TinyFlow: Build Your Own DL System in 2K Lines

TinyFlow is "example code" for NNVM.

It demonstrates how can we build a clean, minimum and powerful computational graph based deep learning system with same API as TensorFlow. The operator code are implemented with Torch7 to reduce the effort to write operators while still demonstrating the concepts of the system (and Embedding Lua in C++ is kinda of fun:).

TinyFlow is a real deep learning system that can run on GPU and CPUs. To support the examples, it takes.

927 lines code for operators
734 lines of code for execution runtime
71 lines of code for API glue
233 lines of code for front-end

Note that more code in operators can easily be added to make it as feature complete as most existing deep learning systems.

What is it for

As explained in the goal of NNVM, it is important to make modular and reusable components for to enable us to build customized learning system easily.

Course Material for teaching DL system. TinyFlow can be used to teach student the concepts in deep learning systems.
- e.g. design homeworks on implementing symbolic differentiation, memory allocation, operator fusion.
Experiment bed for learning system researchers. TinyFlow allows easy addition with new system features with the modular design being portable to other system that reuses NNVM.
Showcase of intermediate representation usecase. It demonstrates how intermediate representation like NNVM to be able to target multiple front-ends(TF, MXNet) and backends(Torch7, MXNet) with common set of optimizations.
Test bed on common reusable modules for DL system. TinyFlow, together with other systems(e.g. MXNet) can be used as testbed on the common reusable modules in deep learning to encourage front-end, optimization module and backends that are shared across frameworks.
Just for fun :)

We believe the Unix Philosophy can building learning system more fun and everyone can be able to build and understand learning system better.

Deep Learning System Course

If you are interested in learning how to build deep learning system from scratch, checkout CSE 599G1: Deep Learning System from University of Washington.

The Design

The graph construction API is automatically reused from NNVM
We choose Torch7 as the default operator execution backend.
- So TinyFlow can also be called "TorchFlow" since it is literally TensorFlow on top of Torch:)
- This allows us to quickly implement the operators and focus code on the system part.
We intentionally choose to avoid using MXNet as front or backend, since MXNet already uses NNVM as intermediate layer, and it would be more fun to try something different.

Although it is minimum. TinyFlow still comes with many advanced design concepts in Deep Learning system.

Automatic differentiation.
Shape/type inference.
Static memory allocation for graph for memory efficient training/inference.

The operator implementation is easy Thanks to Torch7. More fun demonstrations will be added to the project.

Dependencies

Most of TinyFlow's code is self-contained.

TinyFlow depend on Torch7 for operator supports with minimum code.
- We use a lightweight lua bridge code from dmlc-core/dmlc/lua.h
NNVM is used for graph representation and optimizations

Build

Install Torch7
- For OSX User, please install Torch with Lua 5.1 instead of LuaJIT, i.e. TORCH_LUA_VERSION=LUA51 ./install.sh
Set up environment variable TORCH_HOME to root of torch
Type make
Setup python path to include tinyflow and nnvm

export PYTHONPATH=${PYTHONPATH}:/path/to/tinyflow/python:/path/to/tinyflow/nnvm/python

Try example program python example/mnist_softmax.py

Enable Fusion in TinyFlow

Build NNVM with Fusion: uncomment fusion plugin part in config.mk, then make
Build TinyFlow: enable USE_FUSION in Makefile, then make
Try Example program example/mnist_lenet.py, change the config of session from tf.Session(config='gpu') to tf.Session(config='gpu fusion')