# dmlc/mxnet **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/dmlc-mxnet).** 20,812 stars · 6,702 forks · C++ · Apache-2.0 · archived ## Links - GitHub: https://github.com/dmlc/mxnet - Homepage: https://mxnet.apache.org - awesome-repositories: https://awesome-repositories.com/repository/dmlc-mxnet.md ## Description MXNet is a deep learning framework and distributed machine learning engine designed for training and deploying neural networks. It functions as a hardware-agnostic backend that allows for the development of deep learning models through a hybrid of symbolic and imperative programming. The system distinguishes itself through automatic distributed parallelism, which scales training workloads across multiple GPUs and machines. It features an extensible hardware backend interface that enables the integration of custom accelerators and proprietary libraries without modifying the core source code. The framework provides a cross-platform model runtime with multi-language bindings, allowing models to be developed and executed across various programming languages. It further supports mobile deployment by cross-compiling native code for ARM architectures to run on portable devices. ## Tags ### Part of an Awesome List - [Deep Learning](https://awesome-repositories.com/f/awesome-lists/ai/deep-learning.md) — Serves as a scalable deep learning framework for training and deploying neural networks using hybrid programming. - [Distributed Parallelism](https://awesome-repositories.com/f/awesome-lists/ai/distributed-parallelism.md) — Implements automatic distributed parallelism to scale model training and inference across multiple GPUs and machines. - [Machine Learning](https://awesome-repositories.com/f/awesome-lists/ai/machine-learning.md) — Flexible deep learning and GPU-accelerated computing. ### Artificial Intelligence & ML - [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training.md) — Distributes deep learning tasks across multiple GPUs and machines using automatic parallelism for large datasets. ([source](https://github.com/dmlc/mxnet#readme)) - [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks/distributed-training.md) — Scales the training of large neural networks across multiple GPUs and machines to handle massive datasets. - [Hardware Acceleration Abstractions](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-acceleration-abstractions.md) — Provides a unified hardware acceleration abstraction layer that allows custom accelerators to be plugged into the core engine. - [Hybrid Programming Paradigms](https://awesome-repositories.com/f/artificial-intelligence-ml/hybrid-programming-paradigms.md) — Balances rapid development speed and high execution efficiency by combining symbolic and imperative programming styles. - [Machine Learning Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-engines.md) — Functions as a distributed machine learning engine that scales training workloads via automatic parallelism. - [Hardware-Agnostic Inference Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/inference-engines/hardware-agnostic-inference-layers.md) — Implements a hardware-agnostic backend that decouples model execution logic from specific hardware accelerators. - [Neural Network Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-deployment.md) — Enables trained models to be executed across both mobile and desktop platforms using flexible language bindings. ([source](https://github.com/dmlc/mxnet#readme)) - [Neural Network Trainers](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-networks/neural-network-trainers.md) — Provides an optimized training environment using a hybrid of symbolic and imperative programming to execute neural network training loops. ([source](https://github.com/dmlc/mxnet#readme)) - [Hardware Acceleration Backends](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-acceleration-backends.md) — Offers an extensible interface for integrating custom accelerator libraries and proprietary hardware without modifying core code. ([source](https://github.com/dmlc/mxnet#readme)) - [Mobile](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning/pre-trained-model-zoos/model-deployment/mobile.md) — Optimizes and deploys deep learning models for ARM-based portable devices using hardware accelerators. - [Multi-Language Model Development](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-language-model-development.md) — Enables the building and execution of neural networks using various languages including Python, R, Scala, and Go. ### Programming Languages & Runtimes - [Hybrid Execution Modes](https://awesome-repositories.com/f/programming-languages-runtimes/runtime-execution-environments/runtime-environments/runtimes/graph-symbolic-execution-engines/hybrid-execution-modes.md) — Combines a static computation graph for performance optimization with an imperative interface for flexible model development. - [Cross-Language Bindings Layers](https://awesome-repositories.com/f/programming-languages-runtimes/language-interoperability/foreign-function-interfaces/cross-language-bindings-layers.md) — Uses a C++ core with wrapper layers to expose deep learning functionality across multiple high-level programming languages. ### Software Engineering & Architecture - [Dataflow Dependency Scheduling](https://awesome-repositories.com/f/software-engineering-architecture/execution-graphs/graph-evaluation-scheduling/dataflow-dependency-scheduling.md) — Schedules operations by tracking dependencies in a dynamic graph to execute nodes immediately as data dependencies are met. ### Web Development - [Deep Learning Frameworks](https://awesome-repositories.com/f/web-development/state-management-models/state-space-models/deep-learning-frameworks.md) — Provides a scalable environment to build and optimize deep learning models through distributed training workloads. ([source](https://github.com/dmlc/mxnet#readme)) ### Development Tools & Productivity - [Mobile ARM Cross-Compilation](https://awesome-repositories.com/f/development-tools-productivity/native-compilation/mobile-arm-cross-compilation.md) — Provides the ability to cross-compile native code for ARM architectures to enable neural network execution on mobile devices. ### DevOps & Infrastructure - [Cross-Platform Runtimes](https://awesome-repositories.com/f/devops-infrastructure/execution-environments/code-execution-runtimes/cross-platform-runtimes.md) — Provides a cross-platform runtime for deploying trained models to mobile and desktop devices across various languages. ### Mobile Development - [Mobile Model Deployment](https://awesome-repositories.com/f/mobile-development/mobile-model-deployment.md) — Supports the deployment of neural networks on portable smart devices using ARM-native cross-compilation. ([source](https://github.com/dmlc/mxnet#readme)) ### Operating Systems & Systems Programming - [Hardware Acceleration](https://awesome-repositories.com/f/operating-systems-systems-programming/hardware-interfacing-drivers/hardware-acceleration.md) — Provides an extensible interface to integrate proprietary hardware accelerators and specialized libraries.