deepjavalibrary · frankfliu · Aug 12, 2024 · Aug 11, 2024
@@ -151,7 +151,7 @@ Follow our [<img src='https://cdn2.iconfinder.com/data/icons/social-media-2285/5
 
 * [DJL Website](https://djl.ai/)
 * [Documentation](https://docs.djl.ai/)
-* [DJL Demos](https://docs.djl.ai/docs/demos/index.html)
+* [DJL Demos](https://docs.djl.ai/master/docs/demos/index.html)
 * [Dive into Deep Learning Book Java version](https://d2l.djl.ai/)
 
 ## License

@@ -40,7 +40,7 @@ tasks {
                 "Notice" to "DJL will collect telemetry to help us better understand our users'" +
                         " needs, diagnose issues, and deliver additional features. If you would" +
                         " like to learn more or opt-out please go to: " +
-                        "https://docs.djl.ai/docs/telemetry.html for more information."
+                        "https://docs.djl.ai/master/docs/telemetry.html for more information."
             )
         }
     }

@@ -48,10 +48,10 @@
  * contain methods to detect information about the usable machine hardware and to create a new
  * {@link NDManager} and {@link Model}.
  *
- * @see <a href="http://docs.djl.ai/docs/engine.html">Engine Guide</a>
+ * @see <a href="https://docs.djl.ai/master/docs/engine.html">Engine Guide</a>
  * @see EngineProvider
- * @see <a href="http://docs.djl.ai/docs/development/cache_management.html">The guide on resource
- *     and engine caching</a>
+ * @see <a href="https://docs.djl.ai/master/docs/development/cache_management.html">The guide on
+ *     resource and engine caching</a>
  */
 public abstract class Engine {
 

@@ -61,25 +61,26 @@
  *
  * <ul>
  *   <li><a
- *       href="http://docs.djl.ai/docs/demos/jupyter/tutorial/03_image_classification_with_your_model.html">Inference
+ *       href="https://docs.djl.ai/master/docs/demos/jupyter/tutorial/03_image_classification_with_your_model.html">Inference
  *       with a custom trained model</a>
  *   <li><a
- *       href="http://docs.djl.ai/docs/demos/jupyter/object_detection_with_model_zoo.html">Inference
+ *       href="https://docs.djl.ai/master/docs/demos/jupyter/object_detection_with_model_zoo.html">Inference
  *       with a model zoo model</a>
- *   <li><a href="http://docs.djl.ai/docs/demos/jupyter/load_mxnet_model.html">Inference with an
- *       MXNet model</a>
+ *   <li><a href="https://docs.djl.ai/master/docs/demos/jupyter/load_mxnet_model.html">Inference
+ *       with an MXNet model</a>
  * </ul>
  *
  * @param <I> the input type
  * @param <O> the output type
  * @see Model
  * @see Translator
- * @see <a href="http://docs.djl.ai/docs/development/memory_management.html">The guide on memory
- *     management</a>
+ * @see <a href="https://docs.djl.ai/master/docs/development/memory_management.html">The guide on
+ *     memory management</a>
  * @see <a
  *     href="https://github.com/deepjavalibrary/djl/blob/master/examples/docs/multithread_inference.md">The
  *     guide on running multi-threaded inference</a>
- * @see <a href="http://docs.djl.ai/docs/development/inference_performance_optimization.html">The
+ * @see <a
+ *     href="https://docs.djl.ai/master/docs/development/inference_performance_optimization.html">The
  *     guide on inference performance optimization</a>
  */
 public class Predictor<I, O> implements AutoCloseable {

@@ -105,7 +105,7 @@
  * further refine these elements, use {@link Block#freezeParameters(boolean)} to unfreeze them.
  *
  * @see <a
- *     href="http://docs.djl.ai/docs/demos/jupyter/tutorial/01_create_your_first_network.html">this
+ *     href="https://docs.djl.ai/master/docs/demos/jupyter/tutorial/01_create_your_first_network.html">this
  *     tutorial on creating your first network</a>
  * @see <a href="https://d2l.djl.ai/chapter_deep-learning-computation/model-construction.html">The
  *     D2L chapter on blocks</a> and <a

@@ -53,8 +53,8 @@
  *         .build();
  * </pre>
  *
- * <p>See <a href="http://docs.djl.ai/docs/load_model.html#criteria-class">Model loading</a> for
- * more detail.
+ * <p>See <a href="https://docs.djl.ai/master/docs/load_model.html#criteria-class">Model loading</a>
+ * for more detail.
  *
  * @param <I> the model input type
  * @param <O> the model output type

@@ -52,16 +52,17 @@
  *
  * <ul>
  *   <li><a
- *       href="http://docs.djl.ai/docs/demos/jupyter/tutorial/02_train_your_first_model.html">Training
+ *       href="https://docs.djl.ai/master/docs/demos/jupyter/tutorial/02_train_your_first_model.html">Training
  *       your first model</a>
- *   <li><a href="http://docs.djl.ai/docs/demos/jupyter/transfer_learning_on_cifar10.html">Training
+ *   <li><a
+ *       href="https://docs.djl.ai/master/docs/demos/jupyter/transfer_learning_on_cifar10.html">Training
  *       using transfer learning</a>
- *   <li><a href="http://docs.djl.ai/docs/demos/jupyter/load_mxnet_model.html">Inference with an
- *       MXNet model</a>
+ *   <li><a href="https://docs.djl.ai/master/docs/demos/jupyter/load_mxnet_model.html">Inference
+ *       with an MXNet model</a>
  * </ul>
  *
- * @see <a href="http://docs.djl.ai/docs/development/memory_management.html">The guide on memory
- *     management</a>
+ * @see <a href="https://docs.djl.ai/master/docs/development/memory_management.html">The guide on
+ *     memory management</a>
  */
 public class Trainer implements AutoCloseable {
 

@@ -23,8 +23,8 @@
 /**
  * An interface to represent a set of sample data/label pairs to train a model.
  *
- * @see <a href="http://docs.djl.ai/docs/dataset.html">The guide to datasets</a>
- * @see <a href="http://docs.djl.ai/docs/development/how_to_use_dataset.html">The guide to
+ * @see <a href="https://docs.djl.ai/master/docs/dataset.html">The guide to datasets</a>
+ * @see <a href="https://docs.djl.ai/master/docs/development/how_to_use_dataset.html">The guide to
  *     implementing a custom dataset</a>
  */
 public interface Dataset {

@@ -42,7 +42,7 @@
  *
  * <p>Almost all datasets in DJL extend, either directly or indirectly, {@link RandomAccessDataset}.
  *
- * @see <a href="http://docs.djl.ai/docs/development/how_to_use_dataset.html">The guide to
+ * @see <a href="https://docs.djl.ai/master/docs/development/how_to_use_dataset.html">The guide to
  *     implementing a custom dataset</a>
  */
 public abstract class RandomAccessDataset implements Dataset {

@@ -29,8 +29,8 @@
 /**
  * {@link TrainingListener} that outputs the progress of training each batch and epoch into logs.
  *
- * @see <a href="http://docs.djl.ai/docs/development/configure_logging.html">The guide on DJL
- *     logging</a>
+ * @see <a href="https://docs.djl.ai/master/docs/development/configure_logging.html">The guide on
+ *     DJL logging</a>
  */
 public class LoggingTrainingListener implements TrainingListener {
 

@@ -140,7 +140,8 @@ public static void callHome(String engine) {
                         "DJL will collect telemetry to help us better understand our users' needs,"
                             + " diagnose issues, and deliver additional features. If you would like"
                             + " to learn more or opt-out please go to:"
-                            + " https://docs.djl.ai/docs/telemetry.html for more information.");
+                            + " https://docs.djl.ai/master/docs/telemetry.html for more"
+                            + " information.");
             }
         } catch (IOException e) {
             logger.debug("Failed call home.");

@@ -4,7 +4,7 @@
 
 This module contains a number of basic and standard datasets in the Deep Java Library's (DJL). These datasets are used to train deep learning models.
 
-You can find the datasets provided by this module on our [docs](http://docs.djl.ai/docs/dataset.html).
+You can find the datasets provided by this module on our [docs](https://docs.djl.ai/master/docs/dataset.html).
 
 ## Documentation
 

@@ -29,7 +29,7 @@ public static void basic() {
         if (!ModelZoo.hasModelZoo("ai.djl.zoo")) {
             throw new IllegalStateException(
                     "The basic model zoo is required, but not found.Please install it by following"
-                            + " http://docs.djl.ai/model-zoo/index.html#installation");
+                            + " https://docs.djl.ai/model-zoo/index.html#installation");
         }
     }
 
@@ -38,12 +38,12 @@ public static void mxnet() {
         if (!ModelZoo.hasModelZoo("ai.djl.mxnet")) {
             throw new IllegalStateException(
                     "The MXNet model zoo is required, but not found.Please install it by following"
-                        + " http://docs.djl.ai/engines/mxnet/mxnet-model-zoo/index.html#installation");
+                        + " https://docs.djl.ai/master/engines/mxnet/mxnet-model-zoo/index.html#installation");
         }
         if (!Engine.hasEngine("MXNet")) {
             throw new IllegalStateException(
                     "The MXNet engine is required, but not found.Please install it by following"
-                        + " http://docs.djl.ai/engines/mxnet/mxnet-engine/index.html#installation");
+                        + " https://docs.djl.ai/master/engines/mxnet/mxnet-engine/index.html#installation");
         }
     }
 
@@ -53,12 +53,12 @@ public static void pytorch() {
             throw new IllegalStateException(
                     "The PyTorch model zoo is required, but not found.Please install it by"
                         + " following"
-                        + " http://docs.djl.ai/pytorch/pytorch-model-zoo/index.html#installation");
+                        + " https://docs.djl.ai/master/pytorch/pytorch-model-zoo/index.html#installation");
         }
         if (!Engine.hasEngine("PyTorch")) {
             throw new IllegalStateException(
                     "The PyTorch engine is required, but not found.Please install it by following"
-                            + " http://docs.djl.ai/pytorch/pytorch-engine/index.html#installation");
+                        + " https://docs.djl.ai/master/pytorch/pytorch-engine/index.html#installation");
         }
     }
 
@@ -68,13 +68,13 @@ public static void tensorflow() {
             throw new IllegalStateException(
                     "The TensorFlow model zoo is required, but not found.Please install it by"
                         + " following"
-                        + " http://docs.djl.ai/engines/tensorflow/tensorflow-model-zoo/index.html#installation");
+                        + " https://docs.djl.ai/master/engines/tensorflow/tensorflow-model-zoo/index.html#installation");
         }
         if (!Engine.hasEngine("TensorFlow")) {
             throw new IllegalStateException(
                     "The TensorFlow engine is required, but not found.Please install it by"
                         + " following"
-                        + " http://docs.djl.ai/engines/tensorflow/tensorflow-engine/index.html#installation");
+                        + " https://docs.djl.ai/master/engines/tensorflow/tensorflow-engine/index.html#installation");
         }
     }
 }
@@ -14,6 +14,6 @@
 /**
  * Contains a zero deep learning knowledge required wrapper over DJL.
  *
- * <p><a href="https://docs.djl.ai/zero/index.html">See more details</a>.
+ * <p><a href="https://docs.djl.ai/master/zero/index.html">See more details</a>.
  */
 package ai.djl.zero;
@@ -20,14 +20,14 @@ Note: when searching in JavaDoc, if your access is denied, please try removing t
 - [Troubleshooting](development/troubleshooting.md)
 - [Inference Optimization](development/inference_performance_optimization.md)
 
-## [Jupyter notebook tutorials](http://docs.djl.ai/docs/demos/jupyter/index.html)
-
-- **[Beginner Jupyter Tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/index.html)**
-- [Run object detection with model zoo](http://docs.djl.ai/docs/demos/jupyter/object_detection_with_model_zoo.html)
-- [Load pre-trained PyTorch model](http://docs.djl.ai/docs/demos/jupyter/load_pytorch_model.html)
-- [Load pre-trained Apache MXNet model](http://docs.djl.ai/docs/demos/jupyter/load_mxnet_model.html)
-- [Transfer learning example](http://docs.djl.ai/docs/demos/jupyter/transfer_learning_on_cifar10.html)
-- [Question answering example](http://docs.djl.ai/docs/demos/jupyter/BERTQA.html)
+## [Jupyter notebook tutorials](https://docs.djl.ai/master/docs/demos/jupyter/index.html)
+
+- **[Beginner Jupyter Tutorial](https://docs.djl.ai/master/docs/demos/jupyter/tutorial/index.html)**
+- [Run object detection with model zoo](https://docs.djl.ai/master/docs/demos/jupyter/object_detection_with_model_zoo.html)
+- [Load pre-trained PyTorch model](https://docs.djl.ai/master/docs/demos/jupyter/load_pytorch_model.html)
+- [Load pre-trained Apache MXNet model](https://docs.djl.ai/master/docs/demos/jupyter/load_mxnet_model.html)
+- [Transfer learning example](https://docs.djl.ai/master/docs/demos/jupyter/transfer_learning_on_cifar10.html)
+- [Question answering example](https://docs.djl.ai/master/docs/demos/jupyter/BERTQA.html)
 
 ## [API Examples](../examples/README.md)
 

@@ -105,6 +105,6 @@ img.save(out2, "png");
 ```
 
 ## Useful Information
-If you want to learn more about loading models, click [here](http://docs.djl.ai/docs/load_model.html).
+If you want to learn more about loading models, click [here](https://docs.djl.ai/master/docs/load_model.html).
 
-If you want to learn more about the model zoo, click [here](http://docs.djl.ai/docs/model-zoo.html).
+If you want to learn more about the model zoo, click [here](https://docs.djl.ai/master/docs/model-zoo.html).
@@ -15,13 +15,13 @@ For engines that are thread-safe, we do nothing and for ones that are not, we wi
 
 Therefore, we recommend creating a new [Predictor](https://javadoc.io/doc/ai.djl/api/latest/ai/djl/inference/Predictor.html) for each thread.
 That Predictor should be reused if the thread does multiple predictions.
-Alternatively, you can also use a pool of Predictors or you can leverage the [DJL Serving WorkLoadManager](http://docs.djl.ai/docs/serving/wlm/index.html).
+Alternatively, you can also use a pool of Predictors or you can leverage the [DJL Serving WorkLoadManager](https://docs.djl.ai/master/docs/serving/wlm/index.html).
 
 For a reference implementation, see [Multi-threaded Benchmark](https://github.com/deepjavalibrary/djl-serving/blob/master/benchmark/src/main/java/ai/djl/benchmark/MultithreadedBenchmark.java).
 
 In addition, you may need to set engine-specific configurations as well.
 Engine-specific details are given below.
-You can also reference the [list of all DJL system configurations](http://docs.djl.ai/docs/serving/serving/docs/configurations.html).
+You can also reference the [list of all DJL system configurations](https://docs.djl.ai/master/docs/serving/serving/docs/configurations.html).
 
 ### Apache MXNet
 

@@ -204,7 +204,7 @@ ensorflow.so: /usr/lib/x86_64-linux-gnu/libstdc++.so.6: version `CXXABI_1.3.11'
 nsorflow/cache/2.3.1-cu101-linux-x86_64/libtensorflow.so.2)
 ```
 
-Please upgrade gcc version according to the steps on [TensorFlow install page](https://docs.djl.ai/engines/tensorflow/index.html#note)
+Please upgrade gcc version according to the steps on [TensorFlow install page](https://docs.djl.ai/master/engines/tensorflow/index.html#note)
 
 ## 9. X11 error when running object detection notebooks on EC2 instances.
 

@@ -63,14 +63,14 @@ After that, click `run` and you should see the following result:
 
 Finally, you can get the running project setup by clicking `Get Template`. This will bring you a gradle project that can be used in your local machine.
 
-## [Java Jupyter Notebook](http://docs.djl.ai/docs/demos/jupyter/index.html)
+## [Java Jupyter Notebook](https://docs.djl.ai/master/docs/demos/jupyter/index.html)
 
 Wait a second, are we talking about hosting Jupyter Notebook in python? 
 No, it’s Java 11, only.
 
 ![jupyter](https://djl-ai.s3.amazonaws.com/web-data/images/jupyter.gif)
 
 Inspired by Spencer Park’s [IJava project](https://github.com/SpencerPark/IJava), we integrated DJL with Jupyter Notebooks. 
-For more information on the simple setup, follow the instructions in [DJL Jupyter notebooks](http://docs.djl.ai/docs/demos/jupyter/index.html#setup).
+For more information on the simple setup, follow the instructions in [DJL Jupyter notebooks](https://docs.djl.ai/master/docs/demos/jupyter/index.html#setup).
 After that, use the Jupyter Notebook freely in your hosted server. You can do all kinds of work, like block building and plotting a graph.
-There are [tutorials and instructions](http://docs.djl.ai/docs/demos/jupyter/index.html#djl---jupyter-notebooks) to guide you how you can run training and/or inference with Java.
+There are [tutorials and instructions](https://docs.djl.ai/master/docs/demos/jupyter/index.html#djl---jupyter-notebooks) to guide you how you can run training and/or inference with Java.
@@ -3,7 +3,7 @@
 In the following examples, we assume
 
 - `manager` is an instance of `ai.djl.ndarray.NDManager`
-    - it is recommended to look through https://docs.djl.ai/docs/development/memory_management.html
+    - it is recommended to look through https://docs.djl.ai/master/docs/development/memory_management.html
       in advance so that you can get better insight of `NDManager`
 - `$tensor` is a placeholder for an instance of `torch.tensor`
 - `$ndarray` is a placeholder for an instance of `ai.djl.ndarray.NDArray`

@@ -1,7 +1,7 @@
 # Quick start
 
 Deep Java Library (DJL) is designed to be easy to get started with and simple to use.
-The easiest way to learn DJL is to read the [beginner tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/README.md) or
+The easiest way to learn DJL is to read the [beginner tutorial](https://docs.djl.ai/master/docs/demos/jupyter/tutorial/README.md) or
 our [examples](../examples/README.md).
 
 You can also view our 1.5 hour long (in 8 x ~10 minute segments) DJL 101 tutorial video series:
@@ -20,7 +20,7 @@ You can start from here to run inference online and download starter template wi
 
 ## Beginner tutorial
 
-To get started, we recommend that you follow our short [beginner tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/index.html). It takes you through some of the basics of deep learning to create a model, train your model, and run inference using your trained model.
+To get started, we recommend that you follow our short [beginner tutorial](https://docs.djl.ai/master/docs/demos/jupyter/tutorial/index.html). It takes you through some of the basics of deep learning to create a model, train your model, and run inference using your trained model.
 
 ## Run examples
 
@@ -31,7 +31,7 @@ All of our examples are executed by a simple command. For detailed command line
 - [Train your first model](../examples/docs/train_mnist_mlp.md)
 - [Single-shot Object Detection inference example](../examples/docs/object_detection.md)
 - [More examples](https://github.com/deepjavalibrary/djl/tree/master/examples)
-- [Jupyter examples](http://docs.djl.ai/docs/demos/jupyter/index.html)
+- [Jupyter examples](https://docs.djl.ai/master/docs/demos/jupyter/index.html)
 
 ## Other resources
 

@@ -116,7 +116,7 @@ For more information on saving, loading and exporting checkpoints, please refer
 
 ## How to load DJL TensorFlow model zoo models
 
-The steps are the same as loading any other DJL model zoo models, you can use the `Criteria` API as documented [here](https://docs.djl.ai/docs/load_model.html#criteria-class).
+The steps are the same as loading any other DJL model zoo models, you can use the `Criteria` API as documented [here](https://docs.djl.ai/master/docs/load_model.html#criteria-class).
 
 Note for TensorFlow image classification models, you need to manually specify the translator instead of using the built-in one because
 TensorFlow requires channels last ("NHWC") image formats while DJL use channels first ("NCHW") image formats. By default, DJL will add