala-like
 - Scala
-- SQL (limited support of simple select-project queries for now)
+- SQL
+- Java native (recommended only for low level)
 
 ## Quick Guide for Running Wayang
 
@@ -61,8 +62,8 @@ You first have to build the binaries as shown [here](guides/tutorial.md).
 Once you have the binaries built, follow these steps to install Wayang:
 
 ```shell
-tar -xvf wayang-1.0.0-snapshot.tar.gz
-cd wayang-1.0.0-SNAPSHOT
+tar -xvf wayang-1.0.1-SNAPSHOT.tar.gz
+cd wayang-1.0.1-SNAPSHOT
 ```
 
 In linux

RELEASE_NOTES

@@ -7,7 +7,7 @@ Based on this release the projects wants to continue the path towards becoming a
 Apache TLP.
 
 This release supports the following new features:
-- Support for Tensorflow Java as a platform (Note: this platform is not compatible with Apple's M1 chip)
+- Support for Tensorflow Java as a platform (Note: this platform is compatible with Apple's Silicon's chip only with the right arm-based java version)
 - Support for Kafka source/sink
 - Python API
 - MultiContext for federated data processing

wayang-platforms/wayang-tensorflow/src/test/java/org/apache/wayang/tensorflow/model/TensorflowModelTest.java

@@ -35,7 +35,7 @@
 import org.tensorflow.types.TFloat32;
 import org.tensorflow.types.TInt32;
 public class TensorflowModelTest {
-    @Ignore
+    @Test
     public void test() {
         FloatNdArray x = NdArrays.ofFloats(Shape.of(6, 4))
                 .set(NdArrays.vectorOf(5.1f, 3.5f, 1.4f, 0.2f), 0)

wayang-tests-integration/src/test/java/org/apache/wayang/tests/TensorflowIntegrationIT.java

@@ -68,7 +68,7 @@ public class TensorflowIntegrationIT {
 
     public static String[] LABELS = new String[]{"Iris-setosa", "Iris-versicolor", "Iris-virginica"};
 
-    @Ignore
+    @Test
     public void test() {
         /* training features */
         CollectionSource<float[]> trainXSource = new CollectionSource<>(trainX, float[].class);