Chapter 4. Developer Preview features
This section describes Developer Preview features in Red Hat OpenShift AI 2-latest. Developer Preview features are not supported by Red Hat in any way and are not functionally complete or production-ready. Do not use Developer Preview features for production or business-critical workloads. Developer Preview features provide early access to functionality in advance of possible inclusion in a Red Hat product offering. Customers can use these features to test functionality and provide feedback during the development process. Developer Preview features might not have any documentation, are subject to change or removal at any time, and have received limited testing. Red Hat might provide ways to submit feedback on Developer Preview features without an associated SLA.
For more information about the support scope of Red Hat Developer Preview features, see Developer Preview Support Scope.
- Support for AppWrapper in Kueue
- AppWrapper support in Kueue is available as a Developer Preview feature. The experimental API enables the use of AppWrapper-based workloads with the distributed workloads feature.
- ROCm-compatible Ray cluster image
-
An additional Ray cluster image
quay.io/modh/ray:2.35.0-py39-rocm61is available as Developer Preview software. This image is compatible with AMD accelerators that are supported by ROCm 6.1. This image is an AMD64 image, which might not work on other architectures. - Distributed InstructLab training
Distributed InstructLab training is available as a Developer Preview feature, enabling enhanced performance for training tasks on distributed environments compared to single-node setups. This feature improves the training efficiency and scalability of InstructLab, allowing users to leverage distributed resources for more effective AI model development.
Key features:
- Data transfer support: Facilitates the movement of synthetically generated data from InstructLab on Red Hat Enterprise Linux AI to S3-compatible storage for efficient access by distributed training tasks.
- Distributed training execution: Enables orchestration of multi-node InstructLab training jobs, leveraging the performance benefits of distributed infrastructure.
- End-to-end documentation: Comprehensive guidance for users to implement the full InstructLab training flow, including data preparation, transfer, and distributed execution. To access the documentation, see Distributed InstructLab Training on RHOAI.
