From 5b71ee47d13d92ecf118ecc76785c429c81372f4 Mon Sep 17 00:00:00 2001 From: Zach Kimberg Date: Fri, 21 Feb 2020 11:07:12 -0800 Subject: [PATCH] Replace links to gh master with v0.3.0 Change-Id: I32ad7fe3ffc8c2d05d2280f6513f84e493aa915d --- api/src/main/java/ai/djl/inference/Predictor.java | 8 ++++---- api/src/main/java/ai/djl/metric/Metrics.java | 2 +- api/src/main/java/ai/djl/nn/Block.java | 2 +- api/src/main/java/ai/djl/training/Trainer.java | 6 +++--- docs/faq.md | 6 +++--- examples/build.gradle | 2 +- examples/docs/train_mnist_mlp.md | 2 +- examples/pom.xml | 2 +- .../ai/djl/examples/inference/ActionRecognition.java | 2 +- .../ai/djl/examples/inference/BertQaInference.java | 4 ++-- .../djl/examples/inference/ImageClassification.java | 2 +- .../djl/examples/inference/InstanceSegmentation.java | 2 +- .../ai/djl/examples/inference/ObjectDetection.java | 2 +- .../ai/djl/examples/inference/PoseEstimation.java | 2 +- .../java/ai/djl/examples/training/TrainCaptcha.java | 2 +- .../java/ai/djl/examples/training/TrainMnist.java | 2 +- .../java/ai/djl/examples/training/TrainPikachu.java | 2 +- .../transferlearning/TrainResnetWithCifar10.java | 2 +- jupyter/BERTQA.ipynb | 6 +++--- jupyter/load_mxnet_model.ipynb | 4 ++-- jupyter/object_detection_with_model_zoo.ipynb | 8 ++++---- jupyter/transfer_learning_on_cifar10.ipynb | 12 ++++++------ jupyter/tutorial/create_your_first_network.ipynb | 6 +++--- .../image_classification_with_your_model.ipynb | 6 +++--- jupyter/tutorial/train_your_first_model.ipynb | 6 +++--- .../ai/djl/mxnet/zoo/nlp/qa/BertQAModelLoader.java | 2 +- 26 files changed, 51 insertions(+), 51 deletions(-) diff --git a/api/src/main/java/ai/djl/inference/Predictor.java b/api/src/main/java/ai/djl/inference/Predictor.java index 7c28998c587..0195ea238fc 100644 --- a/api/src/main/java/ai/djl/inference/Predictor.java +++ b/api/src/main/java/ai/djl/inference/Predictor.java @@ -39,18 +39,18 @@ * * * *

For information about running multi-threaded inference, see here. + * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/multithread_inference.md">here. * * @param the input type * @param the output type diff --git a/api/src/main/java/ai/djl/metric/Metrics.java b/api/src/main/java/ai/djl/metric/Metrics.java index 305b34ff926..52665ea83fc 100644 --- a/api/src/main/java/ai/djl/metric/Metrics.java +++ b/api/src/main/java/ai/djl/metric/Metrics.java @@ -35,7 +35,7 @@ * latencies, CPU and GPU memory consumption, losses, etc. * *

For more details about using the metrics, see the metrics + * href="https://github.com/awslabs/djl/blob/v0.3.0/docs/how_to_collect_metrics.md">metrics * tutorial. */ public class Metrics { diff --git a/api/src/main/java/ai/djl/nn/Block.java b/api/src/main/java/ai/djl/nn/Block.java index 439b5b5bb68..1d44159fad4 100644 --- a/api/src/main/java/ai/djl/nn/Block.java +++ b/api/src/main/java/ai/djl/nn/Block.java @@ -100,7 +100,7 @@ * fully-trained model. * *

See this tutorial on creating + * href="https://github.com/awslabs/djl/blob/v0.3.0/jupyter/tutorial/create_your_first_network.ipynb">creating * your first network. */ public interface Block { diff --git a/api/src/main/java/ai/djl/training/Trainer.java b/api/src/main/java/ai/djl/training/Trainer.java index 3a28536bc04..367fe225d3f 100644 --- a/api/src/main/java/ai/djl/training/Trainer.java +++ b/api/src/main/java/ai/djl/training/Trainer.java @@ -34,13 +34,13 @@ * *

*/ diff --git a/docs/faq.md b/docs/faq.md index ed2a26a87ca..f54c03c3020 100644 --- a/docs/faq.md +++ b/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? diff --git a/examples/build.gradle b/examples/build.gradle index acaf6c2e776..3776dfaf273 100644 --- a/examples/build.gradle +++ b/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' diff --git a/examples/docs/train_mnist_mlp.md b/examples/docs/train_mnist_mlp.md index 2eab1f5aa8f..2af5ab1e2ad 100644 --- a/examples/docs/train_mnist_mlp.md +++ b/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. diff --git a/examples/pom.xml b/examples/pom.xml index db4163f0e5b..d6308223a30 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -55,7 +55,7 @@ ai.djl.mxnet mxnet-native-auto diff --git a/examples/src/main/java/ai/djl/examples/inference/ActionRecognition.java b/examples/src/main/java/ai/djl/examples/inference/ActionRecognition.java index cfaf1d73f92..5b3f9563849 100644 --- a/examples/src/main/java/ai/djl/examples/inference/ActionRecognition.java +++ b/examples/src/main/java/ai/djl/examples/inference/ActionRecognition.java @@ -33,7 +33,7 @@ * An example of inference using an action recognition model. * *

See this doc for + * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/action_recognition.md">doc for * information about this example. */ public final class ActionRecognition { diff --git a/examples/src/main/java/ai/djl/examples/inference/BertQaInference.java b/examples/src/main/java/ai/djl/examples/inference/BertQaInference.java index 49d504a990f..02278680235 100644 --- a/examples/src/main/java/ai/djl/examples/inference/BertQaInference.java +++ b/examples/src/main/java/ai/djl/examples/inference/BertQaInference.java @@ -32,10 +32,10 @@ *

See: * *

    - *
  • the jupyter + *
  • the jupyter * demo with more information about BERT. *
  • the docs + * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/BERT_question_and_answer.md">docs * for information about running this example. *
*/ diff --git a/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java b/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java index 5b5f333da8d..bff0a85e3a1 100644 --- a/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java +++ b/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java @@ -39,7 +39,7 @@ * An example of inference using an image classification model. * *

See this doc + * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/image_classification.md">doc * for information about this example. */ public final class ImageClassification { diff --git a/examples/src/main/java/ai/djl/examples/inference/InstanceSegmentation.java b/examples/src/main/java/ai/djl/examples/inference/InstanceSegmentation.java index 13036d0d9e5..ff321971290 100644 --- a/examples/src/main/java/ai/djl/examples/inference/InstanceSegmentation.java +++ b/examples/src/main/java/ai/djl/examples/inference/InstanceSegmentation.java @@ -37,7 +37,7 @@ * An example of inference using an instance segmentation model. * *

See this doc + * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/instance_segmentation.md">doc * for information about this example. */ public final class InstanceSegmentation { diff --git a/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java b/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java index 111a0acc72f..a9b7d4e0039 100644 --- a/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java +++ b/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java @@ -37,7 +37,7 @@ * An example of inference using an object detection model. * *

See this doc for + * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/object_detection.md">doc for * information about this example. */ public final class ObjectDetection { diff --git a/examples/src/main/java/ai/djl/examples/inference/PoseEstimation.java b/examples/src/main/java/ai/djl/examples/inference/PoseEstimation.java index 1b679ae6f40..b4808e03d92 100644 --- a/examples/src/main/java/ai/djl/examples/inference/PoseEstimation.java +++ b/examples/src/main/java/ai/djl/examples/inference/PoseEstimation.java @@ -41,7 +41,7 @@ * An example of inference using a pose estimation model. * *

See this doc for + * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/pose_estimation.md">doc for * information about this example. */ public final class PoseEstimation { diff --git a/examples/src/main/java/ai/djl/examples/training/TrainCaptcha.java b/examples/src/main/java/ai/djl/examples/training/TrainCaptcha.java index 96aecacdc37..42d596d6930 100644 --- a/examples/src/main/java/ai/djl/examples/training/TrainCaptcha.java +++ b/examples/src/main/java/ai/djl/examples/training/TrainCaptcha.java @@ -43,7 +43,7 @@ * An example of training a CAPTCHA solving model. * *

See this doc for + * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/train_captcha.md">doc for * information about this example. */ public final class TrainCaptcha { diff --git a/examples/src/main/java/ai/djl/examples/training/TrainMnist.java b/examples/src/main/java/ai/djl/examples/training/TrainMnist.java index bcf7afe404c..64a1baef29b 100644 --- a/examples/src/main/java/ai/djl/examples/training/TrainMnist.java +++ b/examples/src/main/java/ai/djl/examples/training/TrainMnist.java @@ -38,7 +38,7 @@ * An example of training an image classification (MNIST) model. * *

See this doc for + * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/train_mnist_mlp.md">doc for * information about this example. */ public final class TrainMnist { diff --git a/examples/src/main/java/ai/djl/examples/training/TrainPikachu.java b/examples/src/main/java/ai/djl/examples/training/TrainPikachu.java index c0e0245010f..8b6797ccc58 100644 --- a/examples/src/main/java/ai/djl/examples/training/TrainPikachu.java +++ b/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. * *

See this doc for + * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/train_pikachu_ssd.md">doc for * information about this example. */ public final class TrainPikachu { diff --git a/examples/src/main/java/ai/djl/examples/training/transferlearning/TrainResnetWithCifar10.java b/examples/src/main/java/ai/djl/examples/training/transferlearning/TrainResnetWithCifar10.java index 84eaec9a026..409e5215347 100644 --- a/examples/src/main/java/ai/djl/examples/training/transferlearning/TrainResnetWithCifar10.java +++ b/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. * *

See this doc + * href="https://github.com/awslabs/djl/blob/v0.3.0/examples/docs/train_cifar10_resnet.md">doc * for information about this example. */ public final class TrainResnetWithCifar10 { diff --git a/jupyter/BERTQA.ipynb b/jupyter/BERTQA.ipynb index 957d8e3ce81..25fe2e9d8cb 100644 --- a/jupyter/BERTQA.ipynb +++ b/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", diff --git a/jupyter/load_mxnet_model.ipynb b/jupyter/load_mxnet_model.ipynb index af27d10f89d..22605231002 100644 --- a/jupyter/load_mxnet_model.ipynb +++ b/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" ] diff --git a/jupyter/object_detection_with_model_zoo.ipynb b/jupyter/object_detection_with_model_zoo.ipynb index a3051924a34..468338cba5b 100644 --- a/jupyter/object_detection_with_model_zoo.ipynb +++ b/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" ] } ], diff --git a/jupyter/transfer_learning_on_cifar10.ipynb b/jupyter/transfer_learning_on_cifar10.ipynb index 5a27ad1cb45..c987f01d878 100644 --- a/jupyter/transfer_learning_on_cifar10.ipynb +++ b/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" ] diff --git a/jupyter/tutorial/create_your_first_network.ipynb b/jupyter/tutorial/create_your_first_network.ipynb index 75d73631062..830d1c85fa2 100644 --- a/jupyter/tutorial/create_your_first_network.ipynb +++ b/jupyter/tutorial/create_your_first_network.ipynb @@ -25,7 +25,7 @@ "\n", "### Installation\n", "\n", - "This tutorial requires the installation of the Java Jupyter Kernel. To install the kernel, see the [Jupyter README](https://github.com/awslabs/djl/blob/master/jupyter/README.md)." + "This tutorial requires the installation of the Java Jupyter Kernel. To install the kernel, see the [Jupyter README](https://github.com/awslabs/djl/blob/v0.3.0/jupyter/README.md)." ] }, { @@ -39,7 +39,7 @@ "%maven org.slf4j:slf4j-api:1.7.26\n", "%maven org.slf4j:slf4j-simple:1.7.26\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" ] @@ -146,7 +146,7 @@ "\n", "Next chapter: [Train your first model](train_your_first_model.ipynb)\n", "\n", - "You can find the complete source code for this tutorial in the [model zoo](https://github.com/awslabs/djl/blob/master/model-zoo/src/main/java/ai/djl/basicmodelzoo/cv/classification/Mlp.java)." + "You can find the complete source code for this tutorial in the [model zoo](https://github.com/awslabs/djl/blob/v0.3.0/model-zoo/src/main/java/ai/djl/basicmodelzoo/cv/classification/Mlp.java)." ] } ], diff --git a/jupyter/tutorial/image_classification_with_your_model.ipynb b/jupyter/tutorial/image_classification_with_your_model.ipynb index 0ccb6e3d2e9..6b7f0798e73 100644 --- a/jupyter/tutorial/image_classification_with_your_model.ipynb +++ b/jupyter/tutorial/image_classification_with_your_model.ipynb @@ -13,7 +13,7 @@ "\n", "## Preparation\n", "\n", - "This tutorial requires the installation of the Java Jupyter Kernel. To install the kernel, see the [Jupyter README](https://github.com/awslabs/djl/blob/master/jupyter/README.md)." + "This tutorial requires the installation of the Java Jupyter Kernel. To install the kernel, see the [Jupyter README](https://github.com/awslabs/djl/blob/v0.3.0/jupyter/README.md)." ] }, { @@ -32,7 +32,7 @@ "%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" ] @@ -186,7 +186,7 @@ "\n", "Now, you've successfully built a model, trained it, and run inference. Congratulations on finishing the [beginner tutorial series](https://github.com/awslabs/djl/tree/master/jupyter/tutorial). After this, you should read our other [examples](https://github.com/awslabs/djl/tree/master/examples) and [jupyter notebooks](https://github.com/awslabs/djl/tree/master/jupyter) to learn more about DJL.\n", "\n", - "You can find the complete source code for this tutorial in the [examples project](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java)." + "You can find the complete source code for this tutorial in the [examples project](https://github.com/awslabs/djl/blob/v0.3.0/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java)." ] } ], diff --git a/jupyter/tutorial/train_your_first_model.ipynb b/jupyter/tutorial/train_your_first_model.ipynb index 8fd1edd3a1a..c89a2e77a98 100644 --- a/jupyter/tutorial/train_your_first_model.ipynb +++ b/jupyter/tutorial/train_your_first_model.ipynb @@ -10,7 +10,7 @@ "\n", "## Preparation\n", "\n", - "This tutorial requires the installation of the Java Jupyter Kernel. To install the kernel, see the [Jupyter README](https://github.com/awslabs/djl/blob/master/jupyter/README.md)." + "This tutorial requires the installation of the Java Jupyter Kernel. To install the kernel, see the [Jupyter README](https://github.com/awslabs/djl/blob/v0.3.0/jupyter/README.md)." ] }, { @@ -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" ] @@ -258,7 +258,7 @@ "\n", "Now, you've successfully trained a model that can recognize handwritten digits. You'll learn how to apply this model in the next chapter: [Run image classification with your model](image_classification_with_your_model.ipynb).\n", "\n", - "You can find the complete source code for this tutorial in the [examples project](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/training/TrainMnist.java)." + "You can find the complete source code for this tutorial in the [examples project](https://github.com/awslabs/djl/blob/v0.3.0/examples/src/main/java/ai/djl/examples/training/TrainMnist.java)." ] } ], diff --git a/mxnet/mxnet-model-zoo/src/main/java/ai/djl/mxnet/zoo/nlp/qa/BertQAModelLoader.java b/mxnet/mxnet-model-zoo/src/main/java/ai/djl/mxnet/zoo/nlp/qa/BertQAModelLoader.java index db47cb136e5..5f035c2d6eb 100644 --- a/mxnet/mxnet-model-zoo/src/main/java/ai/djl/mxnet/zoo/nlp/qa/BertQAModelLoader.java +++ b/mxnet/mxnet-model-zoo/src/main/java/ai/djl/mxnet/zoo/nlp/qa/BertQAModelLoader.java @@ -36,7 +36,7 @@ *

The model was trained on Gluon and loaded in DJL in MXNet Symbol Block. * *

See the BERT paper or the jupyter demo for more + * href="https://github.com/awslabs/djl/blob/v0.3.0/jupyter/BERTQA.ipynb">jupyter demo for more * information about BERT. * * @see ai.djl.mxnet.engine.MxSymbolBlock