| Apache MXNet (incubating) for Deep Learning |
| ===== |
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| [](https://builds.apache.org/job/incubator-mxnet/job/master/) |
| [](https://mxnet.incubator.apache.org/) |
| [](./LICENSE) |
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|  |
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| Apache MXNet (incubating) is a deep learning framework designed for both *efficiency* and *flexibility*. |
| It allows you to ***mix*** [symbolic and imperative programming](https://mxnet.incubator.apache.org/architecture/index.html#deep-learning-system-design-concepts) |
| to ***maximize*** efficiency and productivity. |
| At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. |
| A graph optimization layer on top of that makes symbolic execution fast and memory efficient. |
| MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. |
| |
| MXNet is also more than a deep learning project. It is also a collection of |
| [blue prints and guidelines](https://mxnet.incubator.apache.org/architecture/index.html#deep-learning-system-design-concepts) for building |
| deep learning systems, and interesting insights of DL systems for hackers. |
| |
| [](https://gitter.im/dmlc/mxnet?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) |
| |
| What's New |
| ---------- |
| * [Version 0.12.1 Release](https://github.com/apache/incubator-mxnet/releases/tag/0.12.1) - MXNet 0.12.1 Patch Release. |
| * [Version 0.12.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/0.12.0) - MXNet 0.12.0 Release. |
| * [Version 0.11.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/0.11.0) - MXNet 0.11.0 Release. |
| * [Apache Incubator](http://incubator.apache.org/projects/mxnet.html) - We are now an Apache Incubator project. |
| * [Version 0.10.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.10.0) - MXNet 0.10.0 Release. |
| * [Version 0.9.3 Release](./docs/architecture/release_note_0_9.md) - First 0.9 official release. |
| * [Version 0.9.1 Release (NNVM refactor)](./docs/architecture/release_note_0_9.md) - NNVM branch is merged into master now. An official release will be made soon. |
| * [Version 0.8.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.8.0) |
| * [Updated Image Classification with new Pre-trained Models](./example/image-classification) |
| * [Python Notebooks for How to Use MXNet](https://github.com/dmlc/mxnet-notebooks) |
| * [MKLDNN for Faster CPU Performance](./MKL_README.md) |
| * [MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost](https://github.com/dmlc/mxnet-memonger) |
| * [Tutorial for NVidia GTC 2016](https://github.com/dmlc/mxnet-gtc-tutorial) |
| * [Embedding Torch layers and functions in MXNet](https://mxnet.incubator.apache.org/how_to/torch.html) |
| * [MXNet.js: Javascript Package for Deep Learning in Browser (without server) |
| ](https://github.com/dmlc/mxnet.js/) |
| * [Design Note: Design Efficient Deep Learning Data Loading Module](https://mxnet.incubator.apache.org/architecture/note_data_loading.html) |
| * [MXNet on Mobile Device](https://mxnet.incubator.apache.org/how_to/smart_device.html) |
| * [Distributed Training](https://mxnet.incubator.apache.org/how_to/multi_devices.html) |
| * [Guide to Creating New Operators (Layers)](https://mxnet.incubator.apache.org/how_to/new_op.html) |
| * [Go binding for inference](https://github.com/songtianyi/go-mxnet-predictor) |
| * [Amalgamation and Go Binding for Predictors](https://github.com/jdeng/gomxnet/) - Outdated |
| * [Large Scale Image Classification](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification) |
| |
| Contents |
| -------- |
| * [Documentation](https://mxnet.incubator.apache.org/) and [Tutorials](https://mxnet.incubator.apache.org/tutorials/) |
| * [Design Notes](https://mxnet.incubator.apache.org/architecture/index.html) |
| * [Code Examples](https://github.com/dmlc/mxnet/tree/master/example) |
| * [Installation](https://mxnet.incubator.apache.org/get_started/install.html) |
| * [Pretrained Models](https://github.com/dmlc/mxnet-model-gallery) |
| * [Contribute to MXNet](https://mxnet.incubator.apache.org/community/contribute.html) |
| * [Frequent Asked Questions](https://mxnet.incubator.apache.org/how_to/faq.html) |
| |
| Features |
| -------- |
| * Design notes providing useful insights that can re-used by other DL projects |
| * Flexible configuration for arbitrary computation graph |
| * Mix and match imperative and symbolic programming to maximize flexibility and efficiency |
| * Lightweight, memory efficient and portable to smart devices |
| * Scales up to multi GPUs and distributed setting with auto parallelism |
| * Support for Python, R, Scala, C++ and Julia |
| * Cloud-friendly and directly compatible with S3, HDFS, and Azure |
| |
| Ask Questions |
| ------------- |
| * Please use [mxnet/issues](https://github.com/dmlc/mxnet/issues) for how to use mxnet and reporting bugs |
| |
| License |
| ------- |
| Licensed under an [Apache-2.0](https://github.com/dmlc/mxnet/blob/master/LICENSE) license. |
| |
| Reference Paper |
| --------------- |
| |
| Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, |
| Bing Xu, Chiyuan Zhang, and Zheng Zhang. |
| [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems](https://github.com/dmlc/web-data/raw/master/mxnet/paper/mxnet-learningsys.pdf). |
| In Neural Information Processing Systems, Workshop on Machine Learning Systems, 2015 |
| |
| History |
| ------- |
| MXNet emerged from a collaboration by the authors of [cxxnet](https://github.com/dmlc/cxxnet), [minerva](https://github.com/dmlc/minerva), and [purine2](https://github.com/purine/purine2). The project reflects what we have learned from the past projects. MXNet combines aspects of each of these projects to achieve flexibility, speed, and memory efficiency. |
