iter8-kfserving | driven live experiments , progressive delivery

by iter8-tools Shell Version: v0.1.0-alpha License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | iter8-kfserving Summary

iter8-kfserving is a Shell library. iter8-kfserving has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Iter8-kfserving is now fully absorbed as part of the larger Iter8 project. To get started with Iter8 for KFServing, please visit

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Quality

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License

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Support

iter8-kfserving has a low active ecosystem.

It has 20 star(s) with 4 fork(s). There are 5 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 12 have been closed. On average issues are closed in 28 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of iter8-kfserving is v0.1.0-alpha

Quality

iter8-kfserving has no bugs reported.

Security

iter8-kfserving has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

iter8-kfserving is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

iter8-kfserving releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

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iter8-kfserving Key Features

No Key Features are available at this moment for iter8-kfserving.

iter8-kfserving Examples and Code Snippets

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Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install iter8-kfserving

Steps 1 to 7 demonstrate metrics-driven progressive canary release of a KFServing model using iter8-kfserving. This demo uses KFServing v0.5.0-rc2. Before you begin, you will need Minikube, Kustomize v3, and Go 1.13+. Step 1: Start Minikube with sufficient resources. Step 2: Install KFServing, kfserving-monitoring, and iter8-kfserving. Step 3: In a separate terminal, setup Minikube tunnel. If prompted, enter password. Step 4: Create a KFServing v1beta1 inferenceservice with a default model. Update it with a canary model. This step may take a couple of minutes. Step 5: In a separate terminal, generate prediction requests for the inferenceservice. Step 6: Create the iter8-kfserving canary experiment. Step 7: In a separate terminal, periodically describe the experiment.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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