experimental-torch-directml

v0.1.13.dev23122022132839

A DirectML backend for hardware acceleration in PyTorch. For more information about how to use this package see README

Latest version published 2 years ago

License: MIT

PyPI

GitHub

Package Health Score

60 / 100

security
No known security issues
popularity
Small
maintenance
Inactive
community
Active

Popularity

Small

GitHub Stars: 2.28K
Forks: 301
Contributors: 50

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package experimental-torch-directml, we found that it has been starred 2,277 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Dec 23, 2022
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

MIT

Security Policy

Yes

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: 207
Open PR: 27
Last Release: 2 years ago
Last Commit: 2 months ago

Further analysis of the maintenance status of experimental-torch-directml 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 experimental-torch-directml 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: No
Code of Conduct: Yes
Contributors: 50
Funding: No

With more than 10 contributors for the experimental-torch-directml repository, this is possibly a sign for a growing and inviting community.

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: >=3.7

Age: 2 years
Latest Release: 2 years ago
Dependencies: 2 Direct
Versions: 19
Maintainers: 0
Wheels: Windows

Readme

PyTorch with DirectML

DirectML acceleration for PyTorch is currently available for Public Preview. PyTorch with DirectML enables training and inference of complex machine learning models on a wide range of DirectX 12-compatible hardware.

DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers, including all DirectX 12-capable GPUs from vendors such as AMD, Intel, NVIDIA, and Qualcomm.

More information about DirectML can be found in Introduction to DirectML.

PyTorch on DirectML is supported on both the latest versions of Windows 10 and the Windows Subsystem for Linux, and is available for download as a PyPI package. For more information about getting started, see GPU accelerated ML training (docs.microsoft.com)

Samples

Refer to the Pytorch with DirectML Samples Repo for samples.

Feedback

We look forward to hearing from you!

For TensorFlow with DirectML issues, bugs, and feedback; or for general DirectML issues and feedback, please file an issue or contact us directly at askdirectml@microsoft.com.
For PyTorch with DirectML issues, bugs, and feedback; or for general DirectML issues and feedback, please file an issue or contact us directly at askdirectml@microsoft.com.
For Windows ML issues, please file a GitHub issue at microsoft/Windows-Machine-Learning or contact us directly at askwindowsml@microsoft.com.
For ONNX Runtime issues, please file an issue at microsoft/onnxruntime.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Workflow

Developers will work on release branches daily and RI to main branch as needed.

main branch builds with the nightly-built pytorch conda install pytorch torchvision cpuonly -c pytorch-nightly
release branches build with the corresponding published pytorch
- both release/1.13 and release/1.13+ build with pytorch 1.13.
- coming release/1.13.1 should build with pytorch 1.13.1

Motivations why in this pattern.

Avoid pushing identical changes twice into release and main.
Avoid to be blocked by broken changes from pytorch master.

Examples of cutting branches:

A new branch for pytorch-1.13 (e.g. [release/1.13+2]) will be cut from the last released branch release/1.13+ for new releases, and developers will stay on the new branch daily until that release is published.
For new pytorch version 1.13.1, release/1.13.1 should be cut from main after all release workloads are RIed.

workflow

External Links

PyTorch homepage

experimental-torch-directml dependencies

torch torchvision

FAQs

What is experimental-torch-directml?

A DirectML backend for hardware acceleration in PyTorch. Visit Snyk Advisor to see a full health score report for experimental-torch-directml, including popularity, security, maintenance & community analysis.

Is experimental-torch-directml popular?

The python package experimental-torch-directml receives a total of 114 weekly downloads. As such, experimental-torch-directml popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is experimental-torch-directml well maintained?

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

Is experimental-torch-directml safe to use?

The python package experimental-torch-directml 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 23 December-2024, at 12:41 (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