Displaying 1 to 11 from 11 results

caffe2 - Caffe2 is a lightweight, modular, and scalable deep learning framework.

  •    C++

Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.

onnx - Open Neural Network Exchange

  •    PureBasic

Open Neural Network Exchange (ONNX) is the first step toward an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Initially we focus on the capabilities needed for inferencing (evaluation). Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are developing ONNX support. Enabling interoperability between different frameworks and streamlining the path from research to production will increase the speed of innovation in the AI community. We are an early stage and we invite the community to submit feedback and help us further evolve ONNX.

deep-learning-model-convertor - The convertor/conversion of deep learning models for different deep learning frameworks/softwares

  •    

Note: This is not one convertor for all frameworks, but a collection of different converters. Because github is an open source platform, I hope we can help each other here, gather everyone's strength. The sheet below is a overview of all convertors in github (not only contain official provided and more are user-self implementations). I just make a little work to collect these convertors. Also, hope everyone can support this project to help more people who're also crazy because of various frameworks.

caffe2 - Caffe2 is a lightweight, modular, and scalable deep learning framework.

  •    Shell

Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.




deepo - A series of Docker images (and their generator) that allows you to quickly set up your deep learning research environment

  •    Python

If you want to share your data and configurations between the host (your machine or VM) and the container in which you are using Deepo, use the -v option, e.g. This will make /host/data from the host visible as /data in the container, and /host/config as /config. Such isolation reduces the chances of your containerized experiments overwriting or using wrong data.

ngraph - nGraph is an open source C++ library, compiler and runtime for Deep Learning frameworks

  •    C++

Welcome to the open-source repository for the Intel® nGraph™ Library. Our code base provides a Compiler and runtime suite of tools (APIs) designed to give developers maximum flexibility for their software design, allowing them to create or customize a scalable solution using any framework while also avoiding device-level hardware lock-in that is so common with many AI vendors. A neural network model compiled with nGraph can run on any of our currently-supported backends, and it will be able to run on any backends we support in the future with minimal disruption to your model. With nGraph, you can co-evolve your software and hardware's capabilities to stay at the forefront of your industry. The nGraph Compiler is Intel's graph compiler for Artificial Neural Networks. Documentation in this repo describes how you can program any framework to run training and inference computations on a variety of Backends including Intel® Architecture Processors (CPUs), Intel® Nervana™ Neural Network Processors (NNPs), cuDNN-compatible graphics cards (GPUs), custom VPUs like Movidius, and many others. The default CPU Backend also provides an interactive Interpreter mode that can be used to zero in on a DL model and create custom nGraph optimizations that can be used to further accelerate training or inference, in whatever scenario you need.

test-tube - Python library to easily log, track machine learning code, experiments and parallelize hyperparameter search

  •    HTML

Test tube is a python library to track and parallelize hyperparameter search for Deep Learning and ML experiments. It's framework agnostic and built on top of the python argparse API for ease of use. If you're a researcher, test-tube is highly encouraged as a way to post your paper's training logs to help add transparency and show others what you've tried that didn't work.


onnx-caffe2 - Caffe2 implementation of Open Neural Network Exchange (ONNX)

  •    Python

Caffe2 implementation of Open Neural Network Exchange (ONNX). Repository location may change.

c2board - Tensorboard for Caffe2

  •    Python

A hacked-up visualization tool for caffe2. Specifically, it dumps the computation graph and the training statistics of caffe2 into a tensorboard compatible format. Once it starts dumping, you can use tensorboard to visualize the results. These screen shots are taken when training a detector with detectron.

dlcookbook-dlbs - Deep Learning Benchmarking Suite

  •    Python

Deep Learning Benchmarking Suite was tested on various servers with Ubuntu / RedHat / CentOS operating systems with and without NVIDIA GPUs. We have a little success with running DLBS on top of AMD GPUs, but this is mostly untested. It may not work with Mac OS due to slightly different command line API of some of the tools we use (like, for instance, sed) - we will fix this in one of the next releases. Install Docker and NVIDIA Docker for containerized benchmarks. Read here why we prefer to use docker and here for installing/troubleshooting tips. This is not required. DLBS can work with bare metal framework installations.