[0.17.0] Increase Final Version for release branch

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

docs/load_model.md

@@ -181,7 +181,7 @@ Here is a few tips you can use to help you debug model loading issue:
 See [here](development/configure_logging.md#configure-logging-level) for how to enable debug log
 
 #### List models programmatically in your code
-You can use [ModelZoo.listModels()](https://javadoc.io/static/ai.djl/api/0.16.0/ai/djl/repository/zoo/ModelZoo.html#listModels--) API to query available models.
+You can use [ModelZoo.listModels()](https://javadoc.io/static/ai.djl/api/0.17.0/ai/djl/repository/zoo/ModelZoo.html#listModels--) API to query available models.
 
 #### List available models using DJL command line
 

engines/dlr/dlr-engine/README.md

@@ -29,13 +29,13 @@ The javadocs output is generated in the `build/doc/javadoc` folder.
 ## Installation
 You can pull the DLR engine from the central Maven repository by including the following dependency:
 
-- ai.djl.dlr:dlr-engine:0.16.0
+- ai.djl.dlr:dlr-engine:0.17.0
 
 ```xml
 <dependency>
     <groupId>ai.djl.dlr</groupId>
     <artifactId>dlr-engine</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```

engines/ml/xgboost/README.md

@@ -37,13 +37,13 @@ XGBoost can only run on top of the Linux/Mac machine. User can build from source
 ## Installation
 You can pull the XGBoost engine from the central Maven repository by including the following dependency:
 
-- ai.djl.ml.xgboost:xgboost:0.16.0
+- ai.djl.ml.xgboost:xgboost:0.17.0
 
 ```xml
 <dependency>
     <groupId>ai.djl.ml.xgboost</groupId>
     <artifactId>xgboost</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```

engines/mxnet/mxnet-engine/README.md

@@ -7,7 +7,7 @@ This module contains the Deep Java Library (DJL) EngineProvider for Apache MXNet
 We don't recommend that developers use classes in this module directly. Use of these classes
 will couple your code with Apache MXNet and make switching between engines difficult. Even so,
 developers are not restricted from using engine-specific features. For more information,
-see [NDManager#invoke()](https://javadoc.io/static/ai.djl/api/0.16.0/ai/djl/ndarray/NDManager.html#invoke-java.lang.String-ai.djl.ndarray.NDArray:A-ai.djl.ndarray.NDArray:A-ai.djl.util.PairList-).
+see [NDManager#invoke()](https://javadoc.io/static/ai.djl/api/0.17.0/ai/djl/ndarray/NDManager.html#invoke-java.lang.String-ai.djl.ndarray.NDArray:A-ai.djl.ndarray.NDArray:A-ai.djl.util.PairList-).
 
 ## Documentation
 
@@ -32,7 +32,7 @@ You can pull the MXNet engine from the central Maven repository by including the
 <dependency>
     <groupId>ai.djl.mxnet</groupId>
     <artifactId>mxnet-engine</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```

engines/mxnet/mxnet-model-zoo/README.md

@@ -27,7 +27,7 @@ You can pull the MXNet engine from the central Maven repository by including the
 <dependency>
     <groupId>ai.djl.mxnet</groupId>
     <artifactId>mxnet-model-zoo</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
 </dependency>
 ```
 

engines/onnxruntime/onnxruntime-engine/README.md

@@ -37,13 +37,13 @@ for the official ONNX Runtime project.
 ## Installation
 You can pull the ONNX Runtime engine from the central Maven repository by including the following dependency:
 
-- ai.djl.onnxruntime:onnxruntime-engine:0.16.0
+- ai.djl.onnxruntime:onnxruntime-engine:0.17.0
 
 ```xml
 <dependency>
     <groupId>ai.djl.onnxruntime</groupId>
     <artifactId>onnxruntime-engine</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```
@@ -61,7 +61,7 @@ Maven:
 <dependency>
     <groupId>ai.djl.onnxruntime</groupId>
     <artifactId>onnxruntime-engine</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
     <scope>runtime</scope>
     <exclusions>
         <exclusion>
@@ -80,7 +80,7 @@ Maven:
 
 Gradle:
 ```
-    implementation("ai.djl.onnxruntime:onnxruntime-engine:0.16.0") {
+    implementation("ai.djl.onnxruntime:onnxruntime-engine:0.17.0") {
         exclude group: "com.microsoft.onnxruntime", module: "onnxruntime"
     }
     implementation "com.microsoft.onnxruntime:onnxruntime_gpu:1.11.0"

engines/paddlepaddle/paddlepaddle-model-zoo/README.md

@@ -26,7 +26,7 @@ from the central Maven repository by including the following dependency:
 <dependency>
     <groupId>ai.djl.paddlepaddle</groupId>
     <artifactId>paddlepaddle-model-zoo</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
 </dependency>
 ```
 

engines/pytorch/pytorch-engine/README.md

@@ -24,13 +24,13 @@ The javadocs output is built in the `build/doc/javadoc` folder.
 ## Installation
 You can pull the PyTorch engine from the central Maven repository by including the following dependency:
 
-- ai.djl.pytorch:pytorch-engine:0.16.0
+- ai.djl.pytorch:pytorch-engine:0.17.0
 
 ```xml
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-engine</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```

engines/pytorch/pytorch-model-zoo/README.md

@@ -25,7 +25,7 @@ You can pull the PyTorch engine from the central Maven repository by including t
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-model-zoo</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
 </dependency>
 ```
 

engines/tensorflow/tensorflow-api/README.md

@@ -16,6 +16,6 @@ You can pull the TensorFlow core java API from the central Maven repository by i
 <dependency>
     <groupId>ai.djl.tensorflow</groupId>
     <artifactId>tensorflow-api</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
 </dependency>
 ```

engines/tensorflow/tensorflow-engine/README.md

@@ -28,13 +28,13 @@ The javadocs output is built in the `build/doc/javadoc` folder.
 
 You can pull the TensorFlow engine from the central Maven repository by including the following dependency:
 
-- ai.djl.tensorflow:tensorflow-engine:0.16.0
+- ai.djl.tensorflow:tensorflow-engine:0.17.0
 
 ```xml
 <dependency>
     <groupId>ai.djl.tensorflow</groupId>
     <artifactId>tensorflow-engine</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```

engines/tensorflow/tensorflow-model-zoo/README.md

@@ -26,7 +26,7 @@ from the central Maven repository by including the following dependency:
 <dependency>
     <groupId>ai.djl.tensorflow</groupId>
     <artifactId>tensorflow-model-zoo</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
 </dependency>
 ```
 

engines/tensorrt/README.md

@@ -28,13 +28,13 @@ The javadocs output is generated in the `build/doc/javadoc` folder.
 ## Installation
 You can pull the TensorRT engine from the central Maven repository by including the following dependency:
 
-- ai.djl.tensorrt:tensorrt:0.16.0
+- ai.djl.tensorrt:tensorrt:0.17.0
 
 ```xml
 <dependency>
     <groupId>ai.djl.tensorrt</groupId>
     <artifactId>tensorrt</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```

engines/tflite/tflite-engine/README.md

@@ -24,13 +24,13 @@ The javadocs output is built in the `build/doc/javadoc` folder.
 ## Installation
 You can pull the TensorFlow Lite engine from the central Maven repository by including the following dependency:
 
-- ai.djl.tflite:tflite-engine:0.16.0
+- ai.djl.tflite:tflite-engine:0.17.0
 
 ```xml
 <dependency>
     <groupId>ai.djl.tflite</groupId>
     <artifactId>tflite-engine</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```

extensions/aws-ai/README.md

@@ -58,6 +58,6 @@ You can pull the module from the central Maven repository by including the follo
 <dependency>
     <groupId>ai.djl.aws</groupId>
     <artifactId>aws-ai</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
 </dependency>
 ```

extensions/benchmark/README.md

@@ -48,25 +48,25 @@ sudo snap alias djlbench djl-bench
 - Or download .deb package from S3
 
 ```
-curl -O https://publish.djl.ai/djl-bench/0.16.0/djl-bench_0.16.0-1_all.deb
-sudo dpkg -i djl-bench_0.16.0-1_all.deb
+curl -O https://publish.djl.ai/djl-bench/0.17.0/djl-bench_0.17.0-1_all.deb
+sudo dpkg -i djl-bench_0.17.0-1_all.deb
 ```
 
 For centOS or Amazon Linux 2
 
-You can download djl-bench zip file from [here](https://publish.djl.ai/djl-bench/0.16.0/benchmark-0.16.0.zip).
+You can download djl-bench zip file from [here](https://publish.djl.ai/djl-bench/0.17.0/benchmark-0.17.0.zip).
 
 ```
-curl -O https://publish.djl.ai/djl-bench/0.16.0/benchmark-0.16.0.zip
-unzip benchmark-0.16.0.zip
-rm benchmark-0.16.0.zip
-sudo ln -s $PWD/benchmark-0.16.0/bin/benchmark /usr/bin/djl-bench
+curl -O https://publish.djl.ai/djl-bench/0.17.0/benchmark-0.17.0.zip
+unzip benchmark-0.17.0.zip
+rm benchmark-0.17.0.zip
+sudo ln -s $PWD/benchmark-0.17.0/bin/benchmark /usr/bin/djl-bench
 ```
 
 For Windows
 
 We are considering to create a `chocolatey` package for Windows. For the time being, you can
-download djl-bench zip file from [here](https://publish.djl.ai/djl-bench/0.16.0/benchmark-0.16.0.zip).
+download djl-bench zip file from [here](https://publish.djl.ai/djl-bench/0.17.0/benchmark-0.17.0.zip).
 
 Or you can run benchmark using gradle:
 

extensions/benchmark/snapcraft/snapcraft.yaml

@@ -1,5 +1,5 @@
 name: djlbench
-version: '0.16.0'
+version: '0.17.0'
 title: DJL Benhmark
 license: Apache-2.0
 summary: A machine learning benchmarking toolkit

extensions/fasttext/README.md

@@ -34,7 +34,7 @@ You can pull the fastText engine from the central Maven repository by including
 <dependency>
     <groupId>ai.djl.fasttext</groupId>
     <artifactId>fasttext-engine</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
 </dependency>
 ```
 

extensions/hadoop/README.md

@@ -52,6 +52,6 @@ You can pull the module from the central Maven repository by including the follo
 <dependency>
     <groupId>ai.djl.hadoop</groupId>
     <artifactId>hadoop</artifactId>
-    <version>0.16.0</version>
+    <version>0.17.0</version>
 </dependency>
 ```