Question Can I use Python libraries in a .NET AI app? Is this approach feasible and performant?

[riddhi]

Hi all,
I’m planning to build an AI-driven application where I’d prefer to write the main logic and structure in .NET (C#) for better control and integration with existing systems.
However, some of the functionalities I need are available in Python libraries, such as:

• azure-cognitiveservices-vision-computervision (for image analysis)
• openai (for LLMs and generative AI)
• pymupdf and reportlab (for PDF processing and generation)
• python-multipart, Pillow, and weasyprint (for file uploads, image processing, and document rendering)

My questions:​

• Is it feasible to use these Python libraries within a .NET application using interop tools like pythonnet or IronPython?
• Can this setup work well for production-level AI applications?
• Are there any performance drawbacks, integration challenges, or better alternatives for this hybrid approach?
• Would you recommend this model or suggest switching entirely to a Python or .NET stack depending on the use case?

I’d really appreciate any insights or real-world experiences from those who’ve tried combining Python and .NET in similar projects.

 

[Skydiver]

Is it feasible to use these Python libraries within a .NET application using interop tools like pythonnet or IronPython?

Yes, with IronPython if the libraries you are using are written in pure Python. If the libraries are dependent on C/C++ libraries, then no.

Yes, if those Python libraries expose any C style APIs, then you can use C#'s P/Invoke to call the C APIs.

My personal recommendation would be to keep the Python stuff in Python, and compile some adapter/wrapper module as standalone executables. Then use ShellExecute() to run those adapters/wrappers. <sarcasm>If this approach is good enough for git which shells out to run other executables, then it should be good enough for production work.</sarcasm>

 

[Justin]

Hi all,
I’m planning to build an AI-driven application where I’d prefer to write the main logic and structure in .NET (C#) for better control and integration with existing systems.
However, some of the functionalities I need are available in Python libraries, such as:

• azure-cognitiveservices-vision-computervision (for image analysis)
• openai (for LLMs and generative AI)
• pymupdf and reportlab (for PDF processing and generation)
• python-multipart, Pillow, and weasyprint (for file uploads, image processing, and document rendering)

My questions:​

• Is it feasible to use these Python libraries within a .NET application using interop tools like pythonnet or IronPython?
• Can this setup work well for production-level AI applications?
• Are there any performance drawbacks, integration challenges, or better alternatives for this hybrid approach?
• Would you recommend this model or suggest switching entirely to a Python or .NET stack depending on the use case?

I’d really appreciate any insights or real-world experiences from those who’ve tried combining Python and .NET in similar projects.

Yes, you absolutely can use Python libraries in a .NET AI application. This is a common scenario, especially given the vast and mature ecosystem of AI and machine learning libraries available in Python (like TensorFlow, PyTorch, scikit-learn, spaCy, NLTK, etc.).

However, the feasibility and performance depend heavily on how you integrate the Python code and libraries. There are several common approaches, each with its own trade-offs:

1. Embedding CPython using pythonnet (aka clr):
   • How it works:This library allows you to import and interact with a standard CPython interpreter directly within your .NET process. You can import Python modules, call Python functions, and exchange data between .NET and Python.
     
     
   • Feasibility: Highly feasible for many scenarios. You can directly leverage existing Python code and libraries.
   • Performance:This is where it gets tricky.
     • Overhead: There's overhead in marshaling data and calls between the .NET CLR and the Python interpreter.
     • GIL (Global Interpreter Lock): CPython's GIL can limit parallelism when calling Python code from multiple .NET threads simultaneously.
       
       
     • Data Transfer: Transferring large amounts of data between .NET and Python memory spaces can be slow.
     • Numeric Libraries: For libraries like NumPy, SciPy, TensorFlow, and PyTorch (which rely heavily on underlying C/C++/CUDA code), the actual computation is fast once it's running in Python. The performance bottleneck comes from getting data into and out of the Python environment via pythonnet.
     • Best Use Case: Good for orchestrating complex Python workflows or using specific Python libraries where the bulk of the work happens within Python and the interactions from .NET are coarse-grained (e.g., calling a single function that does a lot of processing). Can be less performant if you need to make many small, frequent calls or transfer lots of data back and forth.
2. Inter-Process Communication (IPC):
   • How it works: Your .NET application runs as one process, and your Python code runs in a separate process. They communicate using mechanisms like REST APIs, gRPC, message queues (like RabbitMQ, Kafka), or even standard input/output.
   • Feasibility: Very feasible. This is a robust and common pattern for microservices or integrating systems written in different languages.
   • Performance:
     • Overhead: Involves serialization/deserialization of data and network/IPC latency.
     • Scalability: Can be very performant if designed well (e.g., using gRPC for structured data, optimizing data payloads). It allows you to scale the .NET and Python parts independently.
     • Latency: Introduces communication latency compared to in-process execution.
     • Best Use Case: Ideal for decoupling, building scalable services, or when the Python process needs to be managed separately (e.g., requires specific environment setups, uses GPUs). Performance is good for service-oriented interactions.
3. Model Export and .NET Inference Libraries:
   • How it works: You train your AI model using Python libraries (TensorFlow, PyTorch, scikit-learn). Then, you export the trained model into a standard format like ONNX (Open Neural Network Exchange). Your .NET application then uses a .NET library (like Microsoft.ML.OnnxRuntime) to load and run the ONNX model directly within the .NET process for inference (making predictions).
     
     
     
   • Feasibility: Highly feasible and often recommended for deployment.
   • Performance: Generally the most performant approach for inference.
     • No Python dependency or overhead at runtime.
     • ONNX Runtime is a high-performance engine optimized for various hardware (CPUs, GPUs).
       
       
     • Data stays within the .NET process during inference.
   • Limitation: This approach is primarily for using a pre-trained model (inference), not for training the model dynamically from .NET.
   • Best Use Case: When you need to deploy an AI model and perform predictions from your .NET application with maximum performance and minimum dependencies.
4. Using .NET Native AI Libraries:
   • How it works: Microsoft provides ML.NET, an open-source, cross-platform machine learning framework for .NET. There are also .NET bindings available for popular frameworks like TensorFlow (TensorFlow.NET) and PyTorch (TorchSharp), allowing you to build and run models entirely in C#.
     
     
     
   • Feasibility: Very feasible and provides a pure .NET solution.
   • Performance: Can be highly performant as everything runs natively within the .NET ecosystem. Performance is often comparable to or better than Python libraries for similar tasks, especially tasks covered well by ML.NET.
   • Limitation: Requires rewriting your AI/ML code in C# or using the .NET bindings, which might have a different API feel than the Python originals (though TensorFlow.NET and TorchSharp aim to mirror the Python APIs). The ecosystem of pre-built models and cutting-edge research implementations might be smaller than in Python.
   • Best Use Case: When you are starting a new .NET AI project, want to avoid Python dependencies entirely, or have development expertise primarily in .NET.

Summary:

• Feasibility: Yes, using Python libraries is feasible through several methods (pythonnet, IPC, ONNX export).
• Performance:Varies significantly by method.
  • pythonnet adds overhead, performance depends on interaction patterns.
  • IPC adds latency, performance depends on communication design and data transfer.
  • ONNX export + .NET runtime is generally the most performant for inference.
  • .NET native libraries (ML.NET, TensorFlow.NET, TorchSharp) offer high native performance.

The "best" approach for your .NET AI app depends on your specific needs: Are you leveraging existing Python code/models? Do you need to train or only infer? What are your performance bottlenecks? What is your team's expertise?

For many production scenarios where you've trained a model in Python and want to use it in a .NET application, the ONNX export approach is often the most performant and easiest to deploy without runtime Python dependencies. If you need dynamic training or deep interaction with Python libraries, pythonnet or IPC would be necessary. If so contact me rythorian77@gmail.com.

 

[Skydiver]

@Justin: Please put your code in code tags. Or at least tell the AI that you are using to use code tags.