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TensorFlow

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  Analyzed 4 months ago

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy ... [More] computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. [Less]

3.09M lines of code

798 current contributors

4 months since last commit

23 users on Open Hub

Activity Not Available
5.0
 
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deeplearning4j

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  Analyzed about 17 hours ago

Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library; designed to be used in business environments. Deeplearning4j aims to be cutting-edge plug and play, more convention than configuration, which allows for fast prototyping for non-researchers. Vast ... [More] support of scale out: Hadoop, Spark and Akka + AWS et al It includes both a distributed, multi-threaded deep-learning framework and a normal single-threaded deep-learning framework. Iterative reduce net training. First framework adapted for a micro-service architecture. A versatile n-dimensional array class. GPU integration [Less]

1.14M lines of code

17 current contributors

3 days since last commit

5 users on Open Hub

Moderate Activity
4.0
   
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MXNet

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Claimed by Apache Software Foundation Analyzed 1 day ago

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, and more MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of symbolic programming and ... [More] imperative programming together to maximize the efficiency and your productivity. In its core, a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer is build on top, which makes symbolic execution fast and memory efficient. The library is portable and lightweight, and is ready scales to multiple GPUs, and multiple machines. [Less]

424K lines of code

208 current contributors

9 days since last commit

1 users on Open Hub

Moderate Activity
0.0
 
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