.. design philosophy
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Design Philosophy
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Overview
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| Matx is originally designed to integrate training and inference in machine learning production system. It comes with the following goals:

* Python interpreter is not required during deployment. 
* Existing Python code that performs data preprocessing and postprocessing need not to be rewritten. 
* The generated code should run faster than the original Python version. 

| Given them, Matx is an ahead-of-time compiler that transforms Python into C++. Besides machine learning applications, we expect Matx to be used in a wider range of applications that involve translating Python into C++ for better performance.

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High-level Concepts
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Script
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Script is used to compile a Python function/class into a C++ function/class with Python wrapper. The Python wrapper has identical behavior as the original Python function/class. 

Operation (Op)
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An operation (Op) is a compiled C++ function or a method of the compiled C++ class. It is the basic component used to construct a computational graph via tracing.

Trace
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Trace is used to generate a computational graph composed of operations. Note that the computational graph must be a directly asyclic graph. The computational graph can be directly executed in Python. It can also be saved/loaded to/from disk files.

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Python First
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Matx is an ahead-of-time compiler that compiles Python into C++. We choose Python as it is the dominate programming language in deep learning and has a large existing codebase. Note that we only support a subset of Python due to performance consideration.

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Fast Execution
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As compiled code from Matx  is written in C++, it naturally achieves orders of speedup compared with the original Python code executed by Python Interpreter. To further improve the performance, we enforce type annotation when writing Python code.

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Extensions Without Pain
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Writing custom C++ Operations can be easily integrated into Matx.

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Machine Learning Support
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Preprocessing and Postprocessing
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Data preprocessing and postprocessing are two key components in machine learning production systems. In NLP, data preprocessing includes text cleaning, text augmentation and tokenizing. A large portion of existing codebase is written in Python. With Matx, Developers can integrate data preprocessing code into neural network inference code easily.

Pytorch/TensorFlow Integration
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Matx enables easy integration of existing Pytorch/TensorFlow models into an end-to-end pipelines.