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Add offline and online inference drivers with Dockerfile and Anyscale job configs for running SGLang on Ray.
…stness - Dockerfile: use sglang[all]==0.5.8 + sgl-kernel==0.3.21 instead of git fork - Drivers: add logging, named placement groups, exit codes, better error handling - Job configs: add NCCL_DEBUG, fix submit path comment - README: add How It Works, Troubleshooting, local run examples Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Rename sglang_ray_inference -> sglang_inference - Batch inference (job.yaml + driver_offline.py) fully working with multi-node TP=4, PP=2 using SGLang's use_ray=True mode - Ray Serve deployment (service.yaml + serve.py) uses same pattern as official Ray LLM SGLang integration with signal monkey-patching - Add query.py script for testing the service - Simplify configuration with environment variables The serving example is still being validated with multi-replica autoscaling. Single replica works; investigating occasional timeouts with multiple replicas. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> Signed-off-by: Robert Nishihara <rkn@anyscale.com>
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Add offline and online inference drivers with Dockerfile and Anyscale job configs for running SGLang on Ray.