- 14

In this assignment, we would implement a GPU graph executor that can train simple neural nets such as multilayer perceptron models. Our code should be able to construct a simple MLP model using computation graph API implemented in Assignment 1, and train and test the model using either numpy or GPU. If you implement everything correctly, you would see nice speedup in training neural nets with GPU executor compared to numpy executor, as expected.

http://dlsys.cs.washington.edu/https://github.com/dlsys-course/assignment2-2017

Tags | gpu-kernels computation-graph deep-learning neural-nets gpu-executor |

Implementation | Python |

License | Public |

Platform | Windows Linux |

Training very deep neural networks requires a lot of memory. Using the tools in this package, developed jointly by Tim Salimans and Yaroslav Bulatov, you can trade off some of this memory usage with computation to make your model fit into memory more easily. For feed-forward models we were able to fit more than 10x larger models onto our GPU, at only a 20% increase in computation time. The memory intensive part of training deep neural networks is computing the gradient of the loss by backpropagation. By checkpointing nodes in the computation graph defined by your model, and recomputing the parts of the graph in between those nodes during backpropagation, it is possible to calculate this gradient at reduced memory cost. When training deep feed-forward neural networks consisting of n layers, we can reduce the memory consumption to O(sqrt(n)) in this way, at the cost of performing one additional forward pass (see e.g. Training Deep Nets with Sublinear Memory Cost, by Chen et al. (2016)). This repository provides an implementation of this functionality in Tensorflow, using the Tensorflow graph editor to automatically rewrite the computation graph of the backward pass.

DSSTNE (pronounced "Destiny") is an open source software library for training and deploying recommendation models with sparse inputs, fully connected hidden layers, and sparse outputs. Models with weight matrices that are too large for a single GPU can still be trained on a single host. DSSTNE has been used at Amazon to generate personalized product recommendations for our customers at Amazon's scale.

deep-learning neural-networks artificial-intelligence personalizationThis repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. These topics are organised into three high level themes forming a progression from understanding the use of neural networks for sequential language modelling, to understanding their use as conditional language models for transduction tasks, and finally to approaches employing these techniques in combination with other mechanisms for advanced applications. Throughout the course the practical implementation of such models on CPU and GPU hardware is also discussed.

deep-learning machine-learning natural-language-processing nlp oxfordPyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, Deep Neural Networks built on a tape-based autograd system. You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed.

neural-network autograd gpu numpy deep-learning tensorPyTorch is a flexible deep learning framework that allows automatic differentiation through dynamic neural networks (i.e., networks that utilise dynamic control flow like if statements and while loops). It supports GPU acceleration, distributed training, various optimisations, and plenty more neat features. These are some notes on how I think about using PyTorch, and don't encompass all parts of the library or every best practice, but may be helpful to others. Neural networks are a subclass of computation graphs. Computation graphs receive input data, and data is routed to and possibly transformed by nodes which perform processing on the data. In deep learning, the neurons (nodes) in neural networks typically transform data with parameters and differentiable functions, such that the parameters can be optimised to minimise a loss via gradient descent. More broadly, the functions can be stochastic, and the structure of the graph can be dynamic. So while neural networks may be a good fit for dataflow programming, PyTorch's API has instead centred around imperative programming, which is a more common way for thinking about programs. This makes it easier to read code and reason about complex programs, without necessarily sacrificing much performance; PyTorch is actually pretty fast, with plenty of optimisations that you can safely forget about as an end user (but you can dig in if you really want to).

deep-learningKnet uses dynamic computational graphs generated at runtime for automatic differentiation of (almost) any Julia code. This allows machine learning models to be implemented by defining just the forward calculation (i.e. the computation from parameters and data to loss) using the full power and expressivity of Julia. The implementation can use helper functions, loops, conditionals, recursion, closures, tuples and dictionaries, array indexing, concatenation and other high level language features, some of which are often missing in the restricted modeling languages of static computational graph systems like Theano, Torch, Caffe and Tensorflow. GPU operation is supported by simply using the KnetArray type instead of regular Array for parameters and data. Knet builds a dynamic computational graph by recording primitive operations during forward calculation. Only pointers to inputs and outputs are recorded for efficiency. Therefore array overwriting is not supported during forward and backward passes. This encourages a clean functional programming style. High performance is achieved using custom memory management and efficient GPU kernels. See Under the hood for more details.

DeepLearning.scala is a simple library for creating complex neural networks from object-oriented and functional programming constructs. Like other deep learning toolkits, DeepLearning.scala allows you to build neural networks from mathematical formulas. It supports floats, doubles, GPU-accelerated N-dimensional arrays, and calculates derivatives of the weights in the formulas.

automatic-differentiation deep-neural-networks deep-learning neural-network functional-programming symbolic-computation dsl domain-specific-language machine-learning**This project is no longer active. Please check out TensorFlow.js.** The Keras.js demos still work but is no longer updated. Run Keras models in the browser, with GPU support provided by WebGL 2. Models can be run in Node.js as well, but only in CPU mode. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc.

deep-learning machine-learning webgl tensorflow neural-networks keras deep learning neural networks webgl2 gpuMMLSpark provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.MMLSpark requires Scala 2.11, Spark 2.1+, and either Python 2.7 or Python 3.5+. See the API documentation for Scala and for PySpark.

machine-learning spark cntk pyspark azure microsoft-machine-learning microsoft mlHebel is a library for deep learning with neural networks in Python using GPU acceleration with CUDA through PyCUDA. It implements the most important types of neural network models and offers a variety of different activation functions and training methods such as momentum, Nesterov momentum, dropout, and early stopping. I no longer actively develop Hebel. If you are looking for a deep learning framework in Python, I now recommend Chainer.

keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.

keras tensorflow theano reinforcement-learning neural-networks machine-learningGPU accelerated handwritten digit recognition with regl. Note that this network will probably be slower than the corresponding network implemented on the CPU. This is because of the overhead associated with transferring data to and from the GPU. But in the future we will attempt implementing more complex networks in the browser, such as Neural Style, and then we think that we will see a significant speedup compared to the CPU.

regl cnn digit-recognition demo gpu webgl convolutional-neural-networks gpgpu deep-learning glsl digit recognition mnist convolutional neural network networksArraymancer is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing and in particular a deep learning ecosystem. The library is inspired by Numpy and PyTorch. The library provides ergonomics very similar to Numpy, Julia and Matlab but is fully parallel and significantly faster than those libraries. It is also faster than C-based Torch.

tensor nim multidimensional-arrays cuda deep-learning machine-learning cudnn high-performance-computing gpu-computing matrix-library neural-networks parallel-computing openmp linear-algebra ndarray opencl gpgpu iot automatic-differentiation autogradThe Microsoft Cognitive Toolkit is a free, easy-to-use, open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. It is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.

deep-learning neural-networks artificial-intelligenceChainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference. For more details of Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter. The stable version of current Chainer is separated in here: v3.

deep-learning neural-networks machine-learning gpu cuda cudnn numpy cupy chainer neural-networkTel-Aviv Deep Learning Bootcamp is an intensive (and free!) 5-day program intended to teach you all about deep learning. It is nonprofit focused on advancing data science education and fostering entrepreneurship. The Bootcamp is a prominent venue for graduate students, researchers, and data science professionals. It offers a chance to study the essential and innovative aspects of deep learning. Participation is via a donation to the A.L.S ASSOCIATION for promoting research of the Amyotrophic Lateral Sclerosis (ALS) disease.

gpu nvidia docker-image machine-learning deep-learning data-science cuda-kernels kaggle-competition cuda pytorch pytorch-tutorials pytorch-tutorial bootcamp meetup kaggle kaggle-scripts pycudaGunrock is a CUDA library for graph-processing designed specifically for the GPU. It uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. For more details, please visit our website, read Why Gunrock, our TOPC 2017 paper Gunrock: GPU Graph Analytics, look at our results, and find more details in our publications. See Release Notes to keep up with the our latest changes.

gunrock cuda graph-processing graph-analytics gpu graph-primitivesThis guide should help fellow researchers and hobbyists to easily automate and accelerate there deep leaning training with their own Kubernetes GPU cluster. Therefore I will explain how to easily setup a GPU cluster on multiple Ubuntu 16.04 bare metal servers and provide some useful scripts and .yaml files that do the entire setup for you. By the way: If you need a Kubernetes GPU-cluster for other reasons, this guide might be helpful to you as well.

kubernetes kubernetes-cluster kubernetes-setup deep-learning gpu-computing distributed-systems guide kubernetes-gpu-cluster cluster gpu worker-nodesGraph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet. Contact graph-nets@google.com for comments and questions.

graph-networks graphs deep-learning neural-networks tensorflow sonnet artificial-intelligenceDLL is a library that aims to provide a C++ implementation of Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) and their convolution versions as well. It also has support for some more standard neural networks. Note: When you clone the library, you need to clone the sub modules as well, using the --recursive option.

c-plus-plus cpp cpp11 cpp14 performance machine-learning deep-learning artificial-neural-networks gpu rbm cpu convolutional-neural-networks
We have large collection of open source products. Follow the tags from
Tag Cloud >>

Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
**Add Projects.**