Add ecommerce_end_to_end template#703
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End-to-end demo: Ray Data preprocessing → Ray Train embedding fine-tuning → Ray Data batch embedding → Ray Serve multi-model recommendation API with Streamlit UI. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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/test-template ecommerce_end_to_end |
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added the technically don't need compute configs for aws and gcp, just want to run on azure k8s using declarative compute config |
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| # E-Commerce Recommendation System Demo | |||
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When someone click on the template's card in the console, our frontend automatically display the README.md. i'd suggest generating it from the notebook.ipynb for better UX (people can see directly what they would be running)
steps:
- remove current README.md
- run
jupyter nbconvert ...to convert notebook.ipynb to README.md - rename notebook.ipynb to README.ipynb
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| head_node_type: | |||
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do we need an azure.yaml compute config?
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We could use declarative compute config since it is k8s stack on aks
…On Wed, May 20, 2026 at 2:09 AM Elliot Barnwell ***@***.***> wrote:
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In configs/ecommerce_end_to_end/gce.yaml
<#703 (comment)>:
> @@ -0,0 +1,7 @@
+head_node_type:
do we need an azure.yaml compute config?
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The issue is on product/frontend side, I'm not sure it consumes any azure compute config yet. I see @cristianjd authored https://github.com/anyscale/product/pull/39857 so i'd suggest to wait for his input Meanwhile, you can proceed with the publishing but Azure people would have to configure the compute manually, which is very bad UX i agree |
Summary
ecommerce_end_to_endtemplate: an end-to-end e-commerce recommendation system demo covering Ray Data → Ray Train → Ray Data (batch embed) → Ray ServeTest plan
/test-templateon this PR to run CItmpl-publishpipeline🤖 Generated with Claude Code