Package Health Score

91 / 100

security
No known security issues
popularity
Key ecosystem project
maintenance
Sustainable
community
Active

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Popularity

Key ecosystem project

GitHub Stars: 33.71K
Forks: 5.73K
Contributors: 420

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package ray, we found that it has been starred 33,711 times.

Security

No known security issues

Security and license risk for latest version

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

Sustainable

Commit Frequency

Open Issues: 3.6K
Open PR: 577
Last Release: 9 days ago
Last Commit: 20 days ago

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

We found that ray demonstrates a positive version release cadence with at least one new version released in the past 3 months.

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: No
Contributors: 420
Funding: No

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

We found a way for you to contribute to the project! Looks like ray is missing a Code of Conduct.

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Package

Python Versions Compatibility: >=3.9

Age: 7 years
Latest Release: 9 days ago
Dependencies: 7 Direct
Versions: 117
Maintainers: 26
Wheels: Linux, macOS, Windows

Not sure how to use ray?

To help you get started, we've collected the most common ways that ray is being used within popular public projects.

flow-project / flow / tests / slow_tests / test_sac.py View on Github

'hidden_layer_sizes': (256, 256),
            'squash': True,
        }
    },

    sampler_params={
        'type': 'SimpleSampler',
        'kwargs': {
            'max_path_length': HORIZON,
            'min_pool_size': HORIZON,
            'batch_size': 256,
        }
    },

    run_params={
        'seed': tune.sample_from(
            lambda spec: np.random.randint(0, 10000)),
        'checkpoint_at_end': True,
        'checkpoint_frequency': HORIZON // N_CHECKPOINTS,
        'checkpoint_replay_pool': False,
    },

    resources_per_trial={
        'cpu': N_CPUS,
        'gpu': N_GPUS,
        'extra_cpu': 0,
        'extra_gpu': 0,
    }
)


class TestSoftActorCritic(unittest.TestCase):

View more ways to use ray

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute:

Learn more about Ray AI Libraries:

Data: Scalable Datasets for ML
Train: Distributed Training
Tune: Scalable Hyperparameter Tuning
RLlib: Scalable Reinforcement Learning
Serve: Scalable and Programmable Serving

Or more about Ray Core and its key abstractions:

Tasks: Stateless functions executed in the cluster.
Actors: Stateful worker processes created in the cluster.
Objects: Immutable values accessible across the cluster.

Learn more about Monitoring and Debugging:

Monitor Ray apps and clusters with the Ray Dashboard.
Debug Ray apps with the Ray Distributed Debugger.

Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations.

Install Ray with: pip install ray. For nightly wheels, see the Installation page.

Why Ray?

Today's ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands.

Ray is a unified way to scale Python and AI applications from a laptop to a cluster.

With Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. If your application is written in Python, you can scale it with Ray, no other infrastructure required.

More Information

Older documents:

Getting Involved

Platform	Purpose	Estimated Response Time	Support Level
Discourse Forum	For discussions about development and questions about usage.	< 1 day	Community
GitHub Issues	For reporting bugs and filing feature requests.	< 2 days	Ray OSS Team
Slack	For collaborating with other Ray users.	< 2 days	Community
StackOverflow	For asking questions about how to use Ray.	3-5 days	Community
Meetup Group	For learning about Ray projects and best practices.	Monthly	Ray DevRel
Twitter	For staying up-to-date on new features.	Daily	Ray DevRel

ray dependencies

aiosignal filelock frozenlist jsonschema packaging pyyaml requests

FAQs

What is ray?

Ray provides a simple, universal API for building distributed applications. Visit Snyk Advisor to see a full health score report for ray, including popularity, security, maintenance & community analysis.

Is ray popular?

The python package ray receives a total of 1,455,318 weekly downloads. As such, ray popularity was classified as a key ecosystem project. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is ray well maintained?

We found indications that ray maintenance is sustainable demonstrating some project activity. We saw a total of 420 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is ray safe to use?

The python package ray 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 21 November-2024, at 12:31 (UTC).

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ray

Ray provides a simple, universal API for building distributed applications. For more information about how to use this package see README

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Popularity

Direct Usage Popularity

Security

Security and license risk for latest version

Maintenance

Commit Frequency

Community

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Package

Not sure how to use ray?

Why Ray?

More Information

Getting Involved

ray dependencies

FAQs

What is ray?

Is ray popular?

Is ray well maintained?

Is ray safe to use?

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Select a recommended open source package

Scan your app for vulnerabilities

Fix identified vulnerabilities

Monitor for new issues

Package Health Score

Explore Similar Packages

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

Not sure how to use ray?

Why Ray?

More Information

Getting Involved

ray dependencies

FAQs

What is ray?

Is ray popular?

Is ray well maintained?

Is ray 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