View otxdeploy on Snyk Open Source Advisor

Package Health Score

64 / 100

security
Security review needed
popularity
Small
maintenance
Inactive
community
Active

Popularity

Small

GitHub Stars: 2.71K
Forks: 620
Contributors: 110

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package otxdeploy, we found that it has been starred 2,710 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Feb 5, 2024
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

We found a way for you to contribute to the project! Looks like otxdeploy is missing a security policy.

You can connect your project's repository to Snyk to stay up to date on security alerts and receive automatic fix pull requests.

Keep your project free of vulnerabilities with Snyk

Maintenance

Inactive

Commit Frequency

Open Issues: 345
Open PR: 35
Last Release: 10 months ago
Last Commit: 11 months ago

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

We found that otxdeploy demonstrates a positive version release cadence with at least one new version released in the past 12 months.

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: Yes
Code of Conduct: Yes
Contributors: 110
Funding: No

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

Embed Package Health Score Badge

Keep your project healthy

Check your requirements.txt

Ensure all the packages you're using are healthy and well-maintained

Snyk Vulnerability Scanner

Get health score & security insights directly in your IDE

Package

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

Age: 2 years
Latest Release: 10 months ago
Dependencies: 6 Direct / 17 Total
Versions: 3
Maintainers: 1
Wheels: OS Independent

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

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	-	-
ARM CPU	ncnn	-	-	ncnn
RISC-V	ncnn	-	-	-
NVIDIA GPU	ONNXRuntime TensorRT pplnn LibTorch TVM	ONNXRuntime TensorRT pplnn	-	-
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

MMCV: OpenMMLab foundational library for computer vision.
MIM: MIM installs OpenMMLab packages.
MMClassification: OpenMMLab image classification toolbox and benchmark.
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.
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.
MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
MMRazor: OpenMMLab model compression toolbox and benchmark.
MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
MMTracking: OpenMMLab video perception toolbox and benchmark.
MMFlow: OpenMMLab optical flow toolbox and benchmark.
MMEditing: OpenMMLab image and video editing toolbox.
MMGeneration: OpenMMLab image and video generative models toolbox.
MMDeploy: OpenMMLab model deployment framework.

otxdeploy dependencies

grpcio matplotlib multiprocess numpy six terminaltables

FAQs

What is otxdeploy?

(Temp fork for PyPI packaging) OpenMMLab Model Deployment. Visit Snyk Advisor to see a full health score report for otxdeploy, including popularity, security, maintenance & community analysis.

Is otxdeploy popular?

The python package otxdeploy receives a total of 75 weekly downloads. As such, otxdeploy popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is otxdeploy well maintained?

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

Is otxdeploy safe to use?

While scanning the latest version of otxdeploy, 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 26 November-2024, at 17:28 (UTC).

Build a secure application checklist

Select a recommended open source package

Minimize your risk by selecting secure & well maintained open source packages

DONE

Scan your app for vulnerabilities

Scan your application to find vulnerabilities in your: source code, open source dependencies, containers and configuration files

SCAN NOW

Fix identified vulnerabilities

Easily fix your code by leveraging automatically generated PRs

AUTO FIX

Monitor for new issues

New vulnerabilities are discovered every day. Get notified if your application is affected

MONITOR APP

Package Health Score

Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

Embed Package Health Score Badge

Keep your project healthy

Check your requirements.txt

Snyk Vulnerability Scanner

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

otxdeploy dependencies

FAQs

What is otxdeploy?

Is otxdeploy popular?

Is otxdeploy well maintained?

Is otxdeploy 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

Fix identified vulnerabilities

Monitor for new issues