Sold by: Software Sushi
Deployed on AWS
BentoML turns machine learning models into HTTP prediction APIs. This Ubuntu 24.04 AMI runs a private, token-protected BentoML prediction API on CPU with a bundled scikit-learn sample model, so data scientists and ML engineers can send a first prediction within minutes of launch and then serve their own models.
Overview
BentoML is an open source framework for packaging machine learning models as prediction services. This AMI gives data scientists and ML engineers a private CPU serving instance in their own AWS account for testing and running small model APIs, without setting up the serving stack themselves.
What you get: BentoML 1.4.39 serving a small, reviewed scikit-learn sample model on a token-protected HTTPS API (port 8448) with credentials created on first boot (nothing is baked into the image), the sushi-bentoml-api helper that reads the access file for you, and an editable example service you can start with sushi-bentoml-example to explore the interactive Swagger UI on local port 3000. The image does not call any external model provider.
Getting started: launch t3.large with at least 32 GiB of encrypted gp3 storage and IMDSv2, keep inbound application ports closed and connect with Session Manager. Run sudo -iu ubuntu bash /opt/sushi/docs/demo.sh to send a first prediction, then follow /opt/sushi/docs/README.md to replace the sample with your own model.
This is a repackaged open source software product wherein additional charges apply for independent AWS packaging, setup documentation and maintenance by Software Sushi. BentoML is published by the BentoML project under the Apache-2.0 license. Software Sushi is independent of the upstream project and does not claim upstream or AWS endorsement. EC2, EBS and data transfer are billed separately from software charges.
Highlights
- Serve machine learning models as an HTTP prediction API on CPU, in your own AWS account.
- Bundled sample model: send a first prediction within minutes, then explore the Swagger UI.
- Token-protected HTTPS API with per-instance credentials. Access through Session Manager with no inbound ports.
Details
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64-bit (x86) Amazon Machine Image (AMI)
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Operating system
Ubuntu 24.04
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
t3.large Recommended | $0.03 |
Vendor refund policy
We offer a full refund of software charges for the first 48 hours billing starts if the product does not perform as described. To request a refund, email info@softwaresushi.com with your EC2 instance ID and a description of the issue. AWS infrastructure costs (EC2, EBS, data transfer) and any third-party service charges are not eligible for refund by us.
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
BentoML 1.4.39 on Ubuntu 24.04 for t3.large. New in 0.1.3: an editable example service and the sushi-bentoml-example command, which serves it with BentoML's interactive API page (Swagger UI) on local port 3000; the built-in API on 8448 now serves its OpenAPI description at /docs.json with the token; plainer first-login banner and certificate guidance.
Additional details
Usage instructions
- Launch t3.large with at least 32 GiB encrypted gp3 root storage and at least 4 GiB RAM. Require IMDSv2. Leave inbound application ports closed.
- Connect with AWS Systems Manager Session Manager as ubuntu and wait for cloud-init. If you use SSH, limit it to an administrative address range you choose.
- sudo -iu ubuntu bash /opt/sushi/docs/demo.sh
- To see it in a browser: as ubuntu run sushi-bentoml-example, then from your computer open a Session Manager port-forwarding session to port 3000 and open local port 3000 in your browser (Swagger UI: pick an endpoint, Try it out, Execute).
- The built-in prediction API uses port 8448 with a per-instance token; the helper sushi-bentoml-api reads the access file for you. This image does not call a model provider. Documentation: https://docs.bentoml.com/ and /opt/sushi/docs/README.md on the instance.
Resources
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Support
Vendor support
Software Sushi provides installation and configuration guidance for this image. Contact info@softwaresushi.com with your product version and reproduction steps; never send keys or private configuration. Guaranteed response times require a separate agreement. This offer does not include upstream project support.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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