{"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":20815,"forks":6698,"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":21,"ai_summary":"Apache MXNet is a deep learning framework optimized for efficiency and flexibility with support for multiple programming languages including Python, R, Julia, Scala, Go, and JavaScript. It offers dynamic dependency scheduling and graph optimization for 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":"2026-07-12T01:17:48.216767+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"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"}],"tags":[{"slug":"scalable-computing","name":"scalable computing"},{"slug":"deep-learning-framework","name":"deep learning framework"},{"slug":"high-performance-programming-interface","name":"high performance programming interface"},{"slug":"portability-to-smart-devices","name":"portability to smart devices"},{"slug":"multi-language-support","name":"multi-language support"},{"slug":"apache-2-0-license","name":"apache-2.0 license"},{"slug":"hybridization","name":"hybridization"}],"trust":{"provenance":{"is_fork":false,"github_id":34864402,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T23:22:39.843Z","maintenance":{"label":"Archived","score":8,"methodology":"github_public_v1","releases_90d":0,"days_since_push":990,"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":"2026-07-12T01:17:48.147Z"},"languages":{"value":["c++"],"source":"github.language","observed_at":"2026-07-12T01:17:48.147Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-07-12T01:17:48.147Z"}}}}