View mmdeploy on Snyk Open Source Advisor

Package Health Score

72 / 100

security
Security review needed
popularity
Recognized
maintenance
Inactive
community
Active

Popularity

Recognized

GitHub Stars: 2.79K
Forks: 637
Contributors: 110

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package mmdeploy, we found that it has been starred 2,787 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Dec 25, 2023
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

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Maintenance

Inactive

Commit Frequency

Open Issues: 364
Open PR: 36
Last Release: 12 months ago
Last Commit: 3 months ago

Further analysis of the maintenance status of mmdeploy 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 mmdeploy 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.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 110
Funding: No

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

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Package

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

Age: 3 years
Latest Release: 12 months ago
Dependencies: 11 Direct / 34 Total
Versions: 7
Maintainers: 0
Wheels: Linux, Windows

Readme

OpenMMLab website ^HOT OpenMMLab platform ^{TRY IT OUT}

English | 简体中文

Highlights

The MMDeploy 1.x has been released, which is adapted to upstream codebases from OpenMMLab 2.0. Please align the version when using it. The default branch has been switched to main from master. MMDeploy 0.x (master) will be deprecated and new features will only be added to MMDeploy 1.x (main) in future.

mmdeploy	mmengine	mmcv	mmdet	others
0.x.y	-	<=1.x.y	<=2.x.y	0.x.y
1.x.y	0.x.y	2.x.y	3.x.y	1.x.y

deploee offers over 2,300 AI models in ONNX, NCNN, TRT and OpenVINO formats. Featuring a built-in list of real hardware devices, deploee enables users to convert Torch models into any target inference format for profiling purposes.

Introduction

MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project.

Main features

Fully support OpenMMLab models

The currently supported codebases and models are as follows, and more will be included in the future

Multiple inference backends are available

The supported Device-Platform-InferenceBackend matrix is presented as following, and more will be compatible.

The benchmark can be found from here

Device / Platform	Linux	Windows	macOS	Android
x86_64 CPU	_onnxruntime _pplnn _ncnn _LibTorch _OpenVINO _TVM	_onnxruntime _OpenVINO _ncnn	-	-
ARM CPU	_ncnn	-	-	_ncnn
RISC-V	_ncnn	-	-	-
NVIDIA GPU	_onnxruntime _TensorRT _LibTorch _pplnn	_onnxruntime _TensorRT	-	-
NVIDIA Jetson	_TensorRT	-	-	-
Huawei ascend310	_CANN	-	-	-
Rockchip	_RKNN	-	-	-
Apple M1	-	-	_CoreML	-
Adreno GPU	-	-	-	_SNPE _ncnn
Hexagon DSP	-	-	-	_SNPE

Efficient and scalable C/C++ SDK Framework

All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on

Documentation

Please read getting_started for the basic usage of MMDeploy. We also provide tutoials about:

Benchmark and Model zoo

You can find the supported models from here and their performance in the benchmark.

Contributing

We appreciate all contributions to MMDeploy. Please refer to CONTRIBUTING.md for the contributing guideline.

Acknowledgement

We would like to sincerely thank the following teams for their contributions to MMDeploy:

Citation

If you find this project useful in your research, please consider citing:

@misc{=mmdeploy,
    title={OpenMMLab's Model Deployment Toolbox.},
    author={MMDeploy Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmdeploy}},
    year={2021}
}

License

This project is released under the Apache 2.0 license.

Projects in OpenMMLab

MMEngine: OpenMMLab foundational library for training deep learning models.
MMCV: OpenMMLab foundational library for computer vision.
MMPretrain: OpenMMLab pre-training toolbox and benchmark.
MMagic: OpenMMLab Advanced, Generative and Intelligent Creation toolbox.
MMDetection: OpenMMLab detection toolbox and benchmark.
MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
MMYOLO: OpenMMLab YOLO series toolbox and benchmark
MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
MMTracking: OpenMMLab video perception toolbox and benchmark.
MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
MMPose: OpenMMLab pose estimation toolbox and benchmark.
MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
MMFlow: OpenMMLab optical flow toolbox and benchmark.
MMDeploy: OpenMMLab model deployment framework.
MMRazor: OpenMMLab model compression toolbox and benchmark.
MIM: MIM installs OpenMMLab packages.
Playground: A central hub for gathering and showcasing amazing projects built upon OpenMMLab.

mmdeploy dependencies

aenum grpcio matplotlib mmengine multiprocess numpy onnx prettytable protobuf six terminaltables

FAQs

What is mmdeploy?

OpenMMLab Model Deployment. Visit Snyk Advisor to see a full health score report for mmdeploy, including popularity, security, maintenance & community analysis.

Is mmdeploy popular?

The python package mmdeploy receives a total of 3,210 weekly downloads. As such, mmdeploy popularity was classified as a recognized. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is mmdeploy well maintained?

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

Is mmdeploy safe to use?

While scanning the latest version of mmdeploy, 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 19 December-2024, at 23:51 (UTC).

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Package Health Score

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Commit Frequency

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Readme

Highlights

Introduction

Main features

Fully support OpenMMLab models

Multiple inference backends are available

Efficient and scalable C/C++ SDK Framework

Documentation

Benchmark and Model zoo

Contributing

Acknowledgement

Citation

License

Projects in OpenMMLab

mmdeploy dependencies

FAQs

What is mmdeploy?

Is mmdeploy popular?

Is mmdeploy well maintained?

Is mmdeploy safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

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