桌面/移动Java/C++应用中构建使用SyntaxNet的技术咨询

Let's break down your questions one by one, based on my hands-on experience working with SyntaxNet and TensorFlow:

Can I build and use SyntaxNet in Java/C++ apps on desktop and mobile platforms? #

Absolutely, though it’s not as out-of-the-box as using it in Python. Here are practical approaches for different platforms:

• Desktop Java/C++:
  • Wrap SyntaxNet as a lightweight gRPC or REST service (using Python to handle the core model logic) and call it from your Java/C++ application. This avoids the complexity of porting SyntaxNet directly to those languages.
  • Export the trained SyntaxNet model as a TensorFlow SavedModel, then use the TensorFlow Java/C++ API to load and execute the model directly. You’ll need to manually handle input preprocessing (like tokenization) and output parsing, since SyntaxNet’s helper utilities are Python-focused.
• Mobile platforms:
  • Convert the SavedModel to a TensorFlow Lite (TFLite) model. Once converted, you can use the TensorFlow Lite Java API (for Android) or C++ API (for both Android and iOS) to run the model on mobile devices. Just like desktop, you’ll need to implement tokenization and result parsing in your mobile code.

Troubleshooting source build failures on Ubuntu 17.10 #

Ubuntu 17.10 is an end-of-life release, and SyntaxNet (especially older versions) has strict dependencies on specific versions of TensorFlow, Bazel, and system libraries. Here’s how to work through this:

• Switch to a supported Ubuntu version (16.04 or 18.04 are recommended in SyntaxNet’s official docs) to avoid most dependency mismatches.
• If you must stick with 17.10:
  • Install the exact Bazel version required by the SyntaxNet commit you’re building (check the project’s README or WORKSPACE file for this detail).
  • Pin TensorFlow to a version compatible with both your Ubuntu release and SyntaxNet—older SyntaxNet versions typically require TensorFlow 1.x.
  • Ensure all system dependencies are installed: sudo apt-get install build-essential python-pip python-dev git protobuf-compiler libprotobuf-dev python-numpy python-six
  • Try building from the official SyntaxNet Docker image as a base environment—this eliminates most system-level compatibility headaches.

Can I use SyntaxNet models on mobile with just TensorFlow, or do I need extra SyntaxNet components? #

You don’t need any exclusive SyntaxNet components. SyntaxNet’s core functionality is entirely implemented as TensorFlow computation graphs. Here’s the standard workflow:

1. Export your trained SyntaxNet model as a TensorFlow SavedModel using the Python API.
2. Convert the SavedModel to a TensorFlow Lite model using the tflite_convert tool or TensorFlow Lite Converter API.
3. Load and run the TFLite model using the TensorFlow Lite Java/C++ API on your mobile device.

The only catch is that you’ll need to replicate SyntaxNet’s preprocessing (tokenization, sentence splitting) and postprocessing (turning model output into parse trees) in your mobile code—these helper functions live in SyntaxNet’s Python codebase, not the TensorFlow graph itself.

内容的提问来源于stack exchange，提问作者Lakedaemon