Tinyflow is a simple deep learning framework for learning purposes. It supports automatic differentiation and GPU acceleration. TinyFlow currently provides all the operators needed to build a multilayer perceptron models (MLP).

If you want to learn more about the principles behind Tinyflow, the following two blog posts may provide a lot of intuition.

Install

Tinyflow currently only supports running in 64-bit linux environment. Requirement:

gcc >= 4.8;
cmake >= 3.13 (if you choose to use cmake);
CUDA 9.0
python 3

Download the source code.

git clone https://github.com/LB-Yu/tinyflow.git

Generally speaking, CUDA will be installed in /use/local/cuda. If your installation path is different, please modify the CUDA_DIR variable on the first line of the Makefile to your installation path, or modify the CUDA_DIR variable on the fourth line of CMakeLists.txt to your installation path.

For compiling with Makefile.

cd tinyflow
make

For compiling with CMake.

cd tinyflow
mkdir build
cmake ..
make
make install

Run the MNIST Example

After compiling the GPU library, we can train an MLP on the MNIST dataset.

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

# see cmd options with 
# python tests/mnist_dlsys.py -h

# run logistic regression on numpy
python tests/mnist_dlsys.py -l -m logreg -c numpy
# run logistic regression on gpu
python tests/mnist_dlsys.py -l -m logreg -c gpu
# run MLP on numpy
python tests/mnist_dlsys.py -l -m mlp -c numpy
# run MLP on gpu
python tests/mnist_dlsys.py -l -m mlp -c gpu

Overview of Module

python/dlsys/autodiff.py: Implements computation graph, autodiff, GPU/Numpy Executor.
python/dlsys/gpu_op.py: Exposes Python function to call GPU kernels via ctypes.
python/dlsys/ndarray.py: Exposes Python GPU array API.
src/dlarray.h: header for GPU array.
src/c_runtime_api.h: C API header for GPU array and GPU kernels.
src/gpu_op.cu: cuda implementation of kernels