X2Paddle - X2Paddle is a toolkit for converting trained model to PaddlePaddle from other deep learning frameworks

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X2Paddle is a toolkit for converting trained model to PaddlePaddle from other deep learning frameworks




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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.

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models - Model configurations

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PaddlePaddle provides a rich set of computational units to enable users to adopt a modular approach to solving various learning problems. In this repo, we demonstrate how to use PaddlePaddle to solve common machine learning tasks, providing several different neural network model that anyone can easily learn and use. The word embedding expresses words with a real vector. Each dimension of the vector represents some of the latent grammatical or semantic features of the text and is one of the most successful concepts in the field of natural language processing. The generalized word vector can also be applied to discrete features. The study of word vector is usually an unsupervised learning. Therefore, it is possible to take full advantage of massive unmarked data to capture the relationship between features and to solve the problem of sparse features, missing tag data, and data noise. However, in the common word vector learning method, the last layer of the model often encounters a large-scale classification problem, which is the bottleneck of computing performance.

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

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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.

book - Deep Learning 101 with PaddlePaddle

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This book you are reading is interactive -- each chapter can run as a Jupyter Notebook. We packed this book, Jupyter, PaddlePaddle, and all dependencies into a Docker image. So you don't need to install anything except Docker. If you are using Windows, please follow this installation guide. If you are running Mac, please follow this. For various Linux distros, please refer to https://www.docker.com. If you are using Windows or Mac, you might want to give Docker more memory and CPUs/cores.

Paddle - PArallel Distributed Deep LEarning

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Welcome to the PaddlePaddle GitHub. PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.

caffe-tensorflow - Caffe models in TensorFlow

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Convert Caffe models to TensorFlow. Run convert.py to convert an existing Caffe model to TensorFlow.

pytorch-openai-transformer-lm - A PyTorch implementation of OpenAI's finetuned transformer language model with a script to import the weights pre-trained by OpenAI

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This is a PyTorch implementation of the TensorFlow code provided with OpenAI's paper "Improving Language Understanding by Generative Pre-Training" by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. This implementation comprises a script to load in the PyTorch model the weights pre-trained by the authors with the TensorFlow implementation.

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.

tensorflow-vgg16 - conversation of caffe vgg16 model to tensorflow

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VGG-16 is my favorite image classification model to run because of its simplicity and accuracy. The creators of this model published a pre-trained binary that can be used in Caffe. This is to convert that specific file to a TensorFlow model and check its correctness.

DuReader - Baseline Systems of DuReader Dataset

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DuReader system implements 2 classic reading comprehension models(BiDAF and Match-LSTM) on DuReader dataset. The system is implemented with 2 frameworks: PaddlePaddle and TensorFlow. For more details about DuReader dataset please refer to DuReader Homepage.

polyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes

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Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.

models - The ONNX Model Zoo is a collection of pre-trained, state-of-the-art models in the ONNX format

  •    Jupyter

The ONNX Model Zoo is a collection of pre-trained models for state-of-the-art models in deep learning, available in the ONNX format. Accompanying each model are Jupyter notebooks for model training and running inference with the trained model. The notebooks are written in Python and include links to the training dataset as well as references to the original paper that describes the model architecture. The notebooks can be exported and run as python(.py) files. The Open Neural Network eXchange (ONNX) is a open format to represent deep learning models. With ONNX, developers can move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners.

functional-zoo - PyTorch and Tensorflow functional model definitions

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PyTorch, unlike lua torch, has autograd in it's core, so using modular structure of torch.nn modules is not necessary, one can easily allocate needed Variables and write a function that utilizes them, which is sometimes more convenient. This repo contains model definitions in this functional way, with pretrained weights for some models. Weights are serialized as a dict of arrays in hdf5, so should be easily loadable in other frameworks. Thanks to @edgarriba we have cpp_parser for loading weights in C++.

generative-models - Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

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Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.

pytorch-faster-rcnn - 0.4 updated. Support cpu test and demo.

  •    Jupyter

The main differences between new and old master branch are in this two commits: 9d4c24e, c899ce7 The change is related to this issue; master now matches all the details in tf-faster-rcnn so that we can now convert pretrained tf model to pytorch model. A pytorch implementation of faster RCNN detection framework based on Xinlei Chen's tf-faster-rcnn. Xinlei Chen's repository is based on the python Caffe implementation of faster RCNN available here.