Dolphin

General python package for CUDA accelerated deep learning inference.

• Documentation : ReadTheDoc
• Source code : https://github.com/MaximeDebarbat/Dolphin
• Bug reports : https://github.com/MaximeDebarbat/Dolphin/issues
• Getting Starterd :

It provides :

• A set of common image processing functions
• A TensorRT wrapper for easy inference
• Speeds up the inference with CUDA and TensorRT
• An easy to use API with Numpy
• A fast N-Dimensional array

Testing :

In order to test the package, you will need the library pytest which you can run from the root of the project :

pytest

Install

pip install dolphin-python

Build

Dolphin can be installed with Pypi (coming soon) or built with Docker which is the recommended way to use it :

docker build  -f Dockerfile \
              --rm \
              -t dolphin:latest \
              .

Docker run

Ensure that you have the nvidia-docker package installed and run the following command :

docker run \
        -it \
        --rm \
        --gpus all \
        -v "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )":"/app" \
        dolphin:latest \
        bash

Please note that Dolphin might not work without the --gpus all flag or --runtime nvidia.

Acknowledgements

This project could not have been possible without PyCuda:

Andreas Klöckner, Nicolas Pinto, Yunsup Lee, Bryan Catanzaro, Paul Ivanov, Ahmed Fasih, PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation, > Parallel Computing, Volume 38, Issue 3, March 2012, Pages 157-174.

TODOs

• Improve Engine class in order to support int8
• Use Cython to speed up the code