optimum-deepsparse

v0.1.0.dev1

Optimum DeepSparse is an extension of the Hugging Face Transformers library that integrates the DeepSparse inference runtime. DeepSparse offers GPU-class performance on CPUs, making it possible to run Transformers and other deep learning models on commodity hardware with sparsity. Optimum DeepSparse provides a framework for developers to easily integrate DeepSparse into their applications, regardless of the hardware platform. For more information about how to use this package see README

Latest version published 12 months ago

License: Apache-2.0

PyPI

GitHub

Package Health Score

72 / 100

security
No known security issues
popularity
Small
maintenance
Inactive
community
Active

Popularity

Small

GitHub Stars: 3K
Forks: 173
Contributors: 40

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package optimum-deepsparse, we found that it has been starred 2,998 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Oct 26, 2023
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

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Maintenance

Inactive

Commit Frequency

Open Issues: 11
Open PR: 21
Last Release: 12 months ago
Last Commit: 3 months ago

Further analysis of the maintenance status of optimum-deepsparse 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 optimum-deepsparse 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: 40
Funding: No

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

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Package

Python Versions Compatibility: >=3.8, <3.11

Age: 1 year
Latest Release: 12 months ago
Dependencies: 2 Direct
Versions: 2
Maintainers: 1
Wheels: OS Independent

Readme

optimum-deepsparse

Accelerated inference of 🤗 models on CPUs using the DeepSparse Inference Runtime.

Install

Optimum DeepSparse is a fast-moving project, and you may want to install from source.

pip install git+https://github.com/neuralmagic/optimum-deepsparse.git

Installing in developer mode

If you are working on the optimum-deepsparse code then you should use an editable install by cloning and installing optimum and optimum-deepsparse:

git clone https://github.com/huggingface/optimum
git clone https://github.com/neuralmagic/optimum-deepsparse
pip install -e optimum -e optimum-deepsparse

Now whenever you change the code, you'll be able to run with those changes instantly.

How to use it?

To load a model and run inference with DeepSparse, you can just replace your AutoModelForXxx class with the corresponding DeepSparseModelForXxx class.

import requests
from PIL import Image

- from transformers import AutoModelForImageClassification
+ from optimum.deepsparse import DeepSparseModelForImageClassification
from transformers import AutoFeatureExtractor, pipeline

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

model_id = "microsoft/resnet-50"
- model = AutoModelForImageClassification.from_pretrained(model_id)
+ model = DeepSparseModelForImageClassification.from_pretrained(model_id, export=True, input_shapes="[1,3,224,224]")
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
cls_pipe = pipeline("image-classification", model=model, feature_extractor=feature_extractor)
outputs = cls_pipe(image)

Supported Task	Model Class
"image-classification"	DeepSparseModelForImageClassification
"text-classification"/"sentiment-analysis"	DeepSparseModelForSequenceClassification
"audio-classification"	DeepSparseModelForAudioClassification
"question-answering"	DeepSparseModelForQuestionAnswering
"image-segmentation"	DeepSparseModelForSemanticSegmentation

If you find any issue while using those, please open an issue or a pull request.

optimum-deepsparse dependencies

deepsparse-nightly diffusers

FAQs

What is optimum-deepsparse?

Optimum DeepSparse is an extension of the Hugging Face Transformers library that integrates the DeepSparse inference ru... Visit Snyk Advisor to see a full health score report for optimum-deepsparse, including popularity, security, maintenance & community analysis.

Is optimum-deepsparse popular?

The python package optimum-deepsparse receives a total of 47 weekly downloads. As such, optimum-deepsparse popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is optimum-deepsparse well maintained?

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

Is optimum-deepsparse safe to use?

The python package optimum-deepsparse 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 20 October-2024, at 18:56 (UTC).

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Readme

optimum-deepsparse

Install

Installing in developer mode

How to use it?

optimum-deepsparse dependencies

FAQs

What is optimum-deepsparse?

Is optimum-deepsparse popular?

Is optimum-deepsparse well maintained?

Is optimum-deepsparse safe to use?

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Scan your app for vulnerabilities

Source Code

Open Source

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