NNVM: Build deep learning system by parts

NNVM is not a deep learning library. It is a modular, decentralized and lightweight part to help build deep learning libraries.

What is it

While most deep learning systems offer end to end solutions, it is interesting to ask if we can actually assemble a deep learning system by parts. The goal is to enable hackers can customize optimizations, target platforms and set of operators they care about. We believe that the decentralized modular system is an interesting direction.

The hope is that effective parts can be assembled together just like you assemble your own desktops. So the customized deep learning solution can be minimax, minimum in terms of dependencies, while maxiziming the users' need.

NNVM offers one such part, it provides a generic way to do computation graph optimization such as memory reduction, device allocation and more while being agnostic to the operator interface defintion and how operators are executed. NNVM is inspired by LLVM, aiming to be an intermediate representation library for neural nets and computation graphs generation and optimizations.

Why build deep learning system by parts

• Essential parts can be assembled in minimum way for embedding systems.
• Hackers can hack the parts they need and compose with other well defined parts.
• Decentralized modules enable new extensions creators to own their project without creating a monothilic version.

Deep learning system by parts

This is one way to divide the deep learning system into common parts. Each can be isolated to a modular part.

• Computation graph definition, manipulation.
• Computation graph intermediate optimization.
• Computation graph execution.
• Operator kernel libraries.
• Imperative task scheduling and parallel task coordination.

We hope that there will be more modular parts in the future, so system building can be fun and rewarding.

Links

MXNet will be using NNVM as its intermediate representation layer for symbolic graphs.