{"id":13418030,"url":"https://github.com/dmlc/mshadow","last_synced_at":"2025-09-28T20:31:50.508Z","repository":{"id":12329429,"uuid":"14967911","full_name":"dmlc/mshadow","owner":"dmlc","description":"Matrix Shadow:Lightweight CPU/GPU Matrix and Tensor  Template Library in C++/CUDA for (Deep) Machine Learning","archived":true,"fork":false,"pushed_at":"2019-08-04T00:45:20.000Z","size":1514,"stargazers_count":1108,"open_issues_count":37,"forks_count":431,"subscribers_count":102,"default_branch":"master","last_synced_at":"2024-09-27T03:03:22.839Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dmlc.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGES.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2013-12-05T22:47:13.000Z","updated_at":"2024-09-20T18:53:45.000Z","dependencies_parsed_at":"2022-09-13T08:12:01.914Z","dependency_job_id":null,"html_url":"https://github.com/dmlc/mshadow","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmlc%2Fmshadow","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmlc%2Fmshadow/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmlc%2Fmshadow/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmlc%2Fmshadow/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dmlc","download_url":"https://codeload.github.com/dmlc/mshadow/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":234563123,"owners_count":18853056,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-07-30T22:00:57.484Z","updated_at":"2025-09-28T20:31:50.126Z","avatar_url":"https://github.com/dmlc.png","language":"C++","funding_links":[],"categories":["TODO scan for Android support in followings","C++"],"sub_categories":[],"readme":"mshadow: Matrix Shadow\n======\nThis code base has been donated to the Apache MXNet project per [#373](https://github.com/dmlc/mshadow/issues/373), and repo is deprecated. Future development should continue in Apache MXNet.\n\n[![Build Status](https://travis-ci.org/dmlc/mshadow.svg?branch=master)](https://travis-ci.org/dmlc/mshadow)\n\nMShadow is a lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. The goal of mshadow is to support ***efficient***,\n***device invariant*** and ***simple*** tensor library for machine learning project that aims for maximum performance and control, while also emphasize simplicity.\n\nMShadow also provides interface that allows writing Multi-GPU and distributed deep learning programs in an easy and unified way.\n\n* [Contributors](https://github.com/tqchen/mshadow/graphs/contributors)\n* [Tutorial](guide)\n* [Documentation](doc)\n* [Parameter Server Interface for GPU Tensor](guide/mshadow-ps)\n\nFeatures\n--------\n* Efficient: all the expression you write will be lazily evaluated and compiled into optimized code\n  - No temporal memory allocation will happen for expression you write\n  - mshadow will generate specific kernel for every expression you write in compile time.\n* Device invariant: you can write one code and it will run on both CPU and GPU\n* Simple: mshadow allows you to write machine learning code using expressions.\n* Whitebox: put a float* into the Tensor struct and take the benefit of the package, no memory allocation is happened unless explicitly called\n* Lightweight library: light amount of code to support frequently used functions in machine learning\n* Extendable: user can write simple functions that plugs into mshadow and run on GPU/CPU, no experience in CUDA is required.\n* MultiGPU and Distributed ML: mshadow-ps interface allows user to write efficient MultiGPU and distributed programs in an unified way.\n\nVersion\n-------\n* This version mshadow-2.x, there are a lot of changes in the interface and it is not backward compatible with mshadow-1.0\n  - If you use older version of cxxnet, you will need to use the legacy mshadow code\n* For legacy code, refer to [Here](https://github.com/tqchen/mshadow/releases/tag/v1.1)\n* Change log in [CHANGES.md](CHANGES.md)\n\nProjects Using MShadow\n----------------------\n* [MXNet: Efficient and Flexible Distributed Deep Learning Framework](https://github.com/dmlc/mxnet)\n* [CXXNet: A lightweight  C++ based deep learnig framework](https://github.com/dmlc/cxxnet)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmlc%2Fmshadow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdmlc%2Fmshadow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmlc%2Fmshadow/lists"}