[docs] Move jupyter notebooks to DJL Demo (#2854)

* [docs] Move jupyter notebooks to DJL Demo

This moves the notebooks to the demo repositories. It makes it more clearly
follow the trend where the things in the main repo are master build while those
in demos track nightly snapshots or releases.

.github/workflows/docs.yml

@@ -3,7 +3,6 @@ name: Docs
 on:
   pull_request:
     paths:
-      - "**.ipynb"
       - "docs/mkdocs.yml"
   # Publish docs weekly
   schedule:
@@ -38,10 +37,6 @@ jobs:
       - name: add mybinder link
         run: |
           python3 tools/scripts/add_online_runner.py
-      - name: run Notebooks
-        run: |
-          cd jupyter
-          bash test_notebook.sh
       - name: clone demos
         run: |
           cd docs
@@ -50,6 +45,10 @@ jobs:
         run: |
           cd docs
           git clone https://github.com/deepjavalibrary/djl-serving.git serving
+      - name: run Notebooks
+        run: |
+          cd docs/demos/jupyter
+          bash test_notebook.sh
       - name: build docs
         run: |
           cd docs

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

@@ -60,14 +60,13 @@
  *
  * <ul>
  *   <li><a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/tutorial/03_image_classification_with_your_model.ipynb">Inference
+ *       href="http://docs.djl.ai/docs/demos/jupyter/tutorial/03_image_classification_with_your_model.html">Inference
  *       with a custom trained model</a>
  *   <li><a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/object_detection_with_model_zoo.ipynb">Inference
+ *       href="http://docs.djl.ai/docs/demos/jupyter/object_detection_with_model_zoo.html">Inference
  *       with a model zoo model</a>
- *   <li><a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/load_mxnet_model.ipynb">Inference
- *       with an MXNet model</a>
+ *   <li><a href="http://docs.djl.ai/docs/demos/jupyter/load_mxnet_model.html">Inference with an
+ *       MXNet model</a>
  * </ul>
  *
  * @param <I> the input type

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

@@ -105,7 +105,7 @@
  * further refine these elements, use {@link Block#freezeParameters(boolean)} to unfreeze them.
  *
  * @see <a
- *     href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/tutorial/01_create_your_first_network.ipynb">this
+ *     href="http://docs.djl.ai/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

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

@@ -52,14 +52,12 @@
  *
  * <ul>
  *   <li><a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/tutorial/02_train_your_first_model.ipynb">Training
+ *       href="http://docs.djl.ai/docs/demos/jupyter/tutorial/02_train_your_first_model.html">Training
  *       your first model</a>
- *   <li><a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/transfer_learning_on_cifar10.ipynb">Training
+ *   <li><a href="http://docs.djl.ai/docs/demos/jupyter/transfer_learning_on_cifar10.html">Training
  *       using transfer learning</a>
- *   <li><a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/load_mxnet_model.ipynb">Inference
- *       with an MXNet model</a>
+ *   <li><a href="http://docs.djl.ai/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

docs/README.md

@@ -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](../jupyter/README.md)
-
-- **[Beginner Jupyter Tutorial](../jupyter/tutorial/README.md)**
-- [Run object detection with model zoo](../jupyter/object_detection_with_model_zoo.ipynb)
-- [Load pre-trained PyTorch model](../jupyter/load_pytorch_model.ipynb)
-- [Load pre-trained Apache MXNet model](../jupyter/load_mxnet_model.ipynb)
-- [Transfer learning example](../jupyter/transfer_learning_on_cifar10.ipynb)
-- [Question answering example](../jupyter/BERTQA.ipynb)
+## [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)
 
 ## [API Examples](../examples/README.md)
 

docs/interactive_tool.md

@@ -63,17 +63,17 @@ 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](../jupyter/README.md)
+## [Java Jupyter Notebook](http://docs.djl.ai/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](../jupyter/README.md#setup).
+For more information on the simple setup, follow the instructions in [DJL Jupyter notebooks](http://docs.djl.ai/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](../jupyter/README.md#djl---jupyter-notebooks) to guide you how you can run training and/or inference with Java.
+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.
 
 ## About Future Lab
 

docs/mkdocs.yml

@@ -61,15 +61,15 @@ nav:
       - 'docs/faq.md'
   - Tutorials:
       - Beginner Tutorial:
-          - 'jupyter/tutorial/01_create_your_first_network.ipynb'
-          - 'jupyter/tutorial/02_train_your_first_model.ipynb'
-          - 'jupyter/tutorial/03_image_classification_with_your_model.ipynb'
+          - 'docs/demos/jupyter/tutorial/01_create_your_first_network.ipynb'
+          - 'docs/demos/jupyter/tutorial/02_train_your_first_model.ipynb'
+          - 'docs/demos/jupyter/tutorial/03_image_classification_with_your_model.ipynb'
       - 'docs/d2l.md'
-      - 'jupyter/rank_classification_using_BERT_on_Amazon_Review.ipynb'
-      - 'jupyter/transfer_learning_on_cifar10.ipynb'
+      - 'docs/demos/jupyter/rank_classification_using_BERT_on_Amazon_Review.ipynb'
+      - 'docs/demos/jupyter/transfer_learning_on_cifar10.ipynb'
       - Load your own BERT:
-          - BERT with MXNet: 'jupyter/mxnet/load_your_own_mxnet_bert.ipynb'
-          - BERT with PyTorch: 'jupyter/pytorch/load_your_own_pytorch_bert.ipynb'
+          - BERT with MXNet: 'docs/demos/jupyter/mxnet/load_your_own_mxnet_bert.ipynb'
+          - BERT with PyTorch: 'docs/demos/jupyter/pytorch/load_your_own_pytorch_bert.ipynb'
   - Guides:
       - Models:
           - 'docs/load_model.md'
@@ -97,25 +97,25 @@ nav:
           - PyTorch NDArray Operators: 'docs/pytorch/pytorch-djl-ndarray-cheatsheet.md'
           - PyTorch Model Zoo: 'engines/pytorch/pytorch-model-zoo/README.md'
           - Import PyTorch Model: 'docs/pytorch/how_to_convert_your_model_to_torchscript.md'
-          - Load a PyTorch Model: 'jupyter/load_pytorch_model.ipynb'
+          - Load a PyTorch Model: 'docs/demos/jupyter/load_pytorch_model.ipynb'
       - TensorFlow:
           - Overview: 'engines/tensorflow/README.md'
           - TensorFlow Engine: 'engines/tensorflow/tensorflow-engine/README.md'
           - TensorFlow Model Zoo: 'engines/tensorflow/tensorflow-model-zoo/README.md'
           - Import TensorFlow Model: 'docs/tensorflow/how_to_import_tensorflow_models_in_DJL.md'
-          - Load a TensorFlow Model: 'jupyter/tensorflow/pneumonia_detection.ipynb'
+          - Load a TensorFlow Model: 'docs/demos/jupyter/tensorflow/pneumonia_detection.ipynb'
       - Apache MXNet:
           - Overview: 'engines/mxnet/README.md'
           - MXNet Engine: 'engines/mxnet/mxnet-engine/README.md'
           - MXNet Model Zoo: 'engines/mxnet/mxnet-model-zoo/README.md'
           - Import Gluon Model: 'docs/mxnet/how_to_convert_your_model_to_symbol.md'
-          - Load a MXNet Model: 'jupyter/load_mxnet_model.ipynb'
+          - Load a MXNet Model: 'docs/demos/jupyter/load_mxnet_model.ipynb'
           - Backend Optimizer for MXNet: 'docs/mxnet/mxnet_backend_optimizer.md'
       - Hybrid engines:
           - Hybrid engine overview: 'docs/hybrid_engine.md'
           - ONNX Runtime:
               - Overview: 'engines/onnxruntime/onnxruntime-engine/README.md'
-              - Load a ONNX Model: 'jupyter/onnxruntime/machine_learning_with_ONNXRuntime.ipynb'
+              - Load a ONNX Model: 'docs/demos/jupyter/onnxruntime/machine_learning_with_ONNXRuntime.ipynb'
           - PaddlePaddle:
               - Overview: 'engines/paddlepaddle/README.md'
               - PaddlePaddle Engine: 'engines/paddlepaddle/paddlepaddle-engine/README.md'
@@ -124,11 +124,11 @@ nav:
                   - English: 'docs/paddlepaddle/how_to_create_paddlepaddle_model.md'
                   - 中文: 'docs/paddlepaddle/how_to_create_paddlepaddle_model_zh.md'
               - Facemask detection using PaddlePaddle:
-                  - English: 'jupyter/paddlepaddle/face_mask_detection_paddlepaddle.ipynb'
-                  - 中文: 'jupyter/paddlepaddle/face_mask_detection_paddlepaddle_zh.ipynb'
+                  - English: 'docs/demos/jupyter/paddlepaddle/face_mask_detection_paddlepaddle.ipynb'
+                  - 中文: 'docs/demos/jupyter/paddlepaddle/face_mask_detection_paddlepaddle_zh.ipynb'
               - PaddleOCR example:
-                  - English: 'jupyter/paddlepaddle/paddle_ocr_java.ipynb'
-                  - 中文: 'jupyter/paddlepaddle/paddle_ocr_java_zh.ipynb'
+                  - English: 'docs/demos/jupyter/paddlepaddle/paddle_ocr_java.ipynb'
+                  - 中文: 'docs/demos/jupyter/paddlepaddle/paddle_ocr_java_zh.ipynb'
           - XGBoost: 'engines/ml/xgboost/README.md'
           - LightGBM: 'engines/ml/lightgbm/README.md'
           - TensorRT: 'engines/tensorrt/README.md'

docs/paddlepaddle/how_to_create_paddlepaddle_model.md

@@ -157,5 +157,5 @@ predictor.predict(list);
 
 As mentioned, you need to find out what is the input for the model, like images usually interpret as NCHW (batch_size, channel, height, width).
 
-However, usage like this is really basic, you can write a `Translator` in DJL for it. You can find some code examples [here](../../jupyter/paddlepaddle/face_mask_detection_paddlepaddle.ipynb).
+However, usage like this is really basic, you can write a `Translator` in DJL for it. You can find some code examples [here](http://docs.djl.ai/docs/demos/jupyter/paddlepaddle/face_mask_detection_paddlepaddle.html).
 

docs/paddlepaddle/how_to_create_paddlepaddle_model_zh.md

@@ -156,4 +156,4 @@ predictor.predict(list);
 
 在这里，你需要知道模型的输入输出格式, 比如图片经常表达成 NCHW (批大小, RGB通道, 高度, 宽度)的多维矩阵。
 
-虽然这样可以让模型跑起来, 但是最好还是结合 DJL 的 `Translator` class 使用。你可以在 [这里](../../jupyter/paddlepaddle/face_mask_detection_paddlepaddle.ipynb) 找到一些示例代码。
+虽然这样可以让模型跑起来, 但是最好还是结合 DJL 的 `Translator` class 使用。你可以在 [这里](http://docs.djl.ai/docs/demos/jupyter/paddlepaddle/face_mask_detection_paddlepaddle.html) 找到一些示例代码。

docs/quick_start.md

@@ -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](../jupyter/tutorial/README.md) or
+The easiest way to learn DJL is to read the [beginner tutorial](http://docs.djl.ai/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:
@@ -22,7 +22,7 @@ See [DJL Future Labs](interactive_tool.md)
 
 ## Beginner tutorial
 
-To get started, we recommend that you follow our short [beginner tutorial](../jupyter/tutorial/README.md). 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](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.
 
 ## Run examples
 
@@ -33,7 +33,7 @@ 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](../jupyter/README.md)
+- [Jupyter examples](http://docs.djl.ai/docs/demos/jupyter/index.html)
 
 ## Other resources
 

examples/docs/image_classification.md

@@ -6,7 +6,7 @@ In this example, you learn how to implement inference code with Deep Java Librar
 
 The image classification example code can be found at [ImageClassification.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java).
 
-You can also use the [Jupyter notebook tutorial](../../jupyter/tutorial/03_image_classification_with_your_model.ipynb).
+You can also use the [Jupyter notebook tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/03_image_classification_with_your_model.html).
 The Jupyter notebook explains the key concepts in detail.
 
 ## Setup Guide

examples/docs/object_detection.md

@@ -7,7 +7,7 @@ In this example, you learn how to implement inference code with a [ModelZoo mode
 
 The source code can be found at [ObjectDetection.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java).
 
-You can also use the [Jupyter notebook tutorial](../../jupyter/object_detection_with_model_zoo.ipynb).
+You can also use the [Jupyter notebook tutorial](http://docs.djl.ai/docs/demos/jupyter/object_detection_with_model_zoo.html).
 The Jupyter notebook explains the key concepts in detail.
 
 ## Setup guide

examples/docs/train_cifar10_resnet.md

@@ -5,7 +5,7 @@ In this example, you learn how to train the [CIFAR-10](https://www.cs.toronto.ed
 
 You can find the example source code in: [TrainResnetWithCifar10.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/training/transferlearning/TrainResnetWithCifar10.java).
 
-You can also find the Jupyter notebook tutorial [here](../../jupyter/transfer_learning_on_cifar10.ipynb).
+You can also find the Jupyter notebook tutorial [here](http://docs.djl.ai/docs/demos/jupyter/transfer_learning_on_cifar10.html).
 The Jupyter notebook explains the key concepts in detail.
 
 ## Setup guide

examples/docs/train_mnist_mlp.md

@@ -6,7 +6,7 @@ In this example, you learn how to train the MNIST dataset with Deep Java Library
 
 The source code for this example can be found at [TrainMnist.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/training/TrainMnist.java).
 
-You can also use the [Jupyter notebook tutorial](../../jupyter/tutorial/02_train_your_first_model.ipynb).
+You can also use the [Jupyter notebook tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/02_train_your_first_model.html).
 The Jupyter notebook explains the key concepts in detail.
 
 ## Setup guide

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

@@ -34,9 +34,8 @@
  * <p>See:
  *
  * <ul>
- *   <li>the <a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/BERTQA.ipynb">jupyter
- *       demo</a> with more information about BERT.
+ *   <li>the <a href="http://docs.djl.ai/docs/demos/jupyter/BERTQA.html">jupyter demo</a> with more
+ *       information about BERT.
  *   <li>the <a
  *       href="https://github.com/deepjavalibrary/djl/blob/master/examples/docs/BERT_question_and_answer.md">docs</a>
  *       for information about running this example.

index1.0.html

@@ -59,7 +59,7 @@
                     <li><a href="website/javadoc.html">JavaDoc</a></li>
                     <li><a href="website/demo.html">Demos</a></li>
                     <li><a href="website/blog.html">Blogs</a></li>
-                    <li><a href="https://docs.djl.ai/jupyter/tutorial/index.html">Tutorial</a></li>
+                    <li><a href="https://docs.djl.ai/docs/demos/jupyter/tutorial/index.html">Tutorial</a></li>
                     <li><a href="https://github.com/deepjavalibrary/djl/tree/master/examples">Examples</a></li>
                     <li><a
                             href="https://deepjavalibrary.slack.com/join/shared_invite/zt-ar91gjkz-qbXhr1l~LFGEIEeGBibT7w#/">Slack</a>
@@ -73,7 +73,7 @@
                     <li><a href="website/javadoc.html">JavaDoc</a></li>
                     <li><a href="website/demo.html">Demos</a></li>
                     <li><a href="website/blog.html">Blogs</a></li>
-                    <li><a href="https://docs.djl.ai/jupyter/tutorial/index.html">Tutorial</a></li>
+                    <li><a href="https://docs.djl.ai/docs/demos/jupyter/tutorial/index.html">Tutorial</a></li>
                     <li><a href="https://github.com/deepjavalibrary/djl/tree/master/examples">Examples</a></li>
                     <li><a
                             href="https://deepjavalibrary.slack.com/join/shared_invite/zt-ar91gjkz-qbXhr1l~LFGEIEeGBibT7w#/">Slack</a>