[GitHub] [incubator-mxnet] saudet commented on issue #17783: [RFC] MXNet 2.0 JVM Language development
saudet commented on issue #17783: URL: https://github.com/apache/incubator-mxnet/issues/17783#issuecomment-667041968 > We are looking for a robust solution for MXNet Java developers to use especially owned and maintained by the Apache MXNet's community. I will be more than happy to see if you would like to contribute the source code that generate MXNet JavaCpp package to this repo. So we can own the maintainance and responsible for the end users that the package is reliable. > > At the beginning, we were discussing several ways that we can try to preserve a low level Java API for MXNet that anyone who use Java can start with. Most of the problems were lying under the ownership and maintainance part. I have placed JavaCpp option to option 5 so we can see which one works the best in the end. Sounds good, thanks! If you have any specific concerns about the above, please let me know. JNA seems to be maintained by a single person with apparently no connections to the AI industry (https://dzone.com/articles/scratch-netbeans-itch-matthias) whereas I have to maintain anyway as part of my work APIs mainly for OpenCV, FFmpeg, ONNX Runtime, and TensorFlow at the moment, but others as well and it tends to vary with time, MXNet could become part of those eventually, and I have users paying for commercial support of proprietary libraries too, so I think JavaCPP is the better option here, but I'm obviously biased. :) This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-mxnet] saudet commented on issue #17783: [RFC] MXNet 2.0 JVM Language development
saudet commented on issue #17783: URL: https://github.com/apache/incubator-mxnet/issues/17783#issuecomment-663921595 > ## What's missing > > javacpp-presets-mxnet doesn't expose APIs form nnvm/c_api.h (some of current python/gluon API depends on APIs in nnvm/c_api.h) I've added that the other day, thanks to @frankfliu for pointing this out: https://github.com/bytedeco/javacpp-presets/commit/976e6f7d307b3f3855f39413c494d8f482c9adf6 > See javadoc: http://bytedeco.org/javacpp-presets/mxnet/apidocs/ > > 1. Java class name is “mxnet”, which is not following java naming conventions That's not hardcoded. We can use whatever name we want for that class. > 2. Each pointer has a corresponding java class, which is arguable. It's necessary to expose them as strong type class if they meant to be used directly by end developer. But they really should only be internal implementation of the API. It's overkill to expose them as a Type instead of just a pointer. We can map everything to `Pointer`, that's not a problem either. > 3. All the classes (except mxnet.java) are hand written. No, they are not. Everything in the `src/gen` directory here is generated at build time: https://github.com/bytedeco/javacpp-presets/tree/master/mxnet/src/gen/java/org/bytedeco/mxnet > 4. API mapping are hand coded as well. If you're talking about this file, yes, that's the only thing that is written manually: https://github.com/bytedeco/javacpp-presets/blob/master/mxnet/src/main/java/org/bytedeco/mxnet/presets/mxnet.java (The formatting is a bit crappy, I haven't touched it in a while, but we can make it look prettier like this: https://github.com/bytedeco/javacpp-presets/blob/master/onnxruntime/src/main/java/org/bytedeco/onnxruntime/presets/onnxruntime.java ) > ## Performance > > JavaCPP native library load takes a long time, it takes average _2.6 seconds_ to initialize libmxnet.so with javacpp. > > Loader.load(org.bytedeco.mxnet.global.mxnet.class); Something's wrong, that takes less than 500 ms on my laptop, and that includes loading OpenBLAS, OpenCV, and a lookup for CUDA and MKL, which can obviously be optimized... In any case, we can debug that later to see what is going wrong on your end. > ## Issues > > The open source code on github doesn't match the binary release on maven central: > > * the maven group and the java package name are different. Both the group ID and the package names are `org.bytedeco`, but in any case, if that gets maintained somewhere here, I imagine it would be changed to something like `org.apache.mxnet.xyz.internal.etc` > * c predict API is not included in maven version Yes it is: http://bytedeco.org/javacpp-presets/mxnet/apidocs/org/bytedeco/mxnet/global/mxnet.html > * Example code doesn't work with maven artifacts, it can only build with snapshot version locally. https://github.com/bytedeco/javacpp-presets/tree/master/mxnet/samples works fine for me on Linux: ``` $ mvn -U clean compile exec:java -Djavacpp.platform.custom -Djavacpp.platform.host -Dexec.args=apple.jpg ... Downloading from sonatype-nexus-snapshots: https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/mxnet-platform/1.7.0.rc1-1.5.4-SNAPSHOT/maven-metadata.xml Downloaded from sonatype-nexus-snapshots: https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/mxnet-platform/1.7.0.rc1-1.5.4-SNAPSHOT/maven-metadata.xml (1.3 kB at 2.5 kB/s) Downloading from sonatype-nexus-snapshots: https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/mxnet-platform/1.7.0.rc1-1.5.4-SNAPSHOT/mxnet-platform-1.7.0.rc1-1.5.4-20200725.115300-20.pom Downloaded from sonatype-nexus-snapshots: https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/mxnet-platform/1.7.0.rc1-1.5.4-SNAPSHOT/mxnet-platform-1.7.0.rc1-1.5.4-20200725.115300-20.pom (4.7 kB at 9.3 kB/s) Downloading from sonatype-nexus-snapshots: https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/javacpp-presets/1.5.4-SNAPSHOT/maven-metadata.xml Downloaded from sonatype-nexus-snapshots: https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/javacpp-presets/1.5.4-SNAPSHOT/maven-metadata.xml (610 B at 1.5 kB/s) Downloading from sonatype-nexus-snapshots: https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/javacpp-presets/1.5.4-SNAPSHOT/javacpp-presets-1.5.4-20200725.155410-6590.pom Downloaded from sonatype-nexus-snapshots: https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/javacpp-presets/1.5.4-SNAPSHOT/javacpp-presets-1.5.4-20200725.155410-6590.pom (84 kB at 91 kB/s) Downloading from sonatype-nexus-snapshots: https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/opencv-platform/4.4.0-1.5.4-SNAPSHOT/maven-metadata.xml Downloaded from
[GitHub] [incubator-mxnet] saudet commented on issue #17783: [RFC] MXNet 2.0 JVM Language development
saudet commented on issue #17783: URL: https://github.com/apache/incubator-mxnet/issues/17783#issuecomment-663916338 > @saudet Thanks for your proposal. I have four questions would like to ask you: > > 1. If we adopt JavaCpp package, how will that be consumed? Under byteco or apache MXNet? Essentially from our previous discussion, we really don't want another 3rdparty checkin. We can go either way, but I found that projects like MXNet or TensorFlow that need to develop high-level APIs on top of something like JavaCPP prefer to have control over everything in their own repositories, and use JavaCPP pretty much like we would use pybind and pip for Python. I started the JavaCPP Presets because for projects such as OpenCV, FFmpeg, LLVM, etc, high-level APIs for other languages than C/C++ are not being developed as part of those projects. I also realized the Java community needed something like Anaconda... > 2. Can you also do a benchmark on the MXNet's API's performance and possibly share the reproducible code? We did test the performance on JavaCpp vs JNA vs JNI and didn't see much difference on performance (under 10%). > > > * MXImperativeInvokeEx > > * CachedOpForward > > > The above two methods are most frequently used methods in order to do minimum inference request, please try on these two to see how performance goes. > If you're doing only batch operations, as would be the case for Python bindings, you're not going to see much difference, no. What you need to look at are things like the Indexer package, which allows us to implement fast custom operations in Java like this: http://bytedeco.org/news/2014/12/23/third-release/ You're not going to be able to do that with JNA or JNI without essentially recoding that kind of thing. > 3. We do have some additional technical issue with JavaCpp, is there any plan to fix it? (I will put it into a separate comment since it is really big. > > 4. How do you ensure the performance if the build flag is different? Like the mxnet has to build from source (with necessary modification on source code) in order to work along with javacpp > > 5. regarding to the dependencies issue, can we go without additional opencv and openblas in the package? Yes, that's the kind of issues that would be best dealt with by using only JavaCPP as a low-level tool, instead of the presets, which is basically a high-level distribution like Anaconda. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
[GitHub] [incubator-mxnet] saudet commented on issue #17783: [RFC] MXNet 2.0 JVM Language development
saudet commented on issue #17783: URL: https://github.com/apache/incubator-mxnet/issues/17783#issuecomment-662994965 Hi, instead of JNA, I would be happy to provide bindings for the C API and maintain packages based on the JavaCPP Presets here: https://github.com/bytedeco/javacpp-presets/tree/master/mxnet JavaCPP adds no overhead, unlike JNA, and is often faster than manually written JNI. Plus JavaCPP provides more tools than JNA to automate the process of parsing header files as well as packaging native libraries in JAR files. I have been maintaining modules for TensorFlow based on JavaCPP, and we actually got a boost in performance when compared to the original JNI code: https://github.com/tensorflow/java/pull/18#issuecomment-579600568 I would be able to do the same for MXNet and maintain the result in a repository of your choice. Let me know if this sounds interesting! BTW, the developers of DJL also seem opened to switch from JNA to JavaCPP even though it is not a huge priority. Still, standardizing how native bindings are created and loaded with other libraries for which JavaCPP is pretty much already the standard (such as OpenCV, TensorFlow, CUDA, FFmpeg, LLVM, Tesseract) could go a long way in alleviating concerns of stability. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
