Apache MXNet is a modern open-source deep learning framework used to train, and deploy deep neural networks. It allows users to mix symbolic and imperative programming to maximize efficiency and productivity.
The software is scalable, allowing for fast model training, and supports a flexible programming model and multiple languages (C++, Python, Julia, Matlab, JavaScript, Go, R, Scala, Perl, and Wolfram Language).
MXNet is also a collection of blueprints and guidelines for building deep learning systems, and interesting insights of deep learning systems.
Key Features
- Supports state of the art in deep learning models, including convolutional neural networks (CNNs) and long short-term memory networks (LSTMs).
- Designed to be distributed on dynamic cloud infrastructure, using distributed parameter server, and can achieve almost linear scale with multiple GPU/CPU.
- Supports both imperative and symbolic programming.
- Supports an efficient deployment of a trained model to low-end devices for inference, such as mobile device.
- Design notes providing useful insights that can re-used by other deep learning projects.
- Flexible configuration for arbitrary computation graph.
- Mix and match imperative and symbolic programming to maximize flexibility and efficiency.
- APIs:
- NDArray API – provides imperative tensor operations on CPU/GPU. An NDArray represents a multi-dimensional, fixed-size homogeneous array. NDArray supports advanced indexing (both slice and assign).
- Symbol API – provides neural network graphs and auto-differentiation.
- Module API – provides an intermediate and high-level interface for performing computation with a Symbol. It lets us train the network and predict results.
- and others.
- Lightweight, memory efficient and portable to smart devices.
- Cloud-friendly and directly compatible with S3, HDFS, and Azure.
- Speed up multi-GPU and distributed training by compressing communication of gradients.
- Supports the NVIDIA Collective Communication Library.
- Support for C++, Python, Julia, Matlab, JavaScript, Go, R, Scala, Perl, Wolfram Language.
Website: mxnet.apache.org
Support: FAQ, GitHub Code Repository
Developer: Apache Software Foundation
License: Apache License 2.0
Related Software
| Deep Learning with Python | |
|---|---|
| TensorFlow | A very popular Deep Learning framework |
| PyTorch | Tensors and Dynamic neural networks in Python |
| Keras | High-level neural networks API |
| fastai | Simplifies training fast and accurate neural networks |
| tinygrad | Compact, lightweight deep learning framework |
| PyTorch Lightning | Framework for scaling PyTorch training workflows |
| JAX | High-performance numerical computing and ML library |
| PyTensor | Library for fast numerical computation |
| MindSpore | Framework for training and deploying neural networks |
| Elephas | Distributed deep learning with Keras and Spark |
| Chainer | Powerful, flexible, and intuitive framework for neural networks |
| Equinox | Neural network library for building flexible models with JAX |
| Caffe | Convolutional Architecture for Fast Feature Embedding |
| TFlearn | Deep learning library featuring a higher-level API for TensorFlow |
| MXNet | Flexible and efficient library |
| CNTK | Distributed deep learning |
| Neupy | Python library for neural networks and deep learning |
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