Apache MXNet website screenshot

Apache MXNet

Apache MXNet is a retired deep learning framework (now in the Apache Attic) designed for both efficiency and flexibility. It provided a multi-language API for building and training deep neural networks with support for distributed training, the Gluon high-level API, and deployment on edge devices. MXNet supported Python, Scala, Java, C++, R, Julia, and Perl.

1 APIs 8 Features
AIDeep LearningMachine LearningNeural NetworksPythonRetired

APIs

Apache MXNet

MXNet provides APIs in Python, Scala, Java, C++, R, Julia, and Perl for deep learning model development, with the Gluon high-level API for imperative model building, Symbol/NDAr...

Pricing Plans

Rate Limits

Apache Mxnet Rate Limits

5 limits

RATE LIMITS

FinOps

Features

Hybrid Front-End

Seamlessly transitions between Gluon eager imperative mode and symbolic execution for research flexibility and production efficiency.

Distributed Training

Supports Parameter Server and Horovod for scalable distributed training across multiple GPUs and nodes.

Multi-Language Bindings

Native APIs in Python, Scala, Java, C++, R, Julia, Clojure, and Perl for broad developer accessibility.

Gluon High-Level API

Intuitive Gluon API for imperative model building with automatic differentiation and dynamic computation graphs.

NDArray API

NumPy-like array operations for GPU-accelerated numerical computing as the foundation of MXNet computations.

Symbol API

Symbolic computation graph API for efficient inference and production deployment.

Model Zoo

Pre-trained models for computer vision, NLP, and other tasks accessible via the Gluon model zoo.

Edge Deployment

Lightweight deployment support for edge devices and mobile platforms via TVM and ONNX export.

Use Cases

Computer Vision

Build and train image classification, object detection, and segmentation models using GluonCV toolkit.

Natural Language Processing

Develop NLP models for text classification, sentiment analysis, and language modeling using GluonNLP.

Time Series Forecasting

Build time series forecasting models using the GluonTS toolkit for probabilistic forecasting.

Distributed Deep Learning

Train large neural networks across multiple GPUs and nodes using Parameter Server or Horovod.

Research Prototyping

Rapid prototyping of novel deep learning architectures using the Gluon imperative API.

Integrations

GluonCV

Computer vision toolkit built on MXNet providing pre-trained models and training utilities for vision tasks.

GluonNLP

NLP toolkit built on MXNet with pre-trained language models and text processing utilities.

GluonTS

Time series modeling toolkit built on MXNet for probabilistic forecasting.

ONNX

ONNX model format support for importing and exporting models to/from other frameworks.

TVM

Apache TVM deep learning compiler for optimizing MXNet model deployment on diverse hardware targets.

Horovod

Horovod distributed training framework integration for efficient multi-GPU and multi-node training.

D2L.ai

Dive into Deep Learning interactive textbook using MXNet for teaching deep learning concepts.

Resources

🔗
LinkedIn
LinkedIn
🌐
Portal
Portal
👥
GitHubOrganization
GitHubOrganization
👥
GitHubRepository
GitHubRepository
🔗
Wiki
Wiki
🔗
IssueTracker
IssueTracker
🔗
MailingList
MailingList
📜
TermsOfService
TermsOfService

Sources

apis.yml Raw ↑
aid: apache-mxnet
name: Apache MXNet
description: Apache MXNet is a retired deep learning framework (now in the Apache Attic) designed for both efficiency and
  flexibility. It provided a multi-language API for building and training deep neural networks with support for distributed
  training, the Gluon high-level API, and deployment on edge devices. MXNet supported Python, Scala, Java, C++, R, Julia,
  and Perl.
type: Index
position: Consumer
access: 3rd-Party
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
tags:
- AI
- Deep Learning
- Machine Learning
- Neural Networks
- Python
- Retired
created: '2026-03-16'
modified: '2026-04-19'
url: https://raw.githubusercontent.com/api-evangelist/apache-mxnet/refs/heads/main/apis.yml
specificationVersion: '0.19'
apis:
- aid: apache-mxnet:apache-mxnet
  name: Apache MXNet
  description: MXNet provides APIs in Python, Scala, Java, C++, R, Julia, and Perl for deep learning model development, with
    the Gluon high-level API for imperative model building, Symbol/NDArray low-level APIs for efficient computation graphs,
    and distributed training via Parameter Server and Horovod. Final version is 1.9.1.
  humanURL: https://mxnet.apache.org/versions/1.9.1/api
  tags:
  - Deep Learning
  - Distributed Training
  - Gluon
  - Python
  properties:
  - type: Documentation
    url: https://mxnet.apache.org/versions/1.9.1/api
  - type: GettingStarted
    url: https://mxnet.apache.org/versions/1.9.1/get_started
  - type: SDKs
    url: https://pypi.org/project/mxnet/
    title: Python SDK (PyPI)
  - type: SDKs
    url: https://central.sonatype.com/artifact/org.apache.mxnet/mxnet-full_2.12
    title: Scala/Java SDK (Maven)
  - type: GitHubRepository
    url: https://github.com/apache/mxnet
common:
- type: LinkedIn
  url: https://www.linkedin.com/company/apache-mxnet
- type: Portal
  url: https://mxnet.apache.org/
- type: GitHubOrganization
  url: https://github.com/apache
- type: GitHubRepository
  url: https://github.com/apache/mxnet
- type: Wiki
  url: https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home
- type: IssueTracker
  url: https://issues.apache.org/jira/projects/MXNET/issues
- type: MailingList
  url: mailto:dev@mxnet.apache.org
- type: TermsOfService
  url: https://www.apache.org/licenses/LICENSE-2.0
- type: Features
  data:
  - name: Hybrid Front-End
    description: Seamlessly transitions between Gluon eager imperative mode and symbolic execution for research flexibility
      and production efficiency.
  - name: Distributed Training
    description: Supports Parameter Server and Horovod for scalable distributed training across multiple GPUs and nodes.
  - name: Multi-Language Bindings
    description: Native APIs in Python, Scala, Java, C++, R, Julia, Clojure, and Perl for broad developer accessibility.
  - name: Gluon High-Level API
    description: Intuitive Gluon API for imperative model building with automatic differentiation and dynamic computation
      graphs.
  - name: NDArray API
    description: NumPy-like array operations for GPU-accelerated numerical computing as the foundation of MXNet computations.
  - name: Symbol API
    description: Symbolic computation graph API for efficient inference and production deployment.
  - name: Model Zoo
    description: Pre-trained models for computer vision, NLP, and other tasks accessible via the Gluon model zoo.
  - name: Edge Deployment
    description: Lightweight deployment support for edge devices and mobile platforms via TVM and ONNX export.
- type: UseCases
  data:
  - name: Computer Vision
    description: Build and train image classification, object detection, and segmentation models using GluonCV toolkit.
  - name: Natural Language Processing
    description: Develop NLP models for text classification, sentiment analysis, and language modeling using GluonNLP.
  - name: Time Series Forecasting
    description: Build time series forecasting models using the GluonTS toolkit for probabilistic forecasting.
  - name: Distributed Deep Learning
    description: Train large neural networks across multiple GPUs and nodes using Parameter Server or Horovod.
  - name: Research Prototyping
    description: Rapid prototyping of novel deep learning architectures using the Gluon imperative API.
- type: Integrations
  data:
  - name: GluonCV
    description: Computer vision toolkit built on MXNet providing pre-trained models and training utilities for vision tasks.
  - name: GluonNLP
    description: NLP toolkit built on MXNet with pre-trained language models and text processing utilities.
  - name: GluonTS
    description: Time series modeling toolkit built on MXNet for probabilistic forecasting.
  - name: ONNX
    description: ONNX model format support for importing and exporting models to/from other frameworks.
  - name: TVM
    description: Apache TVM deep learning compiler for optimizing MXNet model deployment on diverse hardware targets.
  - name: Horovod
    description: Horovod distributed training framework integration for efficient multi-GPU and multi-node training.
  - name: D2L.ai
    description: Dive into Deep Learning interactive textbook using MXNet for teaching deep learning concepts.
maintainers:
- FN: Kin Lane
  email: info@apievangelist.com