mxnet

What it is and what it does

MXNet is a deep learning framework designed to balance efficiency and flexibility, allowing you to mix different programming styles and hardware targets in a single application. It provides symbolic and imperative programming interfaces, supports distributed training, and can run on CPUs, GPUs, and other accelerators. The package depends on numpy for numerical operations, requests for network communication, and graphviz for visualization.

The framework is mature and was widely used in production, but the repository is now archived and abandoned. The last release was in May 2022, over 1550 days ago. While the package itself may continue to work for existing projects, it receives no new features, bug fixes, or security updates. The codebase supports Python 3.5 through 3.8 according to its classifiers, and installation requires a system-level shared library on Linux.

Use it for:

• Training neural networks for image classification or computer vision tasks using GPU acceleration.
• Building distributed deep learning pipelines across multiple machines or GPUs for large-scale model training.
• Deploying pre-trained models for inference in production environments where MXNet is already established.
• Prototyping neural network architectures using symbolic computation graphs for research or experimentation.
• Running legacy MXNet code or migrating existing projects that were built on this framework.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

MXNet is a deep learning framework that enables you to build and train neural networks with support for multiple programming interfaces and hardware accelerators.

No, not for new projects. MXNet is abandoned—the repository is archived, the last release was in May 2022, and no security or maintenance updates are forthcoming. For new deep learning work, use an actively maintained framework. Install MXNet only if you are maintaining or migrating legacy code that already depends on it, and plan a migration path to a supported alternative.

Install

mxnet on PyPI

pip install mxnet

uv add mxnet

poetry add mxnet

Installing mxnet

Before you install

Medium install friction due to compiled dependencies. The package requires libquadmath.so.0 on Linux systems (installed separately via apt or yum), and pre-built wheels are available for macOS and Linux x86_64/aarch64. Maintenance status is abandoned—the repository was archived after the last commit on 2023-10-25, over 1550 days ago.

License in practice

Licensed under Apache 2.0 (permissive), allowing commercial and private use with minimal restrictions. You must include a copy of the license and note any modifications, but there are no copyleft obligations.

Quickstart

pip install mxnet

import mxnet as mx
import numpy as np

# Create a symbolic variable and layer
data = mx.sym.Variable('data')
fc = mx.sym.FullyConnected(data=data, num_hidden=10)

On Linux, libquadmath.so.0 must be installed separately: `sudo apt install libquadmath0` (Debian/Ubuntu) or `sudo yum install libquadmath` (RHEL/CentOS). Requires numpy and requests as runtime dependencies.

Verify before relying

• Whether the abandoned status and lack of recent updates pose compatibility risks with modern Python or dependency versions.
• Whether GPU-accelerated variants (mxnet-cu112, mxnet-cu110, etc.) remain functional or if CUDA support is also stalled.
• Current community adoption and whether alternative maintained frameworks are now preferred for new projects.

Package facts

Evidence: mxnet-1.9.1-py3-none-macosx_10_13_x86_64.whl; mxnet-1.9.1-py3-none-manylinux2014_aarch64.whl; mxnet-1.9.1-py3-none-manylinux2014_x86_64.whl

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