Bring Deep Learning to small devices
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README.md


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Blueoil provides two features.

  • Training a neural network model
  • Converting a trained model to an executable binary (or library), which utilize FPGAs for acceleration.

Documentation

You can see online documentation with enter.

Check out the Installation and Usage Guide page for getting started.

Note: Currently, Installation page is just in to be written, Please see Setup section to build docker on your development environment.

Prerequisites

  • GNU/Linux x86_64 with kernel version > 3.10
  • NVIDIA GPU with Architecture > Fermi (2.1)
  • NVIDIA drivers >= 367.48
  • Docker >=1.12 (or >=17.03.0)
  • nvidia-docker >= 2.0

The blueoil is run on docker container with original docker image based on NVIDIA's CUDA images (cuda:8.0-cudnn6-devel).

The machine running the CUDA container only requires the NVIDIA driver, the CUDA toolkit doesn't have to be installed.

Please see the detail in the nvidia-docker's prerequisites.

Set up

There are some submodules in this repositry, so you should run git submodule update --init --recursive after cloning or git clone --recursive [this repository].

./docker_build.sh

Note: The private repository submodules are set to connect by ssh, if you want to use HTTPS, you should edit URLs in .gitmodules and run git submodule sync before git submodule update --init --recursive command. (see how to edit)

How to make document

pip install -r doc.requirements.txt
cd docs
make html

You can see generated documents in HTML format under docs/_build/html/ directory on your enviroment.

Also, you can see the deploy-preview online documentation from a Pull Request page that are integrated by netilify.

How to test

We can test each opereations of drore_run.sh by using shell script.

Prerequisites

  • expect >= version 5.45
$ ./blueoil_test.sh

Usage: ./blueoil_test.sh <YML_CONFIG_FILE(optional)>

Arguments:
  YML_CONFIG_FILE       config file path for this test (optional)