Increase Final Version for main branch (deepjavalibrary#1644)

Author: zachgk

android/README.md

@@ -15,9 +15,9 @@ In gradle, you can include the snapshot repository and add the 4 modules in your
 
 ```
 dependencies {
-    implementation "ai.djl:api:0.16.0-SNAPSHOT"
-    implementation "ai.djl.android:core:0.16.0-SNAPSHOT"
-    androidRuntimeOnly "ai.djl.pytorch:pytorch-engine:0.16.0-SNAPSHOT"
-    androidRuntimeOnly "ai.djl.android:pytorch-native:0.16.0-SNAPSHOT"
+    implementation "ai.djl:api:0.17.0-SNAPSHOT"
+    implementation "ai.djl.android:core:0.17.0-SNAPSHOT"
+    androidRuntimeOnly "ai.djl.pytorch:pytorch-engine:0.17.0-SNAPSHOT"
+    androidRuntimeOnly "ai.djl.android:pytorch-native:0.17.0-SNAPSHOT"
 }
 ```

api/README.md

@@ -35,7 +35,7 @@ You can pull the DJL API from the central Maven repository by including the foll
 <dependency>
     <groupId>ai.djl</groupId>
     <artifactId>api</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
 </dependency>
 ```
 

basicdataset/README.md

@@ -39,7 +39,7 @@ You can pull the module from the central Maven repository by including the follo
 <dependency>
     <groupId>ai.djl</groupId>
     <artifactId>basicdataset</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
 </dependency>
 ```
 

bom/README.md

@@ -22,7 +22,7 @@ will need to mention the type as pom and the scope as import) as the following:
         <dependency>
             <groupId>ai.djl</groupId>
             <artifactId>bom</artifactId>
-            <version>0.16.0</version>
+            <version>0.17.0</version>
             <type>pom</type>
             <scope>import</scope>
         </dependency>
@@ -38,7 +38,7 @@ will need to mention the type as pom and the scope as import) as the following:
         <dependency>
             <groupId>ai.djl</groupId>
             <artifactId>bom</artifactId>
-            <version>0.16.0</version>
+            <version>0.17.0</version>
             <type>pom</type>
             <scope>import</scope>
         </dependency>
@@ -70,7 +70,7 @@ will need to mention the type as pom and the scope as import) as the following:
 - First you need add BOM into your build.gradle file as the following:
 
 ```
-    implementation platform("ai.djl:bom:0.16.0")
+    implementation platform("ai.djl:bom:0.17.0")
 ```
 
 - Then you import the desired DJL modules into to you pom.xml file (no version is needed):

djl-zero/README.md

@@ -34,6 +34,6 @@ You can pull the module from the central Maven repository by including the follo
 <dependency>
     <groupId>ai.djl</groupId>
     <artifactId>djl-zero</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
 </dependency>
 ```

docs/development/development_guideline.md

@@ -159,7 +159,7 @@ You can create your own NDArray renderer as follows:
 Please make sure to:
 
 - Check the "On-demand" option, which causes IntelliJ to only render the NDArray when you click on the variable.
-- Change the "Use following expression" field to something like [toDebugString(100, 10, 10, 20)](https://javadoc.io/static/ai.djl/api/0.16.0/ai/djl/ndarray/NDArray.html#toDebugString-int-int-int-int-)
+- Change the "Use following expression" field to something like [toDebugString(100, 10, 10, 20)](https://javadoc.io/static/ai.djl/api/0.17.0/ai/djl/ndarray/NDArray.html#toDebugString-int-int-int-int-)
 if you want to adjust the range of NDArray's debug output.
 
 ## Common Problems

docs/development/how_to_use_dataset.md

@@ -108,8 +108,8 @@ api group: 'org.apache.commons', name: 'commons-csv', version: '1.7'
 In order to extend the dataset, the following dependencies are required:
 
 ```
-api "ai.djl:api:0.16.0"
-api "ai.djl:basicdataset:0.16.0"
+api "ai.djl:api:0.17.0"
+api "ai.djl:basicdataset:0.17.0"
 ```
 
 There are four parts we need to implement for CSVDataset.

docs/development/memory_management.md

@@ -37,7 +37,7 @@ The intermediate NDArrays involving in training case are usually
 
 In general, all the parameters in the model should be associated with Model level NDManager.
 All of the input and output NDArrays should be associated with one NDManager which is one level down to the model NDManager.
-Please check if you call [batch.close()](https://javadoc.io/static/ai.djl/api/0.16.0/ai/djl/training/dataset/Batch.html#close--)
+Please check if you call [batch.close()](https://javadoc.io/static/ai.djl/api/0.17.0/ai/djl/training/dataset/Batch.html#close--)
 to release one batch of the dataset at the end of each batch.
 If you still see the memory grows as the training process goes, it is most likely that intermediate NDArrays are attached to the Model(Block) parameter level.
 As a result, those NDArrays would not closed until the training is finished.

docs/development/troubleshooting.md

@@ -28,7 +28,7 @@ For example, adding MXNet engine dependencies:
 Gradle:
 
 ```
-implementation "ai.djl.mxnet:mxnet-engine:0.16.0"
+implementation "ai.djl.mxnet:mxnet-engine:0.17.0"
 // See https://github.com/deepjavalibrary/djl/blob/master/engines/mxnet/mxnet-engine/README.md for more MXNet library selection options
 runtimeOnly "ai.djl.mxnet:mxnet-native-auto:1.8.0"
 ```

docs/hybrid_engine.md

@@ -22,17 +22,17 @@ to run in a hybrid mode:
 To use it along with Apache MXNet for additional API support, add the following two dependencies:
 
 ```
-runtimeOnly "ai.djl.mxnet:mxnet-engine:0.16.0"
+runtimeOnly "ai.djl.mxnet:mxnet-engine:0.17.0"
 ```
 
 You can also use PyTorch or TensorFlow Engine as the supplemental engine by adding their corresponding dependencies.
 
 ```
-runtimeOnly "ai.djl.pytorch:pytorch-engine:0.16.0"
+runtimeOnly "ai.djl.pytorch:pytorch-engine:0.17.0"
 ```
 
 ```
-runtimeOnly "ai.djl.tensorflow:tensorflow-engine:0.16.0"
+runtimeOnly "ai.djl.tensorflow:tensorflow-engine:0.17.0"
 ```
 
 ## How Hybrid works