From d97fb118d7ab34372c9464e69182171601f0e576 Mon Sep 17 00:00:00 2001 From: Frank Liu Date: Wed, 21 Apr 2021 11:11:43 -0700 Subject: [PATCH] Migrate DJL from awslabs to deepjavalibrary organization (#882) * Migrate DJL from awslabs to deepjavalibrary organization Change-Id: I3b391e5de67f87b31af0c8beafb2622c0b1b60ca * Temporary disable codecov Change-Id: I5c274b8c8ef06b84b9498bfb52606258dfc7c656 --- .github/workflows/continuous.yml | 18 ++++++------ .github/workflows/docs.yml | 2 +- .github/workflows/native_jni_s3_paddle.yml | 4 +-- .github/workflows/native_jni_s3_pytorch.yml | 8 +++--- .../native_jni_s3_pytorch_android.yml | 4 +-- .github/workflows/nightly_publish.yml | 8 +++--- README.md | 28 +++++++++---------- android/core/build.gradle | 6 ++-- .../djl/android/core/BitmapWrapperTest.java | 6 ++-- android/pytorch-native/build.gradle | 6 ++-- api/src/main/java/ai/djl/engine/Engine.java | 2 +- .../main/java/ai/djl/inference/Predictor.java | 8 +++--- api/src/main/java/ai/djl/metric/Metrics.java | 2 +- api/src/main/java/ai/djl/ndarray/NDArray.java | 2 +- .../main/java/ai/djl/ndarray/NDManager.java | 2 +- api/src/main/java/ai/djl/nn/Block.java | 2 +- .../main/java/ai/djl/training/Trainer.java | 6 ++-- .../training/loss/SimpleCompositeLoss.java | 2 +- api/src/main/javadoc/overview.html | 2 +- basicdataset/src/main/javadoc/overview.html | 2 +- bom/build.gradle | 6 ++-- djl-zero/src/main/javadoc/overview.html | 2 +- dlr/dlr-engine/src/main/javadoc/overview.html | 2 +- dlr/dlr-native/build.gradle | 6 ++-- docs/create_serving_ready_model.md | 2 +- docs/dataset.md | 2 +- docs/development/benchmark_with_djl.md | 2 +- docs/development/configure_logging.md | 6 ++-- docs/development/development_guideline.md | 2 +- docs/development/how_to_use_dataset.md | 6 ++-- .../inference_performance_optimization.md | 4 +-- docs/development/memory_management.md | 4 +-- docs/development/troubleshooting.md | 12 ++++---- docs/faq.md | 2 +- docs/forums.md | 18 ++++++------ docs/interactive_tool.md | 2 +- docs/load_model.md | 2 +- docs/mkdocs.yml | 8 +++--- docs/quick_start.md | 4 +-- docs/roadmap.md | 2 +- .../how_to_import_tensorflow_models_in_DJL.md | 4 +-- examples/README.md | 10 +++---- examples/docs/BERT_question_and_answer.md | 2 +- examples/docs/action_recognition.md | 2 +- examples/docs/face_detection.md | 4 +-- examples/docs/face_recognition.md | 4 +-- examples/docs/image_classification.md | 2 +- examples/docs/instance_segmentation.md | 2 +- examples/docs/object_detection.md | 2 +- ...t_detection_with_tensorflow_saved_model.md | 2 +- examples/docs/pose_estimation.md | 2 +- examples/docs/sentiment_analysis.md | 2 +- examples/docs/train_amazon_review_ranking.md | 2 +- examples/docs/train_captcha.md | 2 +- examples/docs/train_cifar10_resnet.md | 2 +- examples/docs/train_mnist_mlp.md | 2 +- examples/docs/train_pikachu_ssd.md | 4 +-- examples/pom.xml | 4 +-- .../examples/inference/ActionRecognition.java | 4 +-- .../examples/inference/BertQaInference.java | 5 ++-- .../inference/ImageClassification.java | 2 +- .../inference/InstanceSegmentation.java | 2 +- .../examples/inference/ObjectDetection.java | 4 +-- ...jectDetectionWithTensorflowSavedModel.java | 2 +- .../examples/inference/PoseEstimation.java | 4 +-- .../examples/inference/SentimentAnalysis.java | 4 +-- .../inference/face/LightFaceDetection.java | 4 +-- .../inference/face/RetinaFaceDetection.java | 4 +-- .../djl/examples/training/TrainCaptcha.java | 4 +-- .../ai/djl/examples/training/TrainMnist.java | 4 +-- .../djl/examples/training/TrainPikachu.java | 4 +-- .../TrainResnetWithCifar10.java | 2 +- examples/src/main/javadoc/overview.html | 2 +- extensions/fasttext/README.md | 2 +- index1.0.html | 12 ++++---- .../modality/cv/BufferedImageFactoryTest.java | 2 +- jupyter/BERTQA.ipynb | 6 ++-- jupyter/README.md | 4 +-- jupyter/load_mxnet_model.ipynb | 6 ++-- jupyter/load_pytorch_model.ipynb | 10 +++---- jupyter/mxnet/load_your_own_mxnet_bert.ipynb | 10 +++---- jupyter/object_detection_with_model_zoo.ipynb | 8 +++--- .../machine_learning_with_ONNXRuntime.ipynb | 6 ++-- .../face_mask_detection_paddlepaddle.ipynb | 2 +- .../face_mask_detection_paddlepaddle_zh.ipynb | 2 +- .../pytorch/load_your_own_pytorch_bert.ipynb | 10 +++---- ...fication_using_BERT_on_Amazon_Review.ipynb | 6 ++-- jupyter/tensorflow/pneumonia_detection.ipynb | 2 +- ...fication_using_BERT_on_Amazon_Review.ipynb | 4 +-- .../inference_with_tensorflow_lite.ipynb | 2 +- jupyter/transfer_learning_on_cifar10.ipynb | 16 +++++------ .../01_create_your_first_network.ipynb | 8 +++--- .../tutorial/02_train_your_first_model.ipynb | 10 +++---- ...image_classification_with_your_model.ipynb | 10 +++---- ml/xgboost/src/main/javadoc/overview.html | 2 +- model-zoo/src/main/javadoc/overview.html | 2 +- .../src/main/javadoc/overview.html | 2 +- .../mxnet/zoo/nlp/qa/BertQAModelLoader.java | 4 +-- .../src/main/javadoc/overview.html | 2 +- mxnet/native/build.gradle | 6 ++-- .../src/main/javadoc/overview.html | 2 +- .../src/main/javadoc/overview.html | 2 +- .../src/main/javadoc/overview.html | 2 +- paddlepaddle/paddlepaddle-native/build.gradle | 6 ++-- .../src/main/javadoc/overview.html | 2 +- .../src/main/javadoc/overview.html | 2 +- pytorch/pytorch-native/build.gradle | 6 ++-- .../serving/src/main/javadoc/overview.html | 2 +- .../src/main/javadoc/overview.html | 2 +- .../src/main/javadoc/overview.html | 2 +- tensorflow/tensorflow-native/build.gradle | 6 ++-- .../src/main/javadoc/overview.html | 2 +- tflite/tflite-native/build.gradle | 6 ++-- tools/gradle/java-formatter.gradle | 2 +- tools/gradle/publish.gradle | 6 ++-- tools/scripts/add_online_runner.py | 2 +- website/javadoc.html | 4 +-- website/js/index.js | 8 +++--- 118 files changed, 270 insertions(+), 269 deletions(-) diff --git a/.github/workflows/continuous.yml b/.github/workflows/continuous.yml index ea5ed642ad8..cfbf532382e 100644 --- a/.github/workflows/continuous.yml +++ b/.github/workflows/continuous.yml @@ -20,7 +20,7 @@ on: jobs: build: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ${{ matrix.operating-system }} strategy: matrix: @@ -127,17 +127,17 @@ jobs: with: name: tensorflow-model-zoo path: tensorflow/tensorflow-model-zoo/build/reports - - name: upload to codecov - uses: codecov/codecov-action@v1.4.0 - with: - files: ./build/reports/jacoco/jacocoRootReport/jacocoRootReport.xml - name: codecov-umbrella - fail_ci_if_error: true - path_to_write_report: ./codecov_report.txt +# - name: upload to codecov +# uses: codecov/codecov-action@v1.4.1 +# with: +# files: ./build/reports/jacoco/jacocoRootReport/jacocoRootReport.xml +# name: codecov-umbrella +# fail_ci_if_error: true +# path_to_write_report: ./codecov_report.txt # Windows platform for testing hybrid engines build-windows: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: windows-latest steps: - uses: actions/checkout@v1 diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index bd0ef066432..cac7eac348b 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -12,7 +12,7 @@ on: jobs: documentation: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ubuntu-18.04 steps: - name: Set up JDK 11 diff --git a/.github/workflows/native_jni_s3_paddle.yml b/.github/workflows/native_jni_s3_paddle.yml index f217bb1b318..25fdd9bc644 100644 --- a/.github/workflows/native_jni_s3_paddle.yml +++ b/.github/workflows/native_jni_s3_paddle.yml @@ -7,7 +7,7 @@ on: jobs: build-paddle-jni-cpu: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ${{ matrix.operating-system }} strategy: matrix: @@ -35,7 +35,7 @@ jobs: path: paddlepaddle/paddlepaddle-native/jnilib publish: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ubuntu-18.04 needs: [build-paddle-jni-cpu] steps: diff --git a/.github/workflows/native_jni_s3_pytorch.yml b/.github/workflows/native_jni_s3_pytorch.yml index d60d4c235a5..43e68ec5e33 100644 --- a/.github/workflows/native_jni_s3_pytorch.yml +++ b/.github/workflows/native_jni_s3_pytorch.yml @@ -7,7 +7,7 @@ on: jobs: build-pytorch-jni-cpu: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ${{ matrix.operating-system }} strategy: matrix: @@ -43,7 +43,7 @@ jobs: aws cloudfront create-invalidation --distribution-id E371VB8JQ6NRVY --paths "/pytorch-1.8.1/jnilib*" build-pytorch-jni-linux-gpu: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ubuntu-latest container: nvidia/cuda:10.2-cudnn7-devel-ubuntu16.04 steps: @@ -82,7 +82,7 @@ jobs: aws cloudfront create-invalidation --distribution-id E371VB8JQ6NRVY --paths "/pytorch-1.8.1/jnilib*" build-pytorch-jni-precxx11: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ubuntu-latest container: nvidia/cuda:10.1-cudnn7-devel-centos7 steps: @@ -120,7 +120,7 @@ jobs: aws cloudfront create-invalidation --distribution-id E371VB8JQ6NRVY --paths "/pytorch-1.8.1/jnilib/precxx11*" build-pytorch-jni-windows-gpu: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: windows-latest steps: - uses: actions/checkout@v1 diff --git a/.github/workflows/native_jni_s3_pytorch_android.yml b/.github/workflows/native_jni_s3_pytorch_android.yml index 9b038c7ffb4..0426e9dedda 100644 --- a/.github/workflows/native_jni_s3_pytorch_android.yml +++ b/.github/workflows/native_jni_s3_pytorch_android.yml @@ -5,7 +5,7 @@ on: jobs: build-pytorch-jni-android: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ubuntu-18.04 env: NDK_VERSION: "20.0.5594570" @@ -36,7 +36,7 @@ jobs: path: pytorch/pytorch-native/jnilib publish: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ubuntu-18.04 needs: [build-pytorch-jni-android] steps: diff --git a/.github/workflows/nightly_publish.yml b/.github/workflows/nightly_publish.yml index 0125224a985..482d5faca34 100644 --- a/.github/workflows/nightly_publish.yml +++ b/.github/workflows/nightly_publish.yml @@ -12,7 +12,7 @@ on: jobs: build: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ${{ matrix.operating-system }} strategy: matrix: @@ -103,7 +103,7 @@ jobs: path: tensorflow/tensorflow-model-zoo/build/reports test-pytorch: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ${{ matrix.operating-system }} strategy: matrix: @@ -126,7 +126,7 @@ jobs: run: ./gradlew :integration:test "-Dai.djl.default_engine=PyTorch" test-tensorflow: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ${{ matrix.operating-system }} strategy: matrix: @@ -149,7 +149,7 @@ jobs: run: ./gradlew :integration:test "-Dai.djl.default_engine=TensorFlow" publish: - if: github.repository == 'awslabs/djl' + if: github.repository == 'deepjavalibrary/djl' runs-on: ubuntu-18.04 needs: [ build, test-pytorch, test-tensorflow ] steps: diff --git a/README.md b/README.md index e8dd2590d76..35ea75f6480 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,11 @@ ![DeepJavaLibrary](website/img/deepjavalibrary.png?raw=true "Deep Java Library") -![Continuous](https://github.com/awslabs/djl/workflows/Continuous/badge.svg) -![Continuous PyTorch](https://github.com/awslabs/djl/workflows/Continous%20PyTorch/badge.svg) -![Continuous Tensorflow](https://github.com/awslabs/djl/workflows/Continuous%20Tensorflow/badge.svg) -![Docs](https://github.com/awslabs/djl/workflows/Docs/badge.svg) -![Nightly Publish](https://github.com/awslabs/djl/workflows/Nightly%20Publish/badge.svg) +![Continuous](https://github.com/deepjavalibrary/djl/workflows/Continuous/badge.svg) +![Continuous PyTorch](https://github.com/deepjavalibrary/djl/workflows/Continous%20PyTorch/badge.svg) +![Continuous Tensorflow](https://github.com/deepjavalibrary/djl/workflows/Continuous%20Tensorflow/badge.svg) +![Docs](https://github.com/deepjavalibrary/djl/workflows/Docs/badge.svg) +![Nightly Publish](https://github.com/deepjavalibrary/djl/workflows/Nightly%20Publish/badge.svg) # Deep Java Library (DJL) @@ -87,15 +87,15 @@ The following pseudocode demonstrates running training: - [JavaDoc API Reference](https://javadoc.djl.ai/) ## Release Notes -* [0.10.0](https://github.com/awslabs/djl/releases/tag/v0.10.0) ([Code](https://github.com/awslabs/djl/tree/v0.10.0)) -* [0.9.0](https://github.com/awslabs/djl/releases/tag/v0.9.0) ([Code](https://github.com/awslabs/djl/tree/v0.9.0)) -* [0.8.0](https://github.com/awslabs/djl/releases/tag/v0.8.0) ([Code](https://github.com/awslabs/djl/tree/v0.8.0)) -* [0.6.0](https://github.com/awslabs/djl/releases/tag/v0.6.0) ([Code](https://github.com/awslabs/djl/tree/v0.6.0)) -* [0.5.0](https://github.com/awslabs/djl/releases/tag/v0.5.0) ([Code](https://github.com/awslabs/djl/tree/v0.5.0)) -* [0.4.0](https://github.com/awslabs/djl/releases/tag/v0.4.0) ([Code](https://github.com/awslabs/djl/tree/v0.4.0)) -* [0.3.0](https://github.com/awslabs/djl/releases/tag/v0.3.0) ([Code](https://github.com/awslabs/djl/tree/v0.3.0)) -* [0.2.1](https://github.com/awslabs/djl/releases/tag/v0.2.1) ([Code](https://github.com/awslabs/djl/tree/v0.2.1)) -* [0.2.0 Initial release](https://github.com/awslabs/djl/releases/tag/v0.2.0) ([Code](https://github.com/awslabs/djl/tree/v0.2.0)) +* [0.10.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.10.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.10.0)) +* [0.9.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.9.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.9.0)) +* [0.8.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.8.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.8.0)) +* [0.6.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.6.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.6.0)) +* [0.5.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.5.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.5.0)) +* [0.4.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.4.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.4.0)) +* [0.3.0](https://github.com/deepjavalibrary/djl/releases/tag/v0.3.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.3.0)) +* [0.2.1](https://github.com/deepjavalibrary/djl/releases/tag/v0.2.1) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.2.1)) +* [0.2.0 Initial release](https://github.com/deepjavalibrary/djl/releases/tag/v0.2.0) ([Code](https://github.com/deepjavalibrary/djl/tree/v0.2.0)) ## Building From Source diff --git a/android/core/build.gradle b/android/core/build.gradle index cf73f3e7291..e67388c174c 100644 --- a/android/core/build.gradle +++ b/android/core/build.gradle @@ -60,9 +60,9 @@ afterEvaluate { } scm { - connection = "scm:git:git@github.com:awslabs/djl.git" - developerConnection = "scm:git:git@github.com:awslabs/djl.git" - url = "https://github.com/awslabs/djl" + connection = "scm:git:git@github.com:deepjavalibrary/djl.git" + developerConnection = "scm:git:git@github.com:deepjavalibrary/djl.git" + url = "https://github.com/deepjavalibrary/djl" tag = "HEAD" } diff --git a/android/core/src/androidTest/java/ai/djl/android/core/BitmapWrapperTest.java b/android/core/src/androidTest/java/ai/djl/android/core/BitmapWrapperTest.java index 0eb20a7e3cc..921d7fabcce 100644 --- a/android/core/src/androidTest/java/ai/djl/android/core/BitmapWrapperTest.java +++ b/android/core/src/androidTest/java/ai/djl/android/core/BitmapWrapperTest.java @@ -61,7 +61,7 @@ public void useAppContext() { public void testImageToNDArray() throws IOException { try (NDManager manager = NDManager.newBaseManager()) { ImageFactory factory = ImageFactory.getInstance(); - Image img = factory.fromUrl("https://github.com/awslabs/djl/raw/master/examples/src/test/resources/dog_bike_car.jpg"); + Image img = factory.fromUrl("https://github.com/deepjavalibrary/djl/raw/master/examples/src/test/resources/dog_bike_car.jpg"); NDArray array = img.toNDArray(manager); assertEquals(new Shape(img.getHeight(), img.getWidth(), 3), array.getShape()); } @@ -71,7 +71,7 @@ public void testImageToNDArray() throws IOException { public void testImageGetSubImage() throws IOException { try (NDManager manager = NDManager.newBaseManager()) { ImageFactory factory = ImageFactory.getInstance(); - Image img = factory.fromUrl("https://github.com/awslabs/djl/raw/master/examples/src/test/resources/dog_bike_car.jpg"); + Image img = factory.fromUrl("https://github.com/deepjavalibrary/djl/raw/master/examples/src/test/resources/dog_bike_car.jpg"); NDArray array = img.toNDArray(manager); Image subImg = img.getSubimage(10, 20, 30, 40); NDArray subArray = subImg.toNDArray(manager); @@ -83,7 +83,7 @@ public void testImageGetSubImage() throws IOException { public void testImageDuplicate() throws IOException { try (NDManager manager = NDManager.newBaseManager()) { ImageFactory factory = ImageFactory.getInstance(); - Image img = factory.fromUrl("https://github.com/awslabs/djl/raw/master/examples/src/test/resources/dog_bike_car.jpg"); + Image img = factory.fromUrl("https://github.com/deepjavalibrary/djl/raw/master/examples/src/test/resources/dog_bike_car.jpg"); NDArray array = img.toNDArray(manager); NDArray duplicate = img.duplicate(Image.Type.TYPE_INT_ARGB).toNDArray(manager); assertEquals(array, duplicate); diff --git a/android/pytorch-native/build.gradle b/android/pytorch-native/build.gradle index 4a96ff69ed7..d1f335c8d03 100644 --- a/android/pytorch-native/build.gradle +++ b/android/pytorch-native/build.gradle @@ -59,9 +59,9 @@ afterEvaluate { } scm { - connection = "scm:git:git@github.com:awslabs/djl.git" - developerConnection = "scm:git:git@github.com:awslabs/djl.git" - url = "https://github.com/awslabs/djl" + connection = "scm:git:git@github.com:deepjavalibrary/djl.git" + developerConnection = "scm:git:git@github.com:deepjavalibrary/djl.git" + url = "https://github.com/deepjavalibrary/djl" tag = "HEAD" } diff --git a/api/src/main/java/ai/djl/engine/Engine.java b/api/src/main/java/ai/djl/engine/Engine.java index 9a786dfcfd6..39aabb0f4b3 100644 --- a/api/src/main/java/ai/djl/engine/Engine.java +++ b/api/src/main/java/ai/djl/engine/Engine.java @@ -112,7 +112,7 @@ public static Engine getInstance() { throw new EngineException( "No deep learning engine found." + System.lineSeparator() - + "Please refer to https://github.com/awslabs/djl/blob/master/docs/development/troubleshooting.md for more details."); + + "Please refer to https://github.com/deepjavalibrary/djl/blob/master/docs/development/troubleshooting.md for more details."); } return getEngine(System.getProperty("ai.djl.default_engine", DEFAULT_ENGINE)); } diff --git a/api/src/main/java/ai/djl/inference/Predictor.java b/api/src/main/java/ai/djl/inference/Predictor.java index a69653ff921..951b064ccf6 100644 --- a/api/src/main/java/ai/djl/inference/Predictor.java +++ b/api/src/main/java/ai/djl/inference/Predictor.java @@ -54,13 +54,13 @@ * * * @@ -71,7 +71,7 @@ * @see The guide on memory * management * @see The + * href="https://github.com/deepjavalibrary/djl/blob/master/examples/docs/multithread_inference.md">The * guide on running multi-threaded inference * @see The * guide on inference performance optimization diff --git a/api/src/main/java/ai/djl/metric/Metrics.java b/api/src/main/java/ai/djl/metric/Metrics.java index b2ffe98be0b..82ae6840058 100644 --- a/api/src/main/java/ai/djl/metric/Metrics.java +++ b/api/src/main/java/ai/djl/metric/Metrics.java @@ -36,7 +36,7 @@ * latencies, CPU and GPU memory consumption, losses, etc. * *

For more details about using the metrics, see the metrics + * href="https://github.com/deepjavalibrary/djl/blob/master/docs/how_to_collect_metrics.md">metrics * tutorial. */ public class Metrics { diff --git a/api/src/main/java/ai/djl/ndarray/NDArray.java b/api/src/main/java/ai/djl/ndarray/NDArray.java index fe4059792f2..a15d9a3b4c7 100644 --- a/api/src/main/java/ai/djl/ndarray/NDArray.java +++ b/api/src/main/java/ai/djl/ndarray/NDArray.java @@ -37,7 +37,7 @@ * multidimensional, fixed-size homogeneous array. It has very similar behaviour to the Numpy python * package with the addition of efficient computing. To understand how to manage NDArray lifecycle, * please refer to NDArray + * href="https://github.com/deepjavalibrary/djl/blob/master/docs/development/memory_management.md">NDArray * Memory Management Guide */ public interface NDArray extends NDResource { diff --git a/api/src/main/java/ai/djl/ndarray/NDManager.java b/api/src/main/java/ai/djl/ndarray/NDManager.java index e5d804a1559..dae10ec8c99 100644 --- a/api/src/main/java/ai/djl/ndarray/NDManager.java +++ b/api/src/main/java/ai/djl/ndarray/NDManager.java @@ -96,7 +96,7 @@ * @see Translator * @see TranslatorContext#getNDManager() * @see NDArray + * href="https://github.com/deepjavalibrary/djl/blob/master/docs/development/memory_management.md">NDArray * Memory Management Guide */ public interface NDManager extends AutoCloseable { diff --git a/api/src/main/java/ai/djl/nn/Block.java b/api/src/main/java/ai/djl/nn/Block.java index 4bd3fb09e2b..0efe57bda9a 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 + * href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/tutorial/01_create_your_first_network.ipynb">this * tutorial on creating your first network * @see The * D2L chapter on blocks and *

  • Training + * href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/tutorial/02_train_your_first_model.ipynb">Training * your first model *
  • Training + * href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/transfer_learning_on_cifar10.ipynb">Training * using transfer learning *
  • Inference + * href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/load_mxnet_model.ipynb">Inference * with an MXNet model * * diff --git a/api/src/main/java/ai/djl/training/loss/SimpleCompositeLoss.java b/api/src/main/java/ai/djl/training/loss/SimpleCompositeLoss.java index 8a630ad73c0..ccaa46d7c14 100644 --- a/api/src/main/java/ai/djl/training/loss/SimpleCompositeLoss.java +++ b/api/src/main/java/ai/djl/training/loss/SimpleCompositeLoss.java @@ -26,7 +26,7 @@ * AbstractCompositeLoss}. * *

    For an example of using this loss, see the + * href="https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/training/TrainCaptcha.java">the * captcha training example. */ public class SimpleCompositeLoss extends AbstractCompositeLoss { diff --git a/api/src/main/javadoc/overview.html b/api/src/main/javadoc/overview.html index 4ffefcac900..4e7be7d5eac 100644 --- a/api/src/main/javadoc/overview.html +++ b/api/src/main/javadoc/overview.html @@ -41,7 +41,7 @@ } -

    More tutorials, documents, and examples can be on our GitHub repository. +

    More tutorials, documents, and examples can be on our GitHub repository. diff --git a/basicdataset/src/main/javadoc/overview.html b/basicdataset/src/main/javadoc/overview.html index fa1b68021f9..b314e450e55 100644 --- a/basicdataset/src/main/javadoc/overview.html +++ b/basicdataset/src/main/javadoc/overview.html @@ -7,7 +7,7 @@

    The basic datasets module contains a number of built-in datasets that can be used for deep learning. - See here for more details. + See here for more details.

    diff --git a/bom/build.gradle b/bom/build.gradle index c8bdc2b408d..dde608baa66 100644 --- a/bom/build.gradle +++ b/bom/build.gradle @@ -73,9 +73,9 @@ publishing { } scm { - connection = "scm:git:git@github.com:awslabs/djl.git" - developerConnection = "scm:git:git@github.com:awslabs/djl.git" - url = "https://github.com/awslabs/djl" + connection = "scm:git:git@github.com:deepjavalibrary/djl.git" + developerConnection = "scm:git:git@github.com:deepjavalibrary/djl.git" + url = "https://github.com/deepjavalibrary/djl" tag = "HEAD" } diff --git a/djl-zero/src/main/javadoc/overview.html b/djl-zero/src/main/javadoc/overview.html index 2c10f0850f0..21709412c73 100644 --- a/djl-zero/src/main/javadoc/overview.html +++ b/djl-zero/src/main/javadoc/overview.html @@ -7,7 +7,7 @@

    The zero module contains a zero deep learning knowledge required wrapper over DJL. - See here for more details. + See here for more details.

    diff --git a/dlr/dlr-engine/src/main/javadoc/overview.html b/dlr/dlr-engine/src/main/javadoc/overview.html index 5a4f16746ef..878bbc829fc 100644 --- a/dlr/dlr-engine/src/main/javadoc/overview.html +++ b/dlr/dlr-engine/src/main/javadoc/overview.html @@ -7,7 +7,7 @@

    The DLR Engine module contains the DLR implementation of the DJL EngineProvider. - See here for more details. + See here for more details.

    diff --git a/dlr/dlr-native/build.gradle b/dlr/dlr-native/build.gradle index c5a888615cd..d646b2a00f9 100644 --- a/dlr/dlr-native/build.gradle +++ b/dlr/dlr-native/build.gradle @@ -198,9 +198,9 @@ flavorNames.each { flavor -> } scm { - connection = "scm:git:git@github.com:awslabs/djl.git" - developerConnection = "scm:git:git@github.com:awslabs/djl.git" - url = "https://github.com/awslabs/djl" + connection = "scm:git:git@github.com:deepjavalibrary/djl.git" + developerConnection = "scm:git:git@github.com:deepjavalibrary/djl.git" + url = "https://github.com/deepjavalibrary/djl" tag = "HEAD" } diff --git a/docs/create_serving_ready_model.md b/docs/create_serving_ready_model.md index d7a7440e0a3..ab5e6e73383 100644 --- a/docs/create_serving_ready_model.md +++ b/docs/create_serving_ready_model.md @@ -23,7 +23,7 @@ this class to conduct the data processing. ### Step 1: Create a ServingTranslator class Create a java class that implements [ServingTranslator](https://javadoc.io/doc/ai.djl/api/latest/ai/djl/translate/ServingTranslator.html) -interface. See: [MyTranslator](https://github.com/awslabs/djl/blob/master/integration/src/test/translator/MyTranslator.java) as an example. +interface. See: [MyTranslator](https://github.com/deepjavalibrary/djl/blob/master/integration/src/test/translator/MyTranslator.java) as an example. ### Step 2: Create a `libs` folder in your model directory DJL will look into `libs` folder to search for Translator implementation. diff --git a/docs/dataset.md b/docs/dataset.md index 16c7da4ad58..5bbddeb20e8 100644 --- a/docs/dataset.md +++ b/docs/dataset.md @@ -11,7 +11,7 @@ Machine learning typically works with three datasets: - Validation dataset The validation set is used to evaluate a given model during the training process. It helps machine learning - engineers to fine-tune the [HyperParameter](https://github.com/awslabs/djl/blob/master/api/src/main/java/ai/djl/training/hyperparameter/param/Hyperparameter.java) + engineers to fine-tune the [HyperParameter](https://github.com/deepjavalibrary/djl/blob/master/api/src/main/java/ai/djl/training/hyperparameter/param/Hyperparameter.java) at model development stage. The model doesn't learn from validation dataset; and validation dataset is optional. diff --git a/docs/development/benchmark_with_djl.md b/docs/development/benchmark_with_djl.md index f335b3dd902..a1a2f75d1f0 100644 --- a/docs/development/benchmark_with_djl.md +++ b/docs/development/benchmark_with_djl.md @@ -69,7 +69,7 @@ To start your benchmarking, we need to make sure we provide the following inform - Sample input for the model - (Optional) Multi-thread benchmark -The benchmark script located [here](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/benchmark/Benchmark.java). +The benchmark script located [here](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/benchmark/Benchmark.java). Just do the following: diff --git a/docs/development/configure_logging.md b/docs/development/configure_logging.md index 5271a2b9eed..d720ff56eb6 100644 --- a/docs/development/configure_logging.md +++ b/docs/development/configure_logging.md @@ -47,8 +47,8 @@ See [SimpleLogger](http://www.slf4j.org/api/org/slf4j/impl/SimpleLogger.html) fo ### Use log4j2 -In our examples module, we use [log4j2 binding](https://github.com/awslabs/djl/blob/master/examples/build.gradle#L13). -While using log4j2 binding, you also need add a [log4j2.xml](https://github.com/awslabs/djl/blob/master/examples/src/main/resources/log4j2.xml) file. +In our examples module, we use [log4j2 binding](https://github.com/deepjavalibrary/djl/blob/master/examples/build.gradle#L13). +While using log4j2 binding, you also need add a [log4j2.xml](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/resources/log4j2.xml) file. ### Use logback @@ -71,7 +71,7 @@ or for Maven: ## Configure logging level -`log4j2` allows you to customize logging level using system properties. See our examples [log4j2.xml](https://github.com/awslabs/djl/blob/master/examples/src/main/resources/log4j2.xml#L13). +`log4j2` allows you to customize logging level using system properties. See our examples [log4j2.xml](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/resources/log4j2.xml#L13). With this configuration, you can easily change your logging level using java command line options: ```shell diff --git a/docs/development/development_guideline.md b/docs/development/development_guideline.md index d24ef13529e..b6733b5f35b 100644 --- a/docs/development/development_guideline.md +++ b/docs/development/development_guideline.md @@ -30,7 +30,7 @@ Script '/Volumes/Unix/projects/Joule/tools/gradle/formatter.gradle' line: 57 * What went wrong: Execution failed for task ':api:verifyJava'. > File not formatted: /Volumes/Unix/projects/Joule/api/src/main/java/ai/djl/nn/convolutional/Conv2d.java - See https://github.com/awslabs/djl/blob/master/docs/development/development_guideline.md#coding-conventions for formatting instructions + See https://github.com/deepjavalibrary/djl/blob/master/docs/development/development_guideline.md#coding-conventions for formatting instructions ``` If you do fail the format check, the easiest way to resolve it is to run the gradle `formatJava` target to reformat your code. It may be helpful to just run the formatter before you build the project rather than waiting for the formatting verification to fail. diff --git a/docs/development/how_to_use_dataset.md b/docs/development/how_to_use_dataset.md index 31dd50221b1..37df16b49fb 100644 --- a/docs/development/how_to_use_dataset.md +++ b/docs/development/how_to_use_dataset.md @@ -176,8 +176,8 @@ Since we don't have to prepare any data on our own for this example, we only hav public void prepare(Progress progress) {} ``` -There are great [examples](https://github.com/awslabs/djl/blob/master/basicdataset/src/main/java/ai/djl/basicdataset/nlp/AmazonReview.java) -in our [basicdataset](https://github.com/awslabs/djl/blob/master/basicdataset/src/main/java/ai/djl/basicdataset) +There are great [examples](https://github.com/deepjavalibrary/djl/blob/master/basicdataset/src/main/java/ai/djl/basicdataset/nlp/AmazonReview.java) +in our [basicdataset](https://github.com/deepjavalibrary/djl/blob/master/basicdataset/src/main/java/ai/djl/basicdataset) folder that show use cases for `prepare()`. @@ -226,4 +226,4 @@ for (Batch batch : dataset.getData(model.getNDManager())) { } ``` -Full example code could be found in [CSVDataset.java](https://github.com/awslabs/djl/blob/master/docs/development/CSVDataset.java). +Full example code could be found in [CSVDataset.java](https://github.com/deepjavalibrary/djl/blob/master/docs/development/CSVDataset.java). diff --git a/docs/development/inference_performance_optimization.md b/docs/development/inference_performance_optimization.md index 903292ab688..6fd59c3fe3e 100644 --- a/docs/development/inference_performance_optimization.md +++ b/docs/development/inference_performance_optimization.md @@ -12,7 +12,7 @@ memory consumption compare to Python. DJL `Predictor` is not designed to be thread-safe (although some implementation is), we recommend creating a new [Predictor](https://javadoc.io/doc/ai.djl/api/latest/ai/djl/inference/Predictor.html) for each thread. -For a reference implementation, see [Multi-threaded Benchmark](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/benchmark/MultithreadedBenchmark.java). +For a reference implementation, see [Multi-threaded Benchmark](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/benchmark/MultithreadedBenchmark.java). you need to set corresponding configuration based on the engine you want to use. @@ -82,7 +82,7 @@ You can find more detail in [PyTorch](https://pytorch.org/docs/stable/notes/cpu_ ### Multithreading Inference You can follow the same steps as other engines for running multithreading inference using TensorFlow engine. It's recommended to use one `Predictor` for each thread and avoid using a new `Predictor` for each inference call. -You can refer to our [Multithreading Benchmark](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/benchmark/MultithreadedBenchmark.java) as an example, +You can refer to our [Multithreading Benchmark](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/benchmark/MultithreadedBenchmark.java) as an example, here is how to run it using TensorFlow engine. ```bash diff --git a/docs/development/memory_management.md b/docs/development/memory_management.md index c23cae1167a..ec63859403f 100644 --- a/docs/development/memory_management.md +++ b/docs/development/memory_management.md @@ -9,8 +9,8 @@ to help us release the native memory. We design the NDManager in tree structure. It provides fine-grained control of native resource and manage the resource scope in more effectively way. NDManager can make any kind of tree. However, using the Predictor/Trainer classes will automatically create a certain kind of tree. -The structure of the NDManager for the classic inference case is like ![structure of the NDManager](https://raw.githubusercontent.com/awslabs/djl/master/docs/development/img/ndmanager_structure_for_inference.png). -The structure of the NDManager for the classic training case is like ![structure of the NDManager](https://github.com/awslabs/djl/blob/master/docs/development/img/ndmanager_structure_for_training.png?raw=true). +The structure of the NDManager for the classic inference case is like ![structure of the NDManager](https://raw.githubusercontent.com/deepjavalibrary/djl/master/docs/development/img/ndmanager_structure_for_inference.png). +The structure of the NDManager for the classic training case is like ![structure of the NDManager](https://github.com/deepjavalibrary/djl/blob/master/docs/development/img/ndmanager_structure_for_training.png?raw=true). The topmost is System NDManager. The model, which is one layer below, contains the weight and bias of the Neural Network. The bottommost NDManager takes care of the intermediate NDArrays we would like to close as soon as the program exit the scope of the functions that use them. diff --git a/docs/development/troubleshooting.md b/docs/development/troubleshooting.md index eaa30317ca1..951d252cab1 100644 --- a/docs/development/troubleshooting.md +++ b/docs/development/troubleshooting.md @@ -29,7 +29,7 @@ Gradle: ``` implementation "ai.djl.mxnet:mxnet-engine:0.10.0" -// See https://github.com/awslabs/djl/blob/master/mxnet/mxnet-engine/README.md for more MXNet library selection options +// See https://github.com/deepjavalibrary/djl/blob/master/mxnet/mxnet-engine/README.md for more MXNet library selection options runtimeOnly "ai.djl.mxnet:mxnet-native-auto:1.7.0-backport" ``` @@ -43,7 +43,7 @@ Maven: ai.djl.mxnet mxnet-native-auto @@ -84,7 +84,7 @@ libtorch.dylib: ``` It shows the `libtorch.dylib` depends on `libiomp5.dylib` and `libc10.dylib`. If one of them is missing, it throws an `UnsatisfiedLinkError` exception. -If you are using `ai.djl.{engine}:{engine}-native-auto`, please create an issue at `https://github.com/awslabs/djl`. +If you are using `ai.djl.{engine}:{engine}-native-auto`, please create an issue at `https://github.com/deepjavalibrary/djl`. **Windows** @@ -104,7 +104,7 @@ DJL on Windows, please download and install CN: 如果您在中国，可以使用 [DirectX 修复工具](https://blog.csdn.net/VBcom/article/details/6962388) 来安装遗失依赖项。 -If the issue continues to persist, you can use the [docker file](https://github.com/awslabs/djl/blob/master/docker/windows/Dockerfile) provided by us. +If the issue continues to persist, you can use the [docker file](https://github.com/deepjavalibrary/djl/blob/master/docker/windows/Dockerfile) provided by us. Please note that this docker will only work with Windows server 2019 by default. If you want it to work with other versions of Windows, you need to pass the version as an argument as follows: @@ -146,9 +146,9 @@ Then, right click the `log4j2.xml` file and select `Recompile log4j2.xml`. ## 4. How to run DJL using other versions of Apache MXNet? **Note:** this is not officially supported by DJL, and some functions may not work. -If you require features in Apache MXNet not provided by DJL, please submit an [issue](https://github.com/awslabs/djl/issues). +If you require features in Apache MXNet not provided by DJL, please submit an [issue](https://github.com/deepjavalibrary/djl/issues). -By default, DJL is running on the [MXNet engine](https://github.com/awslabs/djl/tree/master/mxnet/mxnet-engine). +By default, DJL is running on the [MXNet engine](https://github.com/deepjavalibrary/djl/tree/master/mxnet/mxnet-engine). We use `mxnet-mkl` on CPU machines and `mxnet-cu102mkl` on GPU machines. `mkl` means [Intel-MKLDNN](https://github.com/intel/mkl-dnn) is enabled. `cu102` means [Nvidia CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit) version 10.2 is enabled. diff --git a/docs/faq.md b/docs/faq.md index bc46829a19b..2f0685b24d2 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -37,7 +37,7 @@ 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/deepjavalibrary/djl/blob/master/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 diff --git a/docs/forums.md b/docs/forums.md index e3e6deed2c4..010e91a90d9 100644 --- a/docs/forums.md +++ b/docs/forums.md @@ -4,13 +4,13 @@ There are many forums where the DJL community interacts and ideas for DJL are di ## Questions -The best place to ask questions is on the [Discussions Q&A](https://github.com/awslabs/djl/discussions/categories/q-a). You can also join the [DJL Slack](http://tiny.cc/djl_slack) under the `#help` channel. +The best place to ask questions is on the [Discussions Q&A](https://github.com/deepjavalibrary/djl/discussions/categories/q-a). You can also join the [DJL Slack](http://tiny.cc/djl_slack) under the `#help` channel. ## Ideas -If you have an idea or a feature request for DJL, there are a few places you can share it. If it is an enhancement for DJL directly, you can open an [enhancement issue](https://github.com/awslabs/djl/issues/new?assignees=&labels=enhancement&template=feature_request.md&title=). +If you have an idea or a feature request for DJL, there are a few places you can share it. If it is an enhancement for DJL directly, you can open an [enhancement issue](https://github.com/deepjavalibrary/djl/issues/new?assignees=&labels=enhancement&template=feature_request.md&title=). -If you think that the issue requires some discussion to determine quite what to do, you may want to start a discussion before branching it out into an issue. The best places for those discussions are the [ideas forum](https://github.com/awslabs/djl/discussions/categories/ideas) if the issue might attract broader interest or the [development Forum](https://github.com/awslabs/djl/discussions/categories/development) to share it only with the more focused DJL API developers. +If you think that the issue requires some discussion to determine quite what to do, you may want to start a discussion before branching it out into an issue. The best places for those discussions are the [ideas forum](https://github.com/deepjavalibrary/djl/discussions/categories/ideas) if the issue might attract broader interest or the [development Forum](https://github.com/deepjavalibrary/djl/discussions/categories/development) to share it only with the more focused DJL API developers. Once you decide on a forum, there are two main formats your discussion might take. If you want to just raise a question, use the question as the name of your post and people can discuss the question. If you have a proposal and you are looking for input before you start making changes, you should frame it as a Request for Comments (RFC). The RFC doesn't have a template at this time, so just add `[RFC]` before your proposal title. @@ -18,23 +18,23 @@ Don't forget that DJL is a community project. If you have an idea, you are alway ## Bug Report -Bugs happen. If you see something wrong with DJL or just aren't sure, you should open a [bug issue](https://github.com/awslabs/djl/issues/new?assignees=&labels=bug&template=bug_report.md&title=). Make sure to give us enough information to help figure out what is wrong and how to fix it. +Bugs happen. If you see something wrong with DJL or just aren't sure, you should open a [bug issue](https://github.com/deepjavalibrary/djl/issues/new?assignees=&labels=bug&template=bug_report.md&title=). Make sure to give us enough information to help figure out what is wrong and how to fix it. ## Share projects, blog posts, talks, etc. -If you have a cool project with DJL, wrote a blog post, or are giving a talk, we would love to hear about it! You should write a short post it on our [show-and-tell](https://github.com/awslabs/djl/discussions/categories/show-and-tell). If you mention us on [twitter](https://twitter.com/deepjavalibrary), we can also retweet it. Once we learn about it, we will also add it to our monthly news update as well. You can also use You can also join the [DJL Slack](http://tiny.cc/djl_slack) under the `#projects` channel. +If you have a cool project with DJL, wrote a blog post, or are giving a talk, we would love to hear about it! You should write a short post it on our [show-and-tell](https://github.com/deepjavalibrary/djl/discussions/categories/show-and-tell). If you mention us on [twitter](https://twitter.com/deepjavalibrary), we can also retweet it. Once we learn about it, we will also add it to our monthly news update as well. You can also use You can also join the [DJL Slack](http://tiny.cc/djl_slack) under the `#projects` channel. ## Research -If you like following the latest research and are looking for a place to discuss, check out our [deep learning discussions](https://github.com/awslabs/djl/discussions/categories/deep-learning). There, you can find other people interested in the theory of deep learning. You can also join the [DJL Slack](http://tiny.cc/djl_slack) under the `#deep-learning` channel. +If you like following the latest research and are looking for a place to discuss, check out our [deep learning discussions](https://github.com/deepjavalibrary/djl/discussions/categories/deep-learning). There, you can find other people interested in the theory of deep learning. You can also join the [DJL Slack](http://tiny.cc/djl_slack) under the `#deep-learning` channel. ## Development -If you want to talk about the development of DJL itself, look at our [development discussions](https://github.com/awslabs/djl/discussions/categories/development). You can also join the [DJL Slack](http://tiny.cc/djl_slack) under the `#development` channel. +If you want to talk about the development of DJL itself, look at our [development discussions](https://github.com/deepjavalibrary/djl/discussions/categories/development). You can also join the [DJL Slack](http://tiny.cc/djl_slack) under the `#development` channel. ## Pull Request -If you have an idea that you want to implement for changes to DJL, a bug fix, new datasets, new models, or anything else, open a new [pull request](https://github.com/awslabs/djl/compare). You can view this guide on [git and how to fork the project and make a pull request](https://guides.github.com/activities/forking/). We also have [documentation for contributors](development/README.md) that can help setup development, explain DJL coding conventions, working with DJL CI, and troubleshooting common problems. +If you have an idea that you want to implement for changes to DJL, a bug fix, new datasets, new models, or anything else, open a new [pull request](https://github.com/deepjavalibrary/djl/compare). You can view this guide on [git and how to fork the project and make a pull request](https://guides.github.com/activities/forking/). We also have [documentation for contributors](development/README.md) that can help setup development, explain DJL coding conventions, working with DJL CI, and troubleshooting common problems. ## Follow DJL @@ -48,4 +48,4 @@ You can also find DJL updates in our monthly newsletter including all features, ## General -If you have read through these forums and nothing seems quite right, you can share it on [general discussions](https://github.com/awslabs/djl/discussions/categories/general). We try to keep our discussions organized so people can find what they are looking for. If you aren't sure about your choice of channel, don't worry about it and just use something that seems reasonable. You can also join the [DJL Slack](http://tiny.cc/djl_slack) under the `#random` channel. +If you have read through these forums and nothing seems quite right, you can share it on [general discussions](https://github.com/deepjavalibrary/djl/discussions/categories/general). We try to keep our discussions organized so people can find what they are looking for. If you aren't sure about your choice of channel, don't worry about it and just use something that seems reasonable. You can also join the [DJL Slack](http://tiny.cc/djl_slack) under the `#random` channel. diff --git a/docs/interactive_tool.md b/docs/interactive_tool.md index c259bac97f0..dac38c47a7b 100644 --- a/docs/interactive_tool.md +++ b/docs/interactive_tool.md @@ -44,7 +44,7 @@ Criteria criteria = Criteria.builder() .build(); ZooModel model = ModelZoo.loadModel(criteria); Predictor predictor = model.newPredictor(); -String imageURL = "https://raw.githubusercontent.com/awslabs/djl/master/examples/src/test/resources/kitten.jpg"; +String imageURL = "https://raw.githubusercontent.com/deepjavalibrary/djl/master/examples/src/test/resources/kitten.jpg"; Image image = ImageFactory.getInstance().fromUrl(imageURL); predictor.predict(image); ``` diff --git a/docs/load_model.md b/docs/load_model.md index 6b06597c4c9..216fe1b964e 100644 --- a/docs/load_model.md +++ b/docs/load_model.md @@ -14,7 +14,7 @@ API to load models. The ModelZoo API provides a unified way to load models. The declarative nature of this API allows you to store model information inside a configuration file. This gives you great flexibility to test and deploy your model. -See our reference project: [DJL Spring Boot Starter](https://github.com/awslabs/djl-spring-boot-starter#spring-djl-mxnet-autoconfiguration). +See our reference project: [DJL Spring Boot Starter](https://github.com/deepjavalibrary/djl-spring-boot-starter#spring-djl-mxnet-autoconfiguration). ### Criteria class diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index e1d93d150bc..c73ec688046 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -1,6 +1,6 @@ site_name: Deep Java Library -repo_name: awslabs/djl -repo_url: https://github.com/awslabs/djl/ +repo_name: deepjavalibrary/djl +repo_url: https://github.com/deepjavalibrary/djl/ site_url: https://djl.ai use_directory_urls: false markdown_extensions: @@ -23,7 +23,7 @@ google_analytics: extra: social: - icon: fontawesome/brands/github-alt - link: https://github.com/awslabs/djl + link: https://github.com/deepjavalibrary/djl - icon: fontawesome/brands/twitter link: https://twitter.com/deepjavalibrary - icon: fontawesome/brands/slack @@ -114,7 +114,7 @@ nav: - Import your Paddle model: - English: 'docs/paddlepaddle/how_to_create_paddlepaddle_model.md' - 中文: 'docs/paddlepaddle/how_to_create_paddlepaddle_model_zh.md' - - Facemask detection using PaddlePaddle: + - Facemask detection using PaddlePaddle: - English: 'jupyter/paddlepaddle/face_mask_detection_paddlepaddle.ipynb' - 中文: 'jupyter/paddlepaddle/face_mask_detection_paddlepaddle_zh.ipynb' - PaddleOCR example: diff --git a/docs/quick_start.md b/docs/quick_start.md index 331254d4ada..2b965d86ae6 100644 --- a/docs/quick_start.md +++ b/docs/quick_start.md @@ -26,13 +26,13 @@ To get started, we recommend that you follow our short [beginner tutorial](../ju ## Run examples -DJL also provides examples for both training and performing inference with deep learning models. You can find the examples and their source code in the [examples](https://github.com/awslabs/djl/tree/master/examples) directory. +DJL also provides examples for both training and performing inference with deep learning models. You can find the examples and their source code in the [examples](https://github.com/deepjavalibrary/djl/tree/master/examples) directory. All of our examples are executed by a simple command. For detailed command line instructions, see each example’s Readme.md file. - [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/awslabs/djl/tree/master/examples) +- [More examples](https://github.com/deepjavalibrary/djl/tree/master/examples) - [Jupyter examples](../jupyter/README.md) ## Other resources diff --git a/docs/roadmap.md b/docs/roadmap.md index ec5ba178e7e..74ee9469939 100644 --- a/docs/roadmap.md +++ b/docs/roadmap.md @@ -1,6 +1,6 @@ # Roadmap -This is the tenative roadmap for DJL through 2021. If there is something you feel is missing from this roadmap, please [raise an issue](https://github.com/awslabs/djl/issues) so we can discuss it. +This is the tenative roadmap for DJL through 2021. If there is something you feel is missing from this roadmap, please [raise an issue](https://github.com/deepjavalibrary/djl/issues) so we can discuss it. - **Quarkus Integration** - Integrate with Quarkus where users can get a smaller memory profile and faster startup time - **Big Data Ecosystem Integration** - Better integration with the JVM Big Data libraries including Apache Spark diff --git a/docs/tensorflow/how_to_import_tensorflow_models_in_DJL.md b/docs/tensorflow/how_to_import_tensorflow_models_in_DJL.md index abcdb22827a..a1e968e568d 100644 --- a/docs/tensorflow/how_to_import_tensorflow_models_in_DJL.md +++ b/docs/tensorflow/how_to_import_tensorflow_models_in_DJL.md @@ -49,8 +49,8 @@ Note that you need to click the download model button to find the actual Google Please refer to these two examples: -1. [Object Detection with TensorFlow](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java) for loading from TensorFlow Hub url. -2. [BERT Classification](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/BertClassification.java) for loading from local downloaded model. +1. [Object Detection with TensorFlow](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java) for loading from TensorFlow Hub url. +2. [BERT Classification](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/BertClassification.java) for loading from local downloaded model. ## How to load TensorFlow Checkpoints diff --git a/examples/README.md b/examples/README.md index 3e3dabf5639..37abaaa69cd 100644 --- a/examples/README.md +++ b/examples/README.md @@ -23,14 +23,14 @@ The following examples are included for inference: These examples focus on the overall experience of training and inference. We keep components that are reusable within separate modules for other users to take advantage of in their own applications. For examples and references on creating datasets, look at the -[basic dataset module](https://github.com/awslabs/djl/tree/master/basicdataset). +[basic dataset module](https://github.com/deepjavalibrary/djl/tree/master/basicdataset). For examples and references on building models and translators, look in our -[basic model zoo](https://github.com/awslabs/djl/tree/master/model-zoo). +[basic model zoo](https://github.com/deepjavalibrary/djl/tree/master/model-zoo). You may be able to find more translator examples in our engine specific model zoos: -[Apache MXNet](https://github.com/awslabs/djl/tree/master/mxnet/mxnet-model-zoo), -[PyTorch](https://github.com/awslabs/djl/tree/master/pytorch/pytorch-model-zoo), -and [TensorFlow](https://github.com/awslabs/djl/tree/master/tensorflow/tensorflow-model-zoo). +[Apache MXNet](https://github.com/deepjavalibrary/djl/tree/master/mxnet/mxnet-model-zoo), +[PyTorch](https://github.com/deepjavalibrary/djl/tree/master/pytorch/pytorch-model-zoo), +and [TensorFlow](https://github.com/deepjavalibrary/djl/tree/master/tensorflow/tensorflow-model-zoo). More examples and demos of applications featuring DJL are located in our [demo repository](https://github.com/aws-samples/djl-demo). diff --git a/examples/docs/BERT_question_and_answer.md b/examples/docs/BERT_question_and_answer.md index da32a60d750..63995e47783 100644 --- a/examples/docs/BERT_question_and_answer.md +++ b/examples/docs/BERT_question_and_answer.md @@ -2,7 +2,7 @@ In this example, you learn how to use the BERT QA model trained by GluonNLP (Apache MXNet) and PyTorch. You can provide the model with a question and a paragraph containing an answer. The model is then able to find the best answer from the answer paragraph. -You can find the source code in [BertQaInference.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/BertQaInference.java). +You can find the source code in [BertQaInference.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/BertQaInference.java). Note that Apache MXNet BERT model has a limitation where the max size of the tokens including the question and the paragraph is 384. diff --git a/examples/docs/action_recognition.md b/examples/docs/action_recognition.md index 38a6c253b42..a3f43cacebd 100644 --- a/examples/docs/action_recognition.md +++ b/examples/docs/action_recognition.md @@ -4,7 +4,7 @@ Action recognition is a computer vision technique to infer human actions (presen In this example, you learn how to implement inference code with a [ModelZoo model](../../docs/model-zoo.md) to detect dogs in an image. -The source code can be found at [ActionRecognition.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ActionRecognition.java). +The source code can be found at [ActionRecognition.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ActionRecognition.java). ## Setup Guide diff --git a/examples/docs/face_detection.md b/examples/docs/face_detection.md index edfd74acc9c..fa3654f6c2c 100644 --- a/examples/docs/face_detection.md +++ b/examples/docs/face_detection.md @@ -3,11 +3,11 @@ In this example, you learn how to implement inference code with a pytorch model to detect faces in an image. Server model: -The source code can be found at [RetinaFaceDetection.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/face/RetinaFaceDetection.java). +The source code can be found at [RetinaFaceDetection.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/face/RetinaFaceDetection.java). The model github can be found at [Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface). Lightweight model: -The source code can be found at [LightFaceDetection.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/face/LightFaceDetection.java). +The source code can be found at [LightFaceDetection.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/face/LightFaceDetection.java). The model github can be found at [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB). ## Setup guide diff --git a/examples/docs/face_recognition.md b/examples/docs/face_recognition.md index b62a7d39278..a9beaf1c645 100644 --- a/examples/docs/face_recognition.md +++ b/examples/docs/face_recognition.md @@ -3,11 +3,11 @@ In this example, you learn how to implement inference code with a pytorch model to extract and compare face features. Extract face feature: -The source code can be found at [FeatureExtraction.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/face/FeatureExtraction.java). +The source code can be found at [FeatureExtraction.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/face/FeatureExtraction.java). The model github can be found at [facenet-pytorch](https://github.com/timesler/facenet-pytorch). Compare face features: -The source code can be found at [FeatureComparison.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/face/FeatureComparison.java). +The source code can be found at [FeatureComparison.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/face/FeatureComparison.java). ## Setup guide diff --git a/examples/docs/image_classification.md b/examples/docs/image_classification.md index 7646f3edab6..8be1ebc65f4 100644 --- a/examples/docs/image_classification.md +++ b/examples/docs/image_classification.md @@ -4,7 +4,7 @@ Image classification refers to the task of extracting information classes from a In this example, you learn how to implement inference code with Deep Java Library (DJL) to recognize handwritten digits from an image. -The image classification example code can be found at [ImageClassification.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java). +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). The Jupyter notebook explains the key concepts in detail. diff --git a/examples/docs/instance_segmentation.md b/examples/docs/instance_segmentation.md index 84d20140984..24e534a4818 100644 --- a/examples/docs/instance_segmentation.md +++ b/examples/docs/instance_segmentation.md @@ -6,7 +6,7 @@ In this example, you learn how to implement inference code with Deep Java Librar The following is the instance segmentation example source code: -[InstanceSegmentation.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/InstanceSegmentation.java). +[InstanceSegmentation.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/InstanceSegmentation.java). ## Setup guide diff --git a/examples/docs/object_detection.md b/examples/docs/object_detection.md index bed299f5507..8f01702733b 100644 --- a/examples/docs/object_detection.md +++ b/examples/docs/object_detection.md @@ -5,7 +5,7 @@ for locating instances of objects in images or videos. In this example, you learn how to implement inference code with a [ModelZoo model](../../docs/model-zoo.md) to detect dogs in an image. -The source code can be found at [ObjectDetection.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java). +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). The Jupyter notebook explains the key concepts in detail. diff --git a/examples/docs/object_detection_with_tensorflow_saved_model.md b/examples/docs/object_detection_with_tensorflow_saved_model.md index 639970c8b7c..e954e5ef9e8 100644 --- a/examples/docs/object_detection_with_tensorflow_saved_model.md +++ b/examples/docs/object_detection_with_tensorflow_saved_model.md @@ -7,7 +7,7 @@ In this example we will use pre-trained model from [tensorflow model zoo](https: The following code has been tested with EfficientDet, SSD MobileNet V2, Faster RCNN Inception Resnet V2, but should work with most of tensorflow object detection models. -The source code can be found at [ObjectDetectionWithTensorflowSavedModel.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ObjectDetectionWithTensorflowSavedModel.java). +The source code can be found at [ObjectDetectionWithTensorflowSavedModel.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ObjectDetectionWithTensorflowSavedModel.java). ## Setup guide diff --git a/examples/docs/pose_estimation.md b/examples/docs/pose_estimation.md index 5c4e0c09ac6..394fc02ab99 100644 --- a/examples/docs/pose_estimation.md +++ b/examples/docs/pose_estimation.md @@ -4,7 +4,7 @@ Pose estimation is a computer vision technique for determining the pose of an ob In this example, you learn how to implement inference code with a [ModelZoo model](../../docs/model-zoo.md) to detect dogs in an image. -The source code can be found at [PoseEstimation.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/PoseEstimation.java). +The source code can be found at [PoseEstimation.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/PoseEstimation.java). ## Setup guide diff --git a/examples/docs/sentiment_analysis.md b/examples/docs/sentiment_analysis.md index b70326c21ec..5b7578f886b 100644 --- a/examples/docs/sentiment_analysis.md +++ b/examples/docs/sentiment_analysis.md @@ -2,7 +2,7 @@ In this example, you learn how to use the DistilBERT model trained by HuggingFace using PyTorch. You can provide the model with a question and a paragraph containing an answer. The model is then able to find the best answer from the answer paragraph. -You can find the source code in [SentimentAnalysis.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/SentimentAnalysis.java). +You can find the source code in [SentimentAnalysis.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/SentimentAnalysis.java). Example: diff --git a/examples/docs/train_amazon_review_ranking.md b/examples/docs/train_amazon_review_ranking.md index 2e3b90019d5..1875177dcc0 100644 --- a/examples/docs/train_amazon_review_ranking.md +++ b/examples/docs/train_amazon_review_ranking.md @@ -4,7 +4,7 @@ In this example, you learn how to train the Amazon Review dataset. This dataset includes 30k reviews from Amazon customers on different products. We only use `review_body` and `star_rating` for data and label. -You can find the example source code in: [TrainAmazonReviewRanking.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/training/transferlearning/TrainAmazonReviewRanking.java). +You can find the example source code in: [TrainAmazonReviewRanking.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/training/transferlearning/TrainAmazonReviewRanking.java). ## Setup guide diff --git a/examples/docs/train_captcha.md b/examples/docs/train_captcha.md index 17e2e6cce46..93e14b37054 100644 --- a/examples/docs/train_captcha.md +++ b/examples/docs/train_captcha.md @@ -2,7 +2,7 @@ In this example, you learn how to train the dataset with multiple inputs and labels. -The source code for this example can be found at [TrainCaptcha.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/training/TrainCaptcha.java). +The source code for this example can be found at [TrainCaptcha.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/training/TrainCaptcha.java). ## Setup guide diff --git a/examples/docs/train_cifar10_resnet.md b/examples/docs/train_cifar10_resnet.md index 43eafc096c7..a7d19d438f1 100644 --- a/examples/docs/train_cifar10_resnet.md +++ b/examples/docs/train_cifar10_resnet.md @@ -3,7 +3,7 @@ In this example, you learn how to train the [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset with Deep Java Library (DJL) using [Transfer Learning](https://en.wikipedia.org/wiki/Transfer_learning). -You can find the example source code in: [TrainResnetWithCifar10.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/training/transferlearning/TrainResnetWithCifar10.java). +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). The Jupyter notebook explains the key concepts in detail. diff --git a/examples/docs/train_mnist_mlp.md b/examples/docs/train_mnist_mlp.md index 6e26474eeb6..700e8302af3 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/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). The Jupyter notebook explains the key concepts in detail. diff --git a/examples/docs/train_pikachu_ssd.md b/examples/docs/train_pikachu_ssd.md index 227a6f8da17..a0daf21e485 100644 --- a/examples/docs/train_pikachu_ssd.md +++ b/examples/docs/train_pikachu_ssd.md @@ -3,8 +3,8 @@ [Object detection](https://en.wikipedia.org/wiki/Object_detection) is a computer vision technique for locating instances of objects in images or videos. In this example, you can find an imperative implemention of an SSD model, and the way to train it using the Pikachu Dataset. The code for the example can be found in -[TrainPikachu.java](https://github.com/awslabs/djl/blob/master/examples/src/main/java/ai/djl/examples/training/TrainPikachu.java). -The code for the implementation of SSD can be found in [SingleShotDetection.java](https://github.com/awslabs/djl/blob/master/model-zoo/src/main/java/ai/djl/basicmodelzoo/cv/object_detection/ssd/SingleShotDetection.java). +[TrainPikachu.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/training/TrainPikachu.java). +The code for the implementation of SSD can be found in [SingleShotDetection.java](https://github.com/deepjavalibrary/djl/blob/master/model-zoo/src/main/java/ai/djl/basicmodelzoo/cv/object_detection/ssd/SingleShotDetection.java). There are no small datasets, like MNIST or Fashion-MNIST, in the object detection field. In order to quickly test models, you are using a small dataset of Pikachu images. It contains a series of background images on which a Pikachu image diff --git a/examples/pom.xml b/examples/pom.xml index 0cf659a7fe3..d60d6c60732 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -71,7 +71,7 @@ ai.djl.mxnet mxnet-native-auto @@ -84,7 +84,7 @@ ai.djl.pytorch pytorch-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 d94c0d8c339..0e7db6cd496 100644 --- a/examples/src/main/java/ai/djl/examples/inference/ActionRecognition.java +++ b/examples/src/main/java/ai/djl/examples/inference/ActionRecognition.java @@ -33,8 +33,8 @@ * An example of inference using an action recognition model. * *

    See this doc for - * information about this example. + * href="https://github.com/deepjavalibrary/djl/blob/master/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 a5218876c35..430c07d3c64 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 +32,11 @@ *

    See: * *

      - *
    • the jupyter + *
    • the jupyter * demo with more information about BERT. *
    • the docs + * href="https://github.com/deepjavalibrary/djl/blob/master/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 7b8dad4b61c..9712ecb8260 100644 --- a/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java +++ b/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java @@ -36,7 +36,7 @@ * An example of inference using an image classification model. * *

    See this doc + * href="https://github.com/deepjavalibrary/djl/blob/master/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 80c879e66bf..47538ff9595 100644 --- a/examples/src/main/java/ai/djl/examples/inference/InstanceSegmentation.java +++ b/examples/src/main/java/ai/djl/examples/inference/InstanceSegmentation.java @@ -34,7 +34,7 @@ * An example of inference using an instance segmentation model. * *

    See this doc + * href="https://github.com/deepjavalibrary/djl/blob/master/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 6f03cd24f50..47e6c79d776 100644 --- a/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java +++ b/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java @@ -35,8 +35,8 @@ * An example of inference using an object detection model. * *

    See this doc for - * information about this example. + * href="https://github.com/deepjavalibrary/djl/blob/master/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/ObjectDetectionWithTensorflowSavedModel.java b/examples/src/main/java/ai/djl/examples/inference/ObjectDetectionWithTensorflowSavedModel.java index 683e849a3fb..1bb5122a9ab 100644 --- a/examples/src/main/java/ai/djl/examples/inference/ObjectDetectionWithTensorflowSavedModel.java +++ b/examples/src/main/java/ai/djl/examples/inference/ObjectDetectionWithTensorflowSavedModel.java @@ -61,7 +61,7 @@ * href="https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md">here * *

    See this doc + * href="https://github.com/deepjavalibrary/djl/blob/master/examples/docs/object_detection_with_tensorflow_saved_model.md">doc * for information about this example. */ public final class ObjectDetectionWithTensorflowSavedModel { 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 27d9149a8aa..beaec556fb4 100644 --- a/examples/src/main/java/ai/djl/examples/inference/PoseEstimation.java +++ b/examples/src/main/java/ai/djl/examples/inference/PoseEstimation.java @@ -39,8 +39,8 @@ * An example of inference using a pose estimation model. * *

    See this doc for - * information about this example. + * href="https://github.com/deepjavalibrary/djl/blob/master/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/inference/SentimentAnalysis.java b/examples/src/main/java/ai/djl/examples/inference/SentimentAnalysis.java index b38a3c38f8e..8fe88e1cefa 100644 --- a/examples/src/main/java/ai/djl/examples/inference/SentimentAnalysis.java +++ b/examples/src/main/java/ai/djl/examples/inference/SentimentAnalysis.java @@ -33,8 +33,8 @@ * An example of inference using DistilBERT for Sentiment Analysis. * *

    See this doc for - * information about this example.* + * href="https://github.com/deepjavalibrary/djl/blob/master/examples/docs/sentiment_analysis.md">doc + * for information about this example.* */ public final class SentimentAnalysis { diff --git a/examples/src/main/java/ai/djl/examples/inference/face/LightFaceDetection.java b/examples/src/main/java/ai/djl/examples/inference/face/LightFaceDetection.java index 427f19a9348..395d6a2ea8e 100644 --- a/examples/src/main/java/ai/djl/examples/inference/face/LightFaceDetection.java +++ b/examples/src/main/java/ai/djl/examples/inference/face/LightFaceDetection.java @@ -34,8 +34,8 @@ * An example of inference using a face detection model. * *

    See this doc for - * information about this example. + * href="https://github.com/deepjavalibrary/djl/blob/master/examples/docs/face_detection.md">doc + * for information about this example. */ public final class LightFaceDetection { diff --git a/examples/src/main/java/ai/djl/examples/inference/face/RetinaFaceDetection.java b/examples/src/main/java/ai/djl/examples/inference/face/RetinaFaceDetection.java index 2f8218a0a1e..84bfdef3064 100644 --- a/examples/src/main/java/ai/djl/examples/inference/face/RetinaFaceDetection.java +++ b/examples/src/main/java/ai/djl/examples/inference/face/RetinaFaceDetection.java @@ -34,8 +34,8 @@ * An example of inference using a face detection model. * *

    See this doc for - * information about this example. + * href="https://github.com/deepjavalibrary/djl/blob/master/examples/docs/face_detection.md">doc + * for information about this example. */ public final class RetinaFaceDetection { 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 54942f6f917..40b9f816472 100644 --- a/examples/src/main/java/ai/djl/examples/training/TrainCaptcha.java +++ b/examples/src/main/java/ai/djl/examples/training/TrainCaptcha.java @@ -43,8 +43,8 @@ * An example of training a CAPTCHA solving model. * *

    See this doc for - * information about this example. + * href="https://github.com/deepjavalibrary/djl/blob/master/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 5759857c8dc..9bd11ec05a4 100644 --- a/examples/src/main/java/ai/djl/examples/training/TrainMnist.java +++ b/examples/src/main/java/ai/djl/examples/training/TrainMnist.java @@ -38,8 +38,8 @@ * An example of training an image classification (MNIST) model. * *

    See this doc for - * information about this example. + * href="https://github.com/deepjavalibrary/djl/blob/master/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 c0a23f9a8a0..d436c848ec2 100644 --- a/examples/src/main/java/ai/djl/examples/training/TrainPikachu.java +++ b/examples/src/main/java/ai/djl/examples/training/TrainPikachu.java @@ -59,8 +59,8 @@ * An example of training a simple Single Shot Detection (SSD) model. * *

    See this doc for - * information about this example. + * href="https://github.com/deepjavalibrary/djl/blob/master/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 455a445700a..cfb0de2f1df 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 @@ -62,7 +62,7 @@ * An example of training an image classification (ResNet for Cifar10) model. * *

    See this doc + * href="https://github.com/deepjavalibrary/djl/blob/master/examples/docs/train_cifar10_resnet.md">doc * for information about this example. */ public final class TrainResnetWithCifar10 { diff --git a/examples/src/main/javadoc/overview.html b/examples/src/main/javadoc/overview.html index 66bc6796d99..547e3b3893b 100644 --- a/examples/src/main/javadoc/overview.html +++ b/examples/src/main/javadoc/overview.html @@ -7,7 +7,7 @@

    The examples module contains a number of examples of training and inference using DJL. - See here for more details. + See here for more details.

    diff --git a/extensions/fasttext/README.md b/extensions/fasttext/README.md index c38baa451a6..68522d149a7 100644 --- a/extensions/fasttext/README.md +++ b/extensions/fasttext/README.md @@ -6,7 +6,7 @@ This module contains the NLP support with fastText implementation. fastText module's implementation in DJL is not considered as an Engine, it doesn't support Trainer and Predictor. The training and inference functionality is directly provided through [FtModel](https://javadoc.io/doc/ai.djl.fasttext/fasttext-engine/latest/ai/djl/fasttext/FtModel.html) -class. You can find examples [here](https://github.com/awslabs/djl/blob/master/extensions/fasttext/src/test/java/ai/djl/fasttext/CookingStackExchangeTest.java). +class. You can find examples [here](https://github.com/deepjavalibrary/djl/blob/master/extensions/fasttext/src/test/java/ai/djl/fasttext/CookingStackExchangeTest.java). Current implementation has the following limitations: diff --git a/index1.0.html b/index1.0.html index 03dfceec3a1..236c5127c82 100644 --- a/index1.0.html +++ b/index1.0.html @@ -56,13 +56,13 @@