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Apache MXNet

A deep learning software framework, used to train, and deploy deep neural networks

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+Multi-Language SupportSupports multiple programming languages including Python, R, Julia, and Go.
+Hybrid ProgrammingCombines declarative symbolic expression with imperative tensor computation
+Auto DifferentiationOffers automatic differentiation to derive gradients
+Efficient ExecutionRuns efficiently on various systems, from mobile devices to GPU clusters
+OptimizationProvides global computation graph for performance and memory optimization
+KVStoreFacilitates data synchronization over devices with a distributed key-value store
+Graph OptimizationEmploys strategies like memory allocation and operation grouping for efficiency
+ScalableDesigned for scalability across distributed systems for large-scale deep neural network applications
-Community SizeSmaller open-source community compared to TensorFlow.
-Delayed ImprovementsLonger time for improvements and bug fixes due to limited community support.
-Data-ParallelismSupports data-parallelism only (single GPU model size limitation)
-Sparse Model IncompatibilityNot friendly to sparse models; BytePS focuses on dense DNN models.
-Scala Conversion OverheadOverhead in converting collections when using Scala
-Increased JAR File SizeLarger JAR files due to Scala inclusion




System Requirements

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Apache Software Foundation

Written in

C++, Python, Perl, Scala, CUDA, R, JavaScript, Julia, Golang

Initial Release

8 April 2014