torch-workflow-archiver-nightly

v2024.12.12

Torch Workflow Archiver is used for creating archives of workflow designed using trained neural net models that can be consumed by TorchServe inference For more information about how to use this package see README

Latest version published 10 days ago

License: Apache-2.0

PyPI

GitHub

Package Health Score

86 / 100

security
No known security issues
popularity
Popular
maintenance
Healthy
community
Active

Popularity

Popular

GitHub Stars: 4.18K
Forks: 852
Contributors: 180

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package torch-workflow-archiver-nightly, we found that it has been starred 4,178 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Dec 12, 2024
Direct Vulnerabilities: 0
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0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
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License Risk: 0
H
0
M
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

Healthy

Commit Frequency

Open Issues: 364
Open PR: 41
Last Release: 10 days ago
Last Commit: 3 months ago

Further analysis of the maintenance status of torch-workflow-archiver-nightly based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Healthy.

We found that torch-workflow-archiver-nightly demonstrates a positive version release cadence with at least one new version released in the past 3 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: 180
Funding: No

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

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Package

Python Versions Compatibility: Unspecified

Age: 3 years
Latest Release: 10 days ago
Dependencies: N/A
Versions: 636
Maintainers: 0
Wheels: OS Independent

Readme

Project Description

Torch Workflow Archiver is a tool used for creating archives of workflow designed using trained neural net models that can be consumed by TorchServe inference.

Use the Torch Workflow Archiver CLI to create a .war file.

Torch Workflow Archiver is part of TorchServe. However, you can install Torch Workflow Archiver stand alone.

Detailed documentation and examples are provided in the README.

Installation

pip install torch-workflow-archiver

Source code

You can check the latest source code as follows:

git clone https://github.com/pytorch/serve.git

Citation

If you use TorchServe in a publication or project, please cite TorchServe:

FAQs

What is torch-workflow-archiver-nightly?

Torch Workflow Archiver is used for creating archives of workflow designed using trained neural net models that can be ... Visit Snyk Advisor to see a full health score report for torch-workflow-archiver-nightly, including popularity, security, maintenance & community analysis.

Is torch-workflow-archiver-nightly popular?

The python package torch-workflow-archiver-nightly receives a total of 10,611 weekly downloads. As such, torch-workflow-archiver-nightly popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is torch-workflow-archiver-nightly well maintained?

We found that torch-workflow-archiver-nightly demonstrated a healthy version release cadence and project activity. It has a community of 180 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is torch-workflow-archiver-nightly safe to use?

The python package torch-workflow-archiver-nightly was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 12 December-2024, at 12:43 (UTC).

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