[docs] Updates document to use DJL 0.29.0 (#3351)

README.md

@@ -85,14 +85,15 @@ The following pseudocode demonstrates running training:
 
 ## Release Notes
 
+* [0.29.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.29.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.29.0))
 * [0.28.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.28.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.28.0))
 * [0.27.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.27.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.27.0))
 * [0.26.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.26.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.26.0))
 * [0.25.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.25.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.25.0))
 * [0.24.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.24.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.24.0))
-* [+24 releases](https://github.com/deepjavalibrary/djl/releases)
+* [+25 releases](https://github.com/deepjavalibrary/djl/releases)
 
-The release of DJL 0.29.0 is planned for July 2024.
+The release of DJL 0.30.0 is planned for Sep 2024.
 
 ## Building From Source
 

bom/README.md

@@ -65,7 +65,7 @@ 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:0.28.0")
+    implementation platform("ai.djl:bom:0.29.0")
 ```
 
 - Then you import the desired DJL modules into to you pom.xml file (no version is needed):

docs/development/example_dataset.md

@@ -24,8 +24,8 @@ 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:0.28.0"
-api "ai.djl:basicdataset:0.28.0"
+api "ai.djl:api:0.29.0"
+api "ai.djl:basicdataset:0.29.0"
 ```
 
 There are four parts we need to implement for CSVDataset.

docs/get.md

@@ -99,7 +99,7 @@ dependencies {
     implementation platform("ai.djl:bom:<UPCOMING VERSION>-SNAPSHOT")
 }
 ```
-Currently, the `<UPCOMING VERSION> = 0.28.0`.
+Currently, the `<UPCOMING VERSION> = 0.29.0`.
 This snapshot version is the same as the custom DJL repository. 
 
 You also need to change directory to `djl/bom`. Then build and publish it to maven local same as what was done in `djl`.

docs/hybrid_engine.md

@@ -21,17 +21,17 @@ 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:0.28.0"
+runtimeOnly "ai.djl.mxnet:mxnet-engine:0.29.0"
 ```
 
 You can also use PyTorch or TensorFlow Engine as the supplemental engine by adding their corresponding dependencies.
 
 ```
-runtimeOnly "ai.djl.pytorch:pytorch-engine:0.28.0"
+runtimeOnly "ai.djl.pytorch:pytorch-engine:0.29.0"
 ```
 
 ```
-runtimeOnly "ai.djl.tensorflow:tensorflow-engine:0.28.0"
+runtimeOnly "ai.djl.tensorflow:tensorflow-engine:0.29.0"
 ```
 
 ## How Hybrid works

engines/ml/lightgbm/README.md

@@ -36,7 +36,7 @@ LightGBM can only run on top of the Linux/Mac/Windows machine using x86_64.
 ## Installation
 You can pull the LightGBM engine from the central Maven repository by including the following dependency:
 
-- ai.djl.ml.lightgbm:lightgbm:0.28.0
+- ai.djl.ml.lightgbm:lightgbm:0.29.0
 
 ```xml
 <dependency>

engines/ml/xgboost/README.md

@@ -37,7 +37,7 @@ 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:0.28.0
+- ai.djl.ml.xgboost:xgboost:0.29.0
 
 ```xml
 <dependency>

engines/onnxruntime/onnxruntime-engine/README.md

@@ -37,7 +37,7 @@ 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:0.28.0
+- ai.djl.onnxruntime:onnxruntime-engine:0.29.0
 
 ```xml
 <dependency>
@@ -73,18 +73,18 @@ Maven:
 <dependency>
     <groupId>com.microsoft.onnxruntime</groupId>
     <artifactId>onnxruntime_gpu</artifactId>
-    <version>1.17.1</version>
+    <version>1.18.0</version>
     <scope>runtime</scope>
 </dependency>
 ```
 
 Gradle:
 
 ```groovy
-implementation("ai.djl.onnxruntime:onnxruntime-engine:0.28.0") {
+implementation("ai.djl.onnxruntime:onnxruntime-engine:0.29.0") {
     exclude group: "com.microsoft.onnxruntime", module: "onnxruntime"
 }
-implementation "com.microsoft.onnxruntime:onnxruntime_gpu:1.17.1"
+implementation "com.microsoft.onnxruntime:onnxruntime_gpu:1.18.0"
 ```
 
 #### Enable TensorRT execution

engines/tensorflow/tensorflow-engine/README.md

@@ -28,7 +28,7 @@ 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:0.28.0
+- ai.djl.tensorflow:tensorflow-engine:0.29.0
 
 ```xml
 <dependency>

engines/tensorrt/README.md

@@ -28,7 +28,7 @@ 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:0.28.0
+- ai.djl.tensorrt:tensorrt:0.29.0
 
 ```xml
 <dependency>

extensions/timeseries/docs/forecast_with_M5_data.md

@@ -56,7 +56,7 @@ repositories {
 }
 dependencies {
   implementation "org.apache.logging.log4j:log4j-slf4j-impl:2.17.1"
-  implementation platform("ai.djl:bom:0.28.0")
+  implementation platform("ai.djl:bom:0.29.0")
   implementation "ai.djl:api"
   implementation "ai.djl.timeseries"
   runtimeOnly "ai.djl.mxnet:mxnet-engine"