{"data":{"slug":"apache-mxnet","name":"mxnet","tagline":"Lightweight, Portable, Flexible Distributed/Mobile Deep Learning Framework","github_url":"https://github.com/apache/mxnet","owner":"apache","repo":"mxnet","owner_avatar_url":"https://avatars.githubusercontent.com/u/47359?v=4","primary_language":"C++","stars":20817,"forks":6690,"topics":["mxnet"],"archived":true,"github_pushed_at":"2023-10-25T21:28:33+00:00","maintenance_label":"Archived","url":"https://www.graphcanon.com/tools/apache-mxnet","markdown_url":"https://www.graphcanon.com/tools/apache-mxnet.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/apache-mxnet","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=apache-mxnet","description":"Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more","homepage_url":"https://mxnet.apache.org","license":"Apache-2.0","open_issues":2007,"watchers":22,"ai_summary":"Apache MXNet is designed for efficiency and flexibility, supporting symbolic and imperative programming to maximize productivity. It offers automatic parallelization of operations and optimization for fast and memory-efficient execution.","readme_excerpt":"<div align=\"center\">\n  <a href=\"https://mxnet.apache.org/\"><img src=\"https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet_logo_2.png\"></a><br>\n</div>\n\n\n\nApache MXNet for Deep Learning\n===========================================\n         \n\nApache MXNet is a deep learning framework designed for both *efficiency* and *flexibility*.\nIt allows you to ***mix*** [symbolic and imperative programming](https://mxnet.apache.org/api/architecture/program_model)\nto ***maximize*** efficiency and productivity.\nAt its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.\nA graph optimization layer on top of that makes symbolic execution fast and memory efficient.\nMXNet is portable and lightweight, scalable to many GPUs and machines.\n\nApache MXNet is more than a deep learning project. It is a [community](https://mxnet.apache.org/versions/master/community)\non a mission of democratizing AI. It is a collection of [blue prints and guidelines](https://mxnet.apache.org/api/architecture/overview)\nfor building deep learning systems, and interesting insights of DL systems for hackers.\n\nLicensed under an [Apache-2.0](https://github.com/apache/mxnet/blob/master/LICENSE) license.\n\n| Branch  | Build Status  |\n|:-------:|:-------------:|\n| [master](https://github.com/apache/mxnet/tree/master) |    <br>    <br>    <br>    |\n| [v1.x](https://github.com/apache/mxnet/tree/v1.x) |    <br>    <br>    <br>    |\n\nFeatures\n--------\n* NumPy-like programming interface, and is integrated with the new, easy-to-use Gluon 2.0 interface. NumPy users can easily adopt MXNet and start in deep learning.\n* Automatic hybridization provides imperative programming with the performance of traditional symbolic programming.\n* Lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, and through ecosystem projects such as [TVM](https://tvm.ai), [TensorRT](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html), [OpenVINO](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html).\n* Scales up to multi GPUs and distributed setting with auto parallelism through [ps-lite](https://github.com/dmlc/ps-lite), [Horovod](https://github.com/horovod/horovod), and [BytePS](https://github.com/bytedance/byteps).\n* Extensible backend that supports full customization, allowing integration with custom accelerator libraries and in-house hardware without the need to maintain a fork.\n* Support for [Python](https://mxnet.apache.org/api/python), [Java](https://mxnet.apache.org/api/java), [C++](https://mxnet.apache.org/api/cpp), [R](https://mxnet.apache.org/api/r), [Scala](https://mxnet.apache.org/api/scala), [Clojure](https://mxnet.apache.org/api/clojure), [Go](https://github.com/jdeng/gomxnet/), [Javascript](https://github.com/dmlc/mxnet.js/), [Perl](https://mxnet.apache.org/api/perl), and [Julia](https://mxnet.apache.org/api/julia).\n* Cloud-friendly and directly compatible with AWS and Azure.\n\nContents\n--------\n* [Installation](https://mxnet.apache.org/get_started)\n* [Tutorials](https://mxnet.apache.org/api/python/docs/tutorials/)\n* [Ecosystem](https://mxnet.apache.org/ecosystem)\n* [API Documentation](https://mxnet.apache.org/api)\n* [Examples](https://github.com/apache/mxnet-examples)\n* [Stay Connected](#stay-connected)\n* [Social Media](#social-media)\n\nWhat's New\n----------\n* [1.9.1 Release](https://github.com/apache/mxnet/releases/tag/1.9.1) - MXNet 1.9.1 Release.\n* [1.8.0 Release](https://github.com/apache/mxnet/releases/tag/1.8.0) - MXNet 1.8.0 Release.\n* [1.7.0 Release](https://github.com/apache/mxnet/releases/tag/1.7.0) - MXNet 1.7.0 Release.\n* [1.6.0 Release](https://github.com/apache/mxnet/releases/tag/1.6.0) - MXNet 1.6.0 Release.\n* [1.5.1 Release](https://github.com/apache/mxnet/releases/tag/1.5.1) - MXNet 1.5.1 Patch Release.\n* [1.5.0 Release](https://github.com/apache/mxnet/releases/tag/1.5.0) - MXNet 1","github_created_at":"2015-04-30T16:21:15+00:00","created_at":"2026-07-11T23:22:33.427401+00:00","updated_at":"2026-08-03T00:01:54.875882+00:00","categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"auto-hybridization","name":"auto hybridization"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"distributed-computing","name":"distributed-computing"},{"slug":"flexible","name":"flexible"},{"slug":"lightweight","name":"lightweight"},{"slug":"mobile","name":"mobile"},{"slug":"symbolic-and-imperative-programming","name":"symbolic and imperative programming"}],"trust":{"provenance":{"is_fork":false,"github_id":34864402,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-03T00:01:54.164Z","maintenance":{"label":"Archived","score":8,"methodology":"github_public_v1","releases_90d":0,"days_since_push":1012,"last_release_at":"2022-05-10T20:10:05Z"},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:22:40.388Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-03T00:01:54.601Z"},"languages":{"value":["c++"],"source":"github.language","observed_at":"2026-08-03T00:01:54.601Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-03T00:01:54.601Z"}},"decision_facts":{"hosting":null,"pricing":{"model":"freemium","summary":"Open-source, open-access framework with advanced services potentially requiring proprietary add-ons or cloud service costs."},"requirements":{"notes":["MXNet is known for its lightweight nature and efficient memory management, making it suitable for deployment on various hardware configurations."],"min_ram_gb":null},"constraints":{"min_ram_gb":null,"pricing_model":"freemium"},"when_to_use":["You prefer to mix symbolic and imperative programming styles in your deep learning projects for maximum productivity and performance.","Your project requires lightweight and memory-efficient execution, especially on smart devices due to native cross-compilation support.","You need the framework to be highly portable across multiple computing environments including cloud services like AWS and Azure."],"when_not_to_use":["If you require a framework with more out-of-the-box models and easier-to-use libraries, since MXNet focuses on flexibility and efficiency over convenience in pre-built functionalities.","You are focusing exclusively on one particular programming language (other than Python), as while MXNet supports multiple languages, most community support and updates center around its Python API."],"source":"enrich:decision_facts","observed_at":"2026-07-12T12:49:58.134Z"},"constraint_facets":{"min_ram_gb":null,"pricing_model":"freemium"},"decision_summary":[{"label":"Pricing","value":"freemium - 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