I am currently building a console package with Dart, and Rust using FFI(foreign function interface). Since I can't/shouldn't publish dll/so/dylib files, how can I add a build functionality to it. With that functionality, the required files should be built after the package is downloaded, so that the interop-ed code can work properly. How can it be done? Will the user that downloaded the package need to have Rust in his machine to build the files?
You need to either distribute binaries or require the user to have a Rust compiler.
According to dart-lang/sdk:
Starting in 1.21, the Dart VM also supports application snapshots, which include all the parsed classes and compiled code generated during a training run of a program.
$ dart --snapshot=hello.dart.snapshot --snapshot-kind=app-jit hello.dart arguments-for-training
Hello, world!
$ dart hello.dart.snapshot arguments-for-use
Hello, world!
Now,how can i decompile this hello.dart.snapshot file to hello.dart?
In android Apk that written by java language we can decompile apk and get jar file from class.dex using dex2jar tools, but when application developed by flutter framework(written with dart)how can decompile this application and get application dart classes?
This image show snapshot files that generated in apk assets file.
In release mode, Flutter compiles the Dart code to machine code, currently only ARMv7 (this procedure is called AOT - Ahead-Of-Time compilation). Unlike native Android apps, in which the Java is compiled to byte-code named Smali, which can be (pretty easily) decompiled to Java back again.
Most of the machine code is compiled to the file "isolate_snapshot_instr", which is written in a special format, and the flutter engine (flutterlib.so, also found inside the app), loads it into the app memory in run time. Therefore, you have 2 reasonable options:
Reading the app code at runtime (the .text segment). You can use
frida dump for that, and extract the compiled Dart code that
you need
Pacthing/Using the Flutter engine in order to deserialize the machine code
If you have ipa (IOS app), that could be easier, because all of the code is found in App.Framework.
I was studying basics of Flutter and Dart development and came across package manager called pub. What is it and what is it's importance in Flutter development?
Pub is the package manager for the Dart programming language, containing reusable libraries & packages for Flutter, AngularDart, and general Dart programs.
Some basic command:
Use pub get to get dependencies
Use pub upgrade to upgrade a dependency
Use pub publish to make your library available for others
You can find all packages here.
Also you can develop your own package, find the details on official site.
Pub is a package manager for dart to know more about pub see this: https://www.dartlang.org/tools/pub
I'm interested in incorporating TensorFlow into a C++ server application built in Visual Studio on Windows 10 and I need to know if that's possible.
Google recently announced Windows support for TensorFlow: https://developers.googleblog.com/2016/11/tensorflow-0-12-adds-support-for-windows.html
but from what I can tell this is just a pip install for the more commonly used Python package, and to use the C++ API you need to build the repo from source yourself: How to build and use Google TensorFlow C++ api
I tried building the project myself using bazel, but ran into issues trying to configure the build.
Is there a way to get TensorFlow C++ to work in native Windows (not using Docker or the new Windows 10 Linux subsystem, as I've seen others post about)?
Thanks,
Ian
It is certainly possible to use TensorFlow's C++ API on Windows, but it is not currently very easy. Right now, the easiest way to build against the C++ API on Windows would be to build with CMake, and adapt the CMake rules for the tf_tutorials_example_trainer project (see the source code here). Building with CMake will give you a Visual Studio project in which you can implement your C++ TensorFlow program.
Note that the tf_tutorials_example_trainer project builds a Console Application that statically links all of the TensorFlow runtime into your program. At present we have not written the necessary rules to create a reusable TensorFlow DLL, although this would be technially possible: for example, the Python extension is a DLL that includes the runtime, but does not export the necessary symbols to use TensorFlow's C or C++ APIs directly.
There is a detailed guide by Joe Antognini and a similar TensorFlow ReadMe at GitHub explaining the building of TensorFlow source via CMake. You also need to have SWIG installed on your machine which allows connecting C/C++ source with the Python scripting language. I did use Visual CMAKE (cmake-gui) with the screen capture shown below.
In the CMake configuration, I used Visual Studio 15 2017 compiler. Once this stage successfully completes, you can click on the Generate button to go ahead with the actual build process.
However, on Visual Studio 2015, when I attempted building via the "ALL_BUILD" project, the setup gave me "build tools for v141 cannot be found" error. This did not go away even when I attempted to retarget my solution. Finally, the solution got built successfully with Visual Studio 2017. You also need to manually set the SWIG_EXECUTABLE path in CMake before it successfully configures.
As indicated in the Antognini link, for me the build took about half an hour on a 16GB RAM, Core i7 machine. Once done, you might want to validate your build by attempting to run the tf_tutorials_example_trainer.exe file.
Hope this helps!
For our latest work on building TensorFlow C++ API on Windows, please look at this github page. This works on Windows 10, currently without CUDA support (only CPU).
PS:
Only the bazel build method works, because CMake is not supported and not maintained anymore, resulting in CMake configuration errors.
I had to use a downgraded version of my Visual Studio 2017 (from 15.7.5 to 15.4) by adding "VC++ 2017 version 15.4 v14.11 toolset" through the installer (Individual Components tab).
The cmake command which worked for me was:
cmake .. -A x64 -DCMAKE_BUILD_TYPE=Release ^
-T "v141,version=14.11" ^
-DSWIG_EXECUTABLE="C:/Program Files/swigwin-3.0.12/swig.exe" ^
-DPYTHON_EXECUTABLE="C:/Program Files/Python/python.exe" ^
-DPYTHON_LIBRARIES="C:/Program Files/Python/libs/python27.lib" ^
-Dtensorflow_ENABLE_GPU=ON ^
-DCUDNN_HOME="C:/Program Files/cudnn-9.2-windows10-x64-v7.1/cuda" ^
-DCUDA_TOOLKIT_ROOT_DIR="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.0"
After the build, open tensorflow.sln in Visual Studio and build ALL_BUILD.
If you want to enable GPU computation, do check your Graphics Card here (Compute Capability > 3.5). Do remember to install all the packages (Cuda Toolkit 9.0, cuDNN, Python 3.7, SWIG, Git, CMake...) and add the paths to the environment variable in the beginning.
I made a README detailing how to I built the Tensorflow dll and .lib file for the C++ API on Windows with GPU support building from source with Bazel. Tensorflow version 1.14
The tutorial is step by step and starts at the very beginning, so you may have to scroll down past steps you have already done, like checking your hardware, installing Bazel etc.
Here is the url: https://github.com/sitting-duck/stuff/tree/master/ai/tensorflow/build_tensorflow_1.14_source_for_Windows
Probably you will want to scroll all the way down to this part:
https://github.com/sitting-duck/stuff/tree/master/ai/tensorflow/build_tensorflow_1.14_source_for_Windows#step-7-build-the-dll
It shows how to pass command to create .lib and .dll.
Then to test your .lib you should link it into your c++ project,
Then it will show you how to identify and fix the missing symbols using the TF_EXPORT macro
I am actively working on making this tutorial better so feel free to leave comments on this answer if you are having problems.
Is Dart library exactly the same Java package ?
Is Dart package exactly the same Java library (JAR) ?
A package is a set of libraries which can for example be deployed to pub.dartlang.org. I guess this is similar to a jar file.
A library is one Dart script file with or without a name (or a set of Dart script files with part/part of) and is the boundary for privacy. Private members are only visible or accessible from within the same library.