mxnet

v1.9.1

Apache MXNet is an ultra-scalable deep learning framework. This version uses openblas and MKLDNN. For more information about how to use this package see README

Latest version published 3 years ago

License: Apache-2.0

PyPI

GitHub

Package Health Score

61 / 100

security
Security review needed
popularity
Influential project
maintenance
Inactive
community
Active

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Popularity

Influential project

GitHub Stars: 20.8K
Forks: 6.76K
Contributors: 370

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package mxnet, we found that it has been starred 20,802 times.

Security

Security review needed

Security and license risk for latest version

Release Date: May 17, 2022
Direct Vulnerabilities: 0
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Indirect Vulnerabilities: 0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

Yes

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Maintenance

Inactive

archived

Commit Frequency

No Recent Commits

Open Issues: 1.8K
Open PR: 204
Last Release: 3 years ago
Last Commit: 2 years ago

Further analysis of the maintenance status of mxnet based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for mxnet is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Active

Readme: Yes
Contributing.md: No
Code of Conduct: Yes
Contributors: 370
Funding: No

A good and healthy external contribution signal for mxnet project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: ==3.5.*, ==3.6.*, ==3.7.*, ==3.8.*

Age: 8 years
Latest Release: 3 years ago
Dependencies: 3 Direct / 7 Total
Versions: 38
Maintainers: 3
Wheels: Linux, macOS

Not sure how to use mxnet?

To help you get started, we've collected the most common ways that mxnet is being used within popular public projects.

awslabs / autogluon / autogluon / task / object_detection / pipeline.py View on Github

lr_decay_epoch = list(range(args.lr_decay_period, args.epochs, args.lr_decay_period))
    else:
        lr_decay_epoch = [int(i) for i in args.lr_decay_epoch.split(',')]
    lr_decay_epoch = [e - args.warmup_epochs for e in lr_decay_epoch]
    num_batches = args.num_samples // args.batch_size
    lr_scheduler = LRSequential([
        LRScheduler('linear', base_lr=0, target_lr=args.lr,
                    nepochs=args.warmup_epochs, iters_per_epoch=num_batches),
        LRScheduler(args.lr_mode, base_lr=args.lr,
                    nepochs=args.epochs - args.warmup_epochs,
                    iters_per_epoch=num_batches,
                    step_epoch=lr_decay_epoch,
                    step_factor=args.lr_decay, power=2),
    ])

    trainer = gluon.Trainer(
        net.collect_params(), 'sgd',
        {'wd': args.wd, 'momentum': args.momentum, 'lr_scheduler': lr_scheduler},
        kvstore='local')

    # targets
    sigmoid_ce = gluon.loss.SigmoidBinaryCrossEntropyLoss(from_sigmoid=False)
    l1_loss = gluon.loss.L1Loss()

    # metrics
    obj_metrics = mx.metric.Loss('ObjLoss')
    center_metrics = mx.metric.Loss('BoxCenterLoss')
    scale_metrics = mx.metric.Loss('BoxScaleLoss')
    cls_metrics = mx.metric.Loss('ClassLoss')

    # set up logger
    logging.basicConfig()

View more ways to use mxnet

Apache MXNet (Incubating) Python Package

Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of deep learning programs together to maximize the efficiency and your productivity.

For feature requests on the PyPI package, suggestions, and issue reports, create an issue by clicking here. Prerequisites

This package supports Linux, Mac OSX, and Windows platforms. You may also want to check:

mxnet-cu112 with CUDA-11.2 support.
mxnet-cu110 with CUDA-11.0 support.
mxnet-cu102 with CUDA-10.2 support.
mxnet-cu101 with CUDA-10.1 support.
mxnet-cu100 with CUDA-10.0 support.
mxnet-native CPU variant without MKLDNN.

To use this package on Linux you need the libquadmath.so.0 shared library. On Debian based systems, including Ubuntu, run sudo apt install libquadmath0 to install the shared library. On RHEL based systems, including CentOS, run sudo yum install libquadmath to install the shared library. As libquadmath.so.0 is a GPL library and MXNet part of the Apache Software Foundation, MXNet must not redistribute libquadmath.so.0 as part of the Pypi package and users must manually install it.

To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check Installing MXNet for instructions on building from source.

Installation

To install, use:

pip install mxnet

mxnet dependencies

graphviz numpy requests

FAQs

What is mxnet?

Apache MXNet is an ultra-scalable deep learning framework. This version uses openblas and MKLDNN. Visit Snyk Advisor to see a full health score report for mxnet, including popularity, security, maintenance & community analysis.

Is mxnet popular?

The python package mxnet receives a total of 163,658 weekly downloads. As such, mxnet popularity was classified as an influential project. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is mxnet well maintained?

We found indications that mxnet is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is mxnet safe to use?

While scanning the latest version of mxnet, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 3 May-2025, at 19:08 (UTC).

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