Replace links to gh master with v0.3.0

Change-Id: I32ad7fe3ffc8c2d05d2280f6513f84e493aa915d

api/src/main/java/ai/djl/inference/Predictor.java

@@ -39,18 +39,18 @@
  *
  * <ul>
  *   <li><a
- *       href="https://github.com/awslabs/djl/blob/master/jupyter/tutorial/image_classification_with_your_model.ipynb">Inference
+ *       href="https://github.com/awslabs/djl/blob/v0.3.0/jupyter/tutorial/image_classification_with_your_model.ipynb">Inference
  *       with a custom trained model</a>
  *   <li><a
- *       href="https://github.com/awslabs/djl/blob/master/jupyter/object_detection_with_model_zoo.ipynb">Inference
+ *       href="https://github.com/awslabs/djl/blob/v0.3.0/jupyter/object_detection_with_model_zoo.ipynb">Inference
  *       with a model zoo model</a>
  *   <li><a
- *       href="https://github.com/awslabs/djl/blob/master/jupyter/load_mxnet_model.ipynb">Inference
+ *       href="https://github.com/awslabs/djl/blob/v0.3.0/jupyter/load_mxnet_model.ipynb">Inference
  *       with an MXNet model</a>
  * </ul>
  *
  * <p>For information about running multi-threaded inference, see <a
- * href="https://github.com/awslabs/djl/blob/master/examples/docs/multithread_inference.md">here</a>.
+ * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/multithread_inference.md">here</a>.
  *
  * @param <I> the input type
  * @param <O> the output type

api/src/main/java/ai/djl/metric/Metrics.java

@@ -35,7 +35,7 @@
  * latencies, CPU and GPU memory consumption, losses, etc.
  *
  * <p>For more details about using the metrics, see the <a
- * href="https://github.com/awslabs/djl/blob/master/docs/how_to_collect_metrics.md">metrics
+ * href="https://github.com/awslabs/djl/blob/v0.3.0/docs/how_to_collect_metrics.md">metrics
  * tutorial</a>.
  */
 public class Metrics {

api/src/main/java/ai/djl/nn/Block.java

@@ -100,7 +100,7 @@
  * fully-trained model.
  *
  * <p>See this tutorial on <a
- * href="https://github.com/awslabs/djl/blob/master/jupyter/tutorial/create_your_first_network.ipynb">creating
+ * href="https://github.com/awslabs/djl/blob/v0.3.0/jupyter/tutorial/create_your_first_network.ipynb">creating
  * your first network</a>.
  */
 public interface Block {

api/src/main/java/ai/djl/training/Trainer.java

@@ -34,13 +34,13 @@
  *
  * <ul>
  *   <li><a
- *       href="https://github.com/awslabs/djl/blob/master/jupyter/tutorial/train_your_first_model.ipynb">Training
+ *       href="https://github.com/awslabs/djl/blob/v0.3.0/jupyter/tutorial/train_your_first_model.ipynb">Training
  *       your first model</a>
  *   <li><a
- *       href="https://github.com/awslabs/djl/blob/master/jupyter/transfer_learning_on_cifar10.ipynb">Training
+ *       href="https://github.com/awslabs/djl/blob/v0.3.0/jupyter/transfer_learning_on_cifar10.ipynb">Training
  *       using transfer learning</a>
  *   <li><a
- *       href="https://github.com/awslabs/djl/blob/master/jupyter/load_mxnet_model.ipynb">Inference
+ *       href="https://github.com/awslabs/djl/blob/v0.3.0/jupyter/load_mxnet_model.ipynb">Inference
  *       with an MXNet model</a>
  * </ul>
  */

docs/faq.md

@@ -32,18 +32,18 @@ setting the devices. For example, if you have 7 GPUs available, and you want the
             // Set the devices to run on multi-GPU
             .setDevices(Device.getDevices(numberOfGpus));
 All of the examples in the example folder can be run on 
-multiple GPUs with the appropriate arguments. Follow the steps in the example to [train a ResNet50 model on CIFAR-10 dataset](https://github.com/awslabs/djl/blob/master/examples/docs/train_cifar10_resnet.md#train-using-multiple-gpus) on a GPU.
+multiple GPUs with the appropriate arguments. Follow the steps in the example to [train a ResNet50 model on CIFAR-10 dataset](https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/train_cifar10_resnet.md#train-using-multiple-gpus) on a GPU.
 
 ##### 5. Does DJL support inference on multiple threads?
 Yes. DJL offers multi-threaded inference. If using the MXNet engine for a multi-threaded inference case, you need to 
 specify the 'MXNET_ENGINE_TYPE' environment variable to 'NaiveEngine'. For more information, see the
-[Multi-threaded inference example](https://github.com/awslabs/djl/blob/master/examples/docs/multithread_inference.md).
+[Multi-threaded inference example](https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/multithread_inference.md).
 
 ##### 6. Does DJL support distributed training?
 DJL does not currently support distributed training.
 
 ##### 7. Can I run DJL on other versions of MxNet?
-This is not officially supported by DJL, but you can follow the steps outlined in the [troubleshooting document](https://github.com/awslabs/djl/blob/master/docs/development/troubleshooting.md#3-how-to-run-djl-using-other-versions-of-mxnet)
+This is not officially supported by DJL, but you can follow the steps outlined in the [troubleshooting document](https://github.com/awslabs/djl/blob/v0.3.0/docs/development/troubleshooting.md#3-how-to-run-djl-using-other-versions-of-mxnet)
 to use other versions of MXNet or built your own customized version.
 
 ##### 8. I have a model trained and saved by another DL engine. Can I load that model on to DJL?

examples/build.gradle

@@ -16,7 +16,7 @@ dependencies {
     implementation "ai.djl:model-zoo:0.3.0"
     implementation "ai.djl.mxnet:mxnet-model-zoo:0.3.0"
 
-    // See https://github.com/awslabs/djl/blob/master/mxnet/mxnet-engine/README.md for more MXNet library selection options
+    // See https://github.com/awslabs/djl/blob/v0.3.0/mxnet/mxnet-engine/README.md for more MXNet library selection options
     runtimeOnly "ai.djl.mxnet:mxnet-native-auto:1.6.0-SNAPSHOT"
 
     testImplementation 'org.testng:testng:6.14.3'

examples/docs/train_mnist_mlp.md

@@ -4,7 +4,7 @@ Training a model on a handwritten digit dataset, such as ([MNIST](http://yann.le
 
 In this example, you learn how to train the MNIST dataset with Deep Java Library (DJL) to recognize handwritten digits in an image.
 
-The source code for this example can be found at [TrainMnist.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/training/TrainMnist.java).
+The source code for this example can be found at [TrainMnist.java](https://github.com/awslabs/djl/blob/v0.3.0/examples/src/main/java/ai/djl/examples/training/TrainMnist.java).
 
 You can also use the [Jupyter notebook tutorial](../../jupyter/tutorial/train_your_first_model.ipynb).
 The Jupyter notebook explains the key concepts in detail.

examples/pom.xml

@@ -55,7 +55,7 @@
         </dependency>
         <dependency>
             <!--
-                See https://github.com/awslabs/djl/blob/master/mxnet/mxnet-engine/README.md for more MXNet library selection options
+                See https://github.com/awslabs/djl/blob/v0.3.0/mxnet/mxnet-engine/README.md for more MXNet library selection options
             -->
             <groupId>ai.djl.mxnet</groupId>
             <artifactId>mxnet-native-auto</artifactId>

examples/src/main/java/ai/djl/examples/inference/ActionRecognition.java

@@ -33,7 +33,7 @@
  * An example of inference using an action recognition model.
  *
  * <p>See this <a
- * href="https://github.com/awslabs/djl/blob/master/examples/docs/action_recognition.md">doc</a> for
+ * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/action_recognition.md">doc</a> for
  * information about this example.
  */
 public final class ActionRecognition {

examples/src/main/java/ai/djl/examples/inference/BertQaInference.java

@@ -32,10 +32,10 @@
  * <p>See:
  *
  * <ul>
- *   <li>the <a href="https://github.com/awslabs/djl/blob/master/jupyter/BERTQA.ipynb">jupyter
+ *   <li>the <a href="https://github.com/awslabs/djl/blob/v0.3.0/jupyter/BERTQA.ipynb">jupyter
  *       demo</a> with more information about BERT.
  *   <li>the <a
- *       href="https://github.com/awslabs/djl/blob/master/examples/docs/BERT_question_and_answer.md">docs</a>
+ *       href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/BERT_question_and_answer.md">docs</a>
  *       for information about running this example.
  * </ul>
  */

examples/src/main/java/ai/djl/examples/inference/ImageClassification.java

@@ -39,7 +39,7 @@
  * An example of inference using an image classification model.
  *
  * <p>See this <a
- * href="https://github.com/awslabs/djl/blob/master/examples/docs/image_classification.md">doc</a>
+ * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/image_classification.md">doc</a>
  * for information about this example.
  */
 public final class ImageClassification {

examples/src/main/java/ai/djl/examples/inference/InstanceSegmentation.java

@@ -37,7 +37,7 @@
  * An example of inference using an instance segmentation model.
  *
  * <p>See this <a
- * href="https://github.com/awslabs/djl/blob/master/examples/docs/instance_segmentation.md">doc</a>
+ * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/instance_segmentation.md">doc</a>
  * for information about this example.
  */
 public final class InstanceSegmentation {

examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java

@@ -37,7 +37,7 @@
  * An example of inference using an object detection model.
  *
  * <p>See this <a
- * href="https://github.com/awslabs/djl/blob/master/examples/docs/object_detection.md">doc</a> for
+ * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/object_detection.md">doc</a> for
  * information about this example.
  */
 public final class ObjectDetection {

examples/src/main/java/ai/djl/examples/inference/PoseEstimation.java

@@ -41,7 +41,7 @@
  * An example of inference using a pose estimation model.
  *
  * <p>See this <a
- * href="https://github.com/awslabs/djl/blob/master/examples/docs/pose_estimation.md">doc</a> for
+ * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/pose_estimation.md">doc</a> for
  * information about this example.
  */
 public final class PoseEstimation {

examples/src/main/java/ai/djl/examples/training/TrainCaptcha.java

@@ -43,7 +43,7 @@
  * An example of training a CAPTCHA solving model.
  *
  * <p>See this <a
- * href="https://github.com/awslabs/djl/blob/master/examples/docs/train_captcha.md">doc</a> for
+ * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/train_captcha.md">doc</a> for
  * information about this example.
  */
 public final class TrainCaptcha {

examples/src/main/java/ai/djl/examples/training/TrainMnist.java

@@ -38,7 +38,7 @@
  * An example of training an image classification (MNIST) model.
  *
  * <p>See this <a
- * href="https://github.com/awslabs/djl/blob/master/examples/docs/train_mnist_mlp.md">doc</a> for
+ * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/train_mnist_mlp.md">doc</a> for
  * information about this example.
  */
 public final class TrainMnist {

examples/src/main/java/ai/djl/examples/training/TrainPikachu.java

@@ -59,7 +59,7 @@
  * An example of training a simple Single Shot Detection (SSD) model.
  *
  * <p>See this <a
- * href="https://github.com/awslabs/djl/blob/master/examples/docs/train_pikachu_ssd.md">doc</a> for
+ * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/train_pikachu_ssd.md">doc</a> for
  * information about this example.
  */
 public final class TrainPikachu {

examples/src/main/java/ai/djl/examples/training/transferlearning/TrainResnetWithCifar10.java

@@ -57,7 +57,7 @@
  * An example of training an image classification (ResNet for Cifar10) model.
  *
  * <p>See this <a
- * href="https://github.com/awslabs/djl/blob/master/examples/docs/train_cifar10_resnet.md">doc</a>
+ * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/train_cifar10_resnet.md">doc</a>
  * for information about this example.
  */
 public final class TrainResnetWithCifar10 {

jupyter/BERTQA.ipynb

@@ -33,7 +33,7 @@
    "source": [
     "## Preparation\n",
     "\n",
-    "This tutorial requires the installation of Java Kernel. To install the Java Kernel, see the [README](https://github.com/awslabs/djl/blob/master/jupyter/README.md)."
+    "This tutorial requires the installation of Java Kernel. To install the Java Kernel, see the [README](https://github.com/awslabs/djl/blob/v0.3.0/jupyter/README.md)."
    ]
   },
   {
@@ -50,7 +50,7 @@
     "%maven org.slf4j:slf4j-simple:1.7.26\n",
     "%maven net.java.dev.jna:jna:5.3.0\n",
     "        \n",
-    "// See https://github.com/awslabs/djl/blob/master/mxnet/mxnet-engine/README.md\n",
+    "// See https://github.com/awslabs/djl/blob/v0.3.0/mxnet/mxnet-engine/README.md\n",
     "// for more MXNet library selection options\n",
     "%maven ai.djl.mxnet:mxnet-native-auto:1.6.0-SNAPSHOT"
    ]
@@ -177,7 +177,7 @@
     "DJL abstracts away the whole process for ease of use. It can load the model, perform inference on the input, and provide \n",
     "output. DJL also allows you to provide user-defined inputs. The workflow looks like the following:\n",
     "\n",
-    "![image](https://github.com/awslabs/djl/blob/master/examples/docs/img/workFlow.png?raw=true)\n",
+    "![image](https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/img/workFlow.png?raw=true)\n",
     "\n",
     "The red block (\"Images\") in the workflow is the input that DJL expects from you. The green block (\"Images \n",
     "bounding box\") is the output that you expect. Because DJL does not know which input to expect and which output format that you prefer, DJL provides the `Translator` interface so you can define your own \n",

jupyter/load_mxnet_model.ipynb

@@ -11,7 +11,7 @@
     "\n",
     "## Preparation\n",
     "\n",
-    "This tutorial requires the installation of Java Kernel. For more information on installing the Java Kernel, see the [README](https://github.com/awslabs/djl/blob/master/jupyter/README.md) to install Java Kernel."
+    "This tutorial requires the installation of Java Kernel. For more information on installing the Java Kernel, see the [README](https://github.com/awslabs/djl/blob/v0.3.0/jupyter/README.md) to install Java Kernel."
    ]
   },
   {
@@ -29,7 +29,7 @@
     "%maven org.slf4j:slf4j-simple:1.7.26\n",
     "%maven net.java.dev.jna:jna:5.3.0\n",
     "        \n",
-    "// See https://github.com/awslabs/djl/blob/master/mxnet/mxnet-engine/README.md\n",
+    "// See https://github.com/awslabs/djl/blob/v0.3.0/mxnet/mxnet-engine/README.md\n",
     "// for more MXNet library selection options\n",
     "%maven ai.djl.mxnet:mxnet-native-auto:1.6.0-SNAPSHOT"
    ]

jupyter/object_detection_with_model_zoo.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "## Preparation\n",
     "\n",
-    "This tutorial requires the installation of Java Kernel. To install Java Kernel, see the [README](https://github.com/awslabs/djl/blob/master/jupyter/README.md)."
+    "This tutorial requires the installation of Java Kernel. To install Java Kernel, see the [README](https://github.com/awslabs/djl/blob/v0.3.0/jupyter/README.md)."
    ]
   },
   {
@@ -27,7 +27,7 @@
     "%maven org.slf4j:slf4j-simple:1.7.26\n",
     "%maven net.java.dev.jna:jna:5.3.0\n",
     "        \n",
-    "// See https://github.com/awslabs/djl/blob/master/mxnet/mxnet-engine/README.md\n",
+    "// See https://github.com/awslabs/djl/blob/v0.3.0/mxnet/mxnet-engine/README.md\n",
     "// for more MXNet library selection options\n",
     "%maven ai.djl.mxnet:mxnet-native-auto:1.6.0-SNAPSHOT"
    ]
@@ -71,7 +71,7 @@
     "## Step 2: Load model zoo model\n",
     "\n",
     "In this example, you load a SSD (Single Shot MultiBox Detector) model from the MXNet model zoo.\n",
-    "For more information about model zoo, see the [Model Zoo Documentation](https://github.com/awslabs/djl/blob/master/docs/model-zoo.md) "
+    "For more information about model zoo, see the [Model Zoo Documentation](https://github.com/awslabs/djl/blob/v0.3.0/docs/model-zoo.md) "
    ]
   },
   {
@@ -132,7 +132,7 @@
     "var detections = model.newPredictor().predict(img);\n",
     "```\n",
     "\n",
-    "You can find full SsdExample source code [here](https://github.com/awslabs/djl/blob/master/examples/docs/object_detection.md).\n"
+    "You can find full SsdExample source code [here](https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/object_detection.md).\n"
    ]
   }
  ],

jupyter/transfer_learning_on_cifar10.ipynb

@@ -19,8 +19,8 @@
     "\n",
     "## Pre-requisites\n",
     "This tutorial assumes you have the following knowledge. Follow the READMEs and tutorials if you are not familiar with:\n",
-    "1. How to setup and run [Java Kernel in Jupyter Notebook](https://github.com/awslabs/djl/blob/master/jupyter/README.md)\n",
-    "2. Basic components of Deep Java Library, and how to [train your first model](https://github.com/awslabs/djl/blob/master/jupyter/tutorial/train_your_first_model.ipynb).\n",
+    "1. How to setup and run [Java Kernel in Jupyter Notebook](https://github.com/awslabs/djl/blob/v0.3.0/jupyter/README.md)\n",
+    "2. Basic components of Deep Java Library, and how to [train your first model](https://github.com/awslabs/djl/blob/v0.3.0/jupyter/tutorial/train_your_first_model.ipynb).\n",
     "\n",
     "\n",
     "## Getting started\n",
@@ -43,7 +43,7 @@
     "%maven org.slf4j:slf4j-simple:1.7.26\n",
     "%maven net.java.dev.jna:jna:5.3.0\n",
     "        \n",
-    "// See https://github.com/awslabs/djl/blob/master/mxnet/mxnet-engine/README.md\n",
+    "// See https://github.com/awslabs/djl/blob/v0.3.0/mxnet/mxnet-engine/README.md\n",
     "// for more MXNet library selection options\n",
     "%maven ai.djl.mxnet:mxnet-native-auto:1.6.0-SNAPSHOT"
    ]
@@ -91,7 +91,7 @@
    "source": [
     "## Construct your model\n",
     "\n",
-    "Load the pre-trained ResNet50V1 model. You can find it in the [Model Zoo](https://github.com/awslabs/djl/blob/master/docs/model-zoo.md). First construct the `criteria` to specify which ResNet model to load, then call `loadModel` to get a ResNet50V1 model with pre-trained weights. Note this model was trained on ImageNet with 1000 classes; the last layer is a Linear layer with 1000 output channels. Because you are repurposing it on CIFAR10 with 10 classes, you need to remove the last layer and add a new Linear layer with 10 output channels. After you are done modifying the block, set it back to model using `setBlock`."
+    "Load the pre-trained ResNet50V1 model. You can find it in the [Model Zoo](https://github.com/awslabs/djl/blob/v0.3.0/docs/model-zoo.md). First construct the `criteria` to specify which ResNet model to load, then call `loadModel` to get a ResNet50V1 model with pre-trained weights. Note this model was trained on ImageNet with 1000 classes; the last layer is a Linear layer with 1000 output channels. Because you are repurposing it on CIFAR10 with 10 classes, you need to remove the last layer and add a new Linear layer with 10 output channels. After you are done modifying the block, set it back to model using `setBlock`."
    ]
   },
   {
@@ -190,7 +190,7 @@
    "metadata": {},
    "source": [
     "## Train your model\n",
-    "Now you can start training. This procedure is similar to the one in [Train Your First Model](https://github.com/awslabs/djl/blob/master/jupyter/tutorial/train_your_first_model.ipynb). Training requires the following steps:\n",
+    "Now you can start training. This procedure is similar to the one in [Train Your First Model](https://github.com/awslabs/djl/blob/v0.3.0/jupyter/tutorial/train_your_first_model.ipynb). Training requires the following steps:\n",
     "1. Initialize a new trainer using the training config you just set up\n",
     "2. Initialize the weights in trainer\n",
     "3. Using a `for` loop to iterate through the whole dataset 10 times (epochs), resetting the evaluators at the end of each epoch\n",
@@ -280,7 +280,7 @@
    "source": [
     "## What's next\n",
     "\n",
-    "1. Try inference using the model you just trained. You can find an airplane image in [test resources](https://github.com/awslabs/djl/blob/master/examples/src/test/resources/airplane1.png) and follow the inference tutorials in the [Jupyter module](https://github.com/awslabs/djl/tree/master/jupyter).\n",
+    "1. Try inference using the model you just trained. You can find an airplane image in [test resources](https://github.com/awslabs/djl/blob/v0.3.0/examples/src/test/resources/airplane1.png) and follow the inference tutorials in the [Jupyter module](https://github.com/awslabs/djl/tree/v0.3.0/jupyter).\n",
     "\n",
     "2. Follow the complete example with multi-GPU support, a validation dataset, and the fit API in the [examples module](https://github.com/awslabs/djl/tree/master/examples/docs).\n"
    ]