Repository: deepjavalibrary/djl
Pull request #1638 (2022-05-09 20:53:34)

[integration] Enable gather unit test for windows
Change-Id: I2e34015b4e28b195ba1509ee3900b876250a7bfb

## Description ##

Brief description of what this PR is about

- If this change is a backward incompatible change, why must this change be made?
- Interesting edge cases to note here


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```diff
@@             Coverage Diff              @@
##             master    #1638      +/-   ##
============================================
- Coverage     72.08%   70.86%   -1.23%     
- Complexity     5126     5433     +307     
============================================
  Files           473      507      +34     
  Lines         21970    23769    +1799     
  Branches       2351     2589     +238     
============================================
+ Hits          15838    16843    +1005     
- Misses         4925     5637     +712     
- Partials       1207     1289      +82     
```


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Patch:
diff --git a/docs/development/dependency_management.md b/docs/development/dependency_management.md
index c7d05ea00..3625bc95a 100644
--- a/docs/development/dependency_management.md
+++ b/docs/development/dependency_management.md
@@ -46,9 +46,10 @@ See [How to use DJL's BOM](../../bom/README.md#how-to-use-djls-bom) for detail.
 | | [mxnet-native-mkl(osx-x86_64)](../../engines/mxnet/mxnet-engine/README.md#macos) | Contains Apache MXNet native library for macOS |
 | | [mxnet-native-mkl(win-x86_64)](../../engines/mxnet/mxnet-engine/README.md#windows-cpu) | Contains Apache MXNet native library for Windows |
 | | [mxnet-native-mkl(linux-x86_64)](../../engines/mxnet/mxnet-engine/README.md#linux-cpu) | Contains Apache MXNet native library for Linux |
-| | [mxnet-native-cu110(linux-x86_64)](../../engines/mxnet/mxnet-engine/README.md#linux-gpu) | Contains Apache MXNet native library for Linux with CUDA 11.0|
+| | [mxnet-native-cu112(linux-x86_64)](../../engines/mxnet/mxnet-engine/README.md#linux-gpu) | Contains Apache MXNet native library for Linux with CUDA 11.2|
 | | [mxnet-native-cu102(linux-x86_64)](../../engines/mxnet/mxnet-engine/README.md#linux-gpu) | Contains Apache MXNet native library for Linux with CUDA 10.2|
 | | mxnet-native-auto (deprecated) | No longer needed since DJL 0.15.0 |
+| | mxnet-native-cu110(linux-x86_64) (deprecated) | Contains Apache MXNet native library <= 1.8.1 for Linux with CUDA 11.0|
 | | mxnet-native-cu101(linux-x86_64) (deprecated) | Contains Apache MXNet native library <= 1.7.0 for Linux with CUDA 10.1 |
 | | mxnet-native-cu92(linux-x86_64) (deprecated) | Contains Apache MXNet native library <= 1.6.0 for Linux with CUDA 9.2 |
 | | | |
@@ -63,6 +64,7 @@ See [How to use DJL's BOM](../../bom/README.md#how-to-use-djls-bom) for detail.
 | | [pytorch-native-cu102(win-x86_64)](../../engines/pytorch/pytorch-engine/README.md#windows-gpu) | Contains PyTorch native library for Windows with CUDA 10.2 |
 | | [pytorch-native-cu113-precxx11(linux-x86_64)](../../engines/pytorch/pytorch-engine/README.md#for-pre-cxx11-build) | Contains PyTorch native library for Linux with CUDA 11.3 |
 | | [pytorch-native-cpu-precxx11(linux-x86_64)](../../engines/pytorch/pytorch-engine/README.md#for-pre-cxx11-build) | Contains PyTorch native library for centOS 7 and Ubuntu 14.04 |
+| | [pytorch-native-cpu-precxx11(linux-aarch64)](../../engines/pytorch/pytorch-engine/README.md#for-aarch64-build) | Contains PyTorch native library for Linux ARM |
 | | [pytorch-jni](../../engines/pytorch/pytorch-engine/README.md) | Contains PyTorch JNI native library |
 | | pytorch-native-auto (deprecated) | No longer needed since DJL 0.15.0 |
 | | pytorch-native-cu111(linux-x86_64) (deprecated) | Contains PyTorch native library for Linux with CUDA 11.1 |
@@ -89,7 +91,9 @@ See [How to use DJL's BOM](../../bom/README.md#how-to-use-djls-bom) for detail.
 | | [paddlepaddle-native-cpu(linux-x86_64)](../../engines/paddlepaddle/paddlepaddle-engine/README.md#linux) | Contains PaddlePaddle native library for Linux |
 | | [paddlepaddle-native-cpu(win-x86_64)](../../engines/paddlepaddle/paddlepaddle-engine/README.md#windows) | Contains PaddlePaddle native library for Windows |
 | | [paddlepaddle-native-cu102(linux-x86_64)](../../engines/paddlepaddle/paddlepaddle-engine/README.md#Linux-GPU) | Contains PaddlePaddle native library for Linux with CUDA 10.2 |
-| | [paddlepaddle-native-cu101(linux-x86_64)](../../engines/paddlepaddle/paddlepaddle-engine/README.md#Linux-GPU) | Contains PaddlePaddle native library for Linux with CUDA 10.1 |
+| | [paddlepaddle-native-cu112(linux-x86_64)](../../engines/paddlepaddle/paddlepaddle-engine/README.md#Linux-GPU) | Contains PaddlePaddle native library for Linux with CUDA 11.2 |
+| | [paddlepaddle-native-cu110(win-x86_64)](../../engines/paddlepaddle/paddlepaddle-engine/README.md#Windows-GPU-Experimental) | Contains PaddlePaddle native library for Windows with CUDA 11.0 |
+| | paddlepaddle-native-cu101 (deprecated) | Contains PaddlePaddle native library <= 2.0.2 for Linux with CUDA 10.1 |
 | | paddlepaddle-native-auto (deprecated) | No longer needed since DJL 0.15.0 |
 | | | |
 | [ai.djl.onnxruntime](https://search.maven.org/search?q=g:ai.djl.onnxruntime) | [onnxruntime-engine](../../engines/onnxruntime/onnxruntime-engine/README.md#installation) | ONNX Runtime engine adapter |
diff --git a/engines/paddlepaddle/paddlepaddle-engine/README.md b/engines/paddlepaddle/paddlepaddle-engine/README.md
index 00b01641a..9f1e5a758 100644
--- a/engines/paddlepaddle/paddlepaddle-engine/README.md
+++ b/engines/paddlepaddle/paddlepaddle-engine/README.md
@@ -115,7 +115,7 @@ LD_LIBRARY_PATH=$HOME/.djl.ai/paddle/2.2.2-<cuda-flavor>-linux-x86_64
 </dependency>
 ```
 
-#### Windows GPU (Experimental)
+#### Windows GPU Experimental
 
 - ai.djl.paddlepaddle:paddlepaddle-native-cu110:2.2.2:win-x86_64
 
diff --git a/engines/pytorch/pytorch-engine/README.md b/engines/pytorch/pytorch-engine/README.md
index f1fa8a325..48d163943 100644
--- a/engines/pytorch/pytorch-engine/README.md
+++ b/engines/pytorch/pytorch-engine/README.md
@@ -45,7 +45,8 @@ The following table illustrates which pytorch version that DJL supports:
 
 | PyTorch engine version | PyTorch native library version             |
 |------------------------|--------------------------------------------|
-| pytorch-engine:0.16.0  | pytorch-native-auto: 1.8.1, 1.9.1, 1.10.0  |
+| pytorch-engine:0.17.0  | 1.9.1, 1.10.0, 1.11.0                      |
+| pytorch-engine:0.16.0  | 1.8.1, 1.9.1, 1.10.0                       |
 | pytorch-engine:0.15.0  | pytorch-native-auto: 1.8.1, 1.9.1, 1.10.0  |
 | pytorch-engine:0.14.0  | pytorch-native-auto: 1.8.1, 1.9.0, 1.9.1   |
 | pytorch-engine:0.13.0  | pytorch-native-auto:1.9.0                  |
@@ -69,21 +70,21 @@ You can choose a native library based on your platform if you don't have network
 ### macOS
 For macOS, you can use the following library:
 
-- ai.djl.pytorch:pytorch-jni:1.10.0-0.16.0
-- ai.djl.pytorch:pytorch-native-cpu:1.10.0:osx-x86_64
+- ai.djl.pytorch:pytorch-jni:1.11.0-0.17.0
+- ai.djl.pytorch:pytorch-native-cpu:1.11.0:osx-x86_64
 
 ```xml
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-native-cpu</artifactId>
     <classifier>osx-x86_64</classifier>
-    <version>1.10.0</version>
+    <version>1.11.0</version>
     <scope>runtime</scope>
 </dependency>
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-native-jni</artifactId>
-    <version>1.10.0-0.16.0</version>
+    <version>1.11.0-0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```
@@ -94,22 +95,22 @@ installed on your GPU machine, you can use one of the following library:
 
 #### Linux GPU
 
-- ai.djl.pytorch:pytorch-jni:1.10.0-0.16.0
-- ai.djl.pytorch:pytorch-native-cu113:1.10.0:linux-x86_64 - CUDA 11.3
-- ai.djl.pytorch:pytorch-native-cu102:1.10.0:linux-x86_64 - CUDA 10.2
+- ai.djl.pytorch:pytorch-jni:1.11.0-0.17.0
+- ai.djl.pytorch:pytorch-native-cu113:1.11.0:linux-x86_64 - CUDA 11.3
+- ai.djl.pytorch:pytorch-native-cu102:1.11.0:linux-x86_64 - CUDA 10.2
 
 ```xml
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-native-cu113</artifactId>
     <classifier>linux-x86_64</classifier>
-    <version>1.10.0</version>
+    <version>1.11.0</version>
     <scope>runtime</scope>
 </dependency>
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-jni</artifactId>
-    <version>1.10.0-0.16.0</version>
+    <version>1.11.0-0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```
@@ -119,21 +120,21 @@ installed on your GPU machine, you can use one of the following library:
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-native-cu102</artifactId>
     <classifier>linux-x86_64</classifier>
-    <version>1.10.0</version>
+    <version>1.11.0</version>
     <scope>runtime</scope>
 </dependency>
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-jni</artifactId>
-    <version>1.10.0-0.16.0</version>
+    <version>1.11.0-0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```
 
 ### Linux CPU
 
-- ai.djl.pytorch:pytorch-jni:1.10.0-0.16.0
-- ai.djl.pytorch:pytorch-native-cpu:1.10.0:linux-x86_64
+- ai.djl.pytorch:pytorch-jni:1.11.0-0.17.0
+- ai.djl.pytorch:pytorch-native-cpu:1.11.0:linux-x86_64
 
 ```xml
 <dependency>
@@ -141,12 +142,33 @@ installed on your GPU machine, you can use one of the following library:
     <artifactId>pytorch-native-cpu</artifactId>
     <classifier>linux-x86_64</classifier>
     <scope>runtime</scope>
-    <version>1.10.0</version>
+    <version>1.11.0</version>
 </dependency>
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-jni</artifactId>
-    <version>1.10.0-0.16.0</version>
+    <version>1.11.0-0.17.0</version>
+    <scope>runtime</scope>
+</dependency>
+```
+
+### For aarch64 build
+
+- ai.djl.pytorch:pytorch-jni:1.11.0-0.17.0
+- ai.djl.pytorch:pytorch-native-cpu-precxx11:1.11.0:linux-aarch64
+
+```xml
+<dependency>
+    <groupId>ai.djl.pytorch</groupId>
+    <artifactId>pytorch-native-cpu-precxx11</artifactId>
+    <classifier>linux-aarch64</classifier>
+    <scope>runtime</scope>
+    <version>1.11.0</version>
+</dependency>
+<dependency>
+    <groupId>ai.djl.pytorch</groupId>
+    <artifactId>pytorch-jni</artifactId>
+    <version>1.11.0-0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```
@@ -156,22 +178,22 @@ installed on your GPU machine, you can use one of the following library:
 We also provide packages for the system like CentOS 7/Ubuntu 14.04 with GLIBC >= 2.17.
 All the package were built with GCC 7, we provided a newer `libstdc++.so.6.24` in the package that contains `CXXABI_1.3.9` to use the package successfully.
 
-- ai.djl.pytorch:pytorch-jni:1.10.0-0.16.0
-- ai.djl.pytorch:pytorch-native-cu113-precxx11:1.10.0:linux-x86_64 - CUDA 11.3
-- ai.djl.pytorch:pytorch-native-cpu-precxx11:1.10.0:linux-x86_64   - CPU
+- ai.djl.pytorch:pytorch-jni:1.11.0-0.17.0
+- ai.djl.pytorch:pytorch-native-cu113-precxx11:1.11.0:linux-x86_64 - CUDA 11.3
+- ai.djl.pytorch:pytorch-native-cpu-precxx11:1.11.0:linux-x86_64   - CPU
 
 ```xml
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-native-cu113-precxx11</artifactId>
     <classifier>linux-x86_64</classifier>
-    <version>1.10.0</version>
+    <version>1.11.0</version>
     <scope>runtime</scope>
 </dependency>
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-jni</artifactId>
-    <version>1.10.0-0.16.0</version>
+    <version>1.11.0-0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```
@@ -181,13 +203,13 @@ All the package were built with GCC 7, we provided a newer `libstdc++.so.6.24` i
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-native-cpu-precxx11</artifactId>
     <classifier>linux-x86_64</classifier>
-    <version>1.10.0</version>
+    <version>1.11.0</version>
     <scope>runtime</scope>
 </dependency>
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-jni</artifactId>
-    <version>1.10.0-0.16.0</version>
+    <version>1.11.0-0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```
@@ -202,22 +224,22 @@ For the Windows platform, you can choose between CPU and GPU.
 
 #### Windows GPU
 
-- ai.djl.pytorch:pytorch-jni:1.10.0-0.16.0
-- ai.djl.pytorch:pytorch-native-cu113:1.10.0:win-x86_64
-- ai.djl.pytorch:pytorch-native-cu102:1.10.0:win-x86_64
+- ai.djl.pytorch:pytorch-jni:1.11.0-0.17.0
+- ai.djl.pytorch:pytorch-native-cu113:1.11.0:win-x86_64
+- ai.djl.pytorch:pytorch-native-cu102:1.11.0:win-x86_64
 
 ```xml
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-native-cu113</artifactId>
     <classifier>win-x86_64</classifier>
-    <version>1.10.0</version>
+    <version>1.11.0</version>
     <scope>runtime</scope>
 </dependency>
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-jni</artifactId>
-    <version>1.10.0-0.16.0</version>
+    <version>1.11.0-0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```
@@ -227,21 +249,21 @@ For the Windows platform, you can choose between CPU and GPU.
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-native-cu102</artifactId>
     <classifier>win-x86_64</classifier>
-    <version>1.10.0</version>
+    <version>1.11.0</version>
     <scope>runtime</scope>
 </dependency>
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-jni</artifactId>
-    <version>1.10.0-0.16.0</version>
+    <version>1.11.0-0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```
 
 ### Windows CPU
 
-- ai.djl.pytorch:pytorch-jni:1.10.0-0.16.0
-- ai.djl.pytorch:pytorch-native-cpu:1.10.0:win-x86_64
+- ai.djl.pytorch:pytorch-jni:1.11.0-0.17.0
+- ai.djl.pytorch:pytorch-native-cpu:1.11.0:win-x86_64
 
 ```xml
 <dependency>
@@ -249,12 +271,12 @@ For the Windows platform, you can choose between CPU and GPU.
     <artifactId>pytorch-native-cpu</artifactId>
     <classifier>win-x86_64</classifier>
     <scope>runtime</scope>
-    <version>1.10.0</version>
+    <version>1.11.0</version>
 </dependency>
 <dependency>
     <groupId>ai.djl.pytorch</groupId>
     <artifactId>pytorch-jni</artifactId>
-    <version>1.10.0-0.16.0</version>
+    <version>1.11.0-0.17.0</version>
     <scope>runtime</scope>
 </dependency>
 ```


Test patch:
diff --git a/integration/src/main/java/ai/djl/integration/tests/ndarray/NDIndexTest.java b/integration/src/main/java/ai/djl/integration/tests/ndarray/NDIndexTest.java
index a954f3e0f..59e1b14ac 100644
--- a/integration/src/main/java/ai/djl/integration/tests/ndarray/NDIndexTest.java
+++ b/integration/src/main/java/ai/djl/integration/tests/ndarray/NDIndexTest.java
@@ -16,7 +16,6 @@ import ai.djl.ndarray.NDArray;
 import ai.djl.ndarray.NDManager;
 import ai.djl.ndarray.index.NDIndex;
 import ai.djl.ndarray.types.Shape;
-import ai.djl.testing.TestRequirements;
 import org.testng.Assert;
 import org.testng.annotations.Test;
 
@@ -55,10 +54,6 @@ public class NDIndexTest {
 
     @Test
     public void testGather() {
-        // Currently in windows gradle cannot find all the engines.
-        // In the dependencies, changing runtimeOnly to api however will remedy the problem.
-        // TODO: remove this when gradle problem is fixed.
-        TestRequirements.notWindows();
         try (NDManager manager = NDManager.newBaseManager()) {
             NDArray arr = manager.arange(20f).reshape(-1, 4);
             long[] idx = {0, 0, 2, 1, 1, 2};