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…29.0.0 Point the LMI container at the patched vLLM v0.28.0 wheel for the DJL 0.37.0 / LMI 29.0.0 release. torch 2.11.0 -> 2.13.0 is required, not optional: vLLM 0.28.0 pins torch==2.13.0, and the wheel's compiled .so files are torch-ABI-locked, so the container runtime must move together with the wheel. torchaudio stays at 2.11.0 (newest released; the torch/torchaudio version numbers have decoupled this cycle) and resolves transitively. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Superseded by #3075 — same commit, reopened from an in-repo branch so CI runs without the fork-PR approval gate. Closing this one to keep the trail clean. |
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Point the LMI container at the patched vLLM v0.28.0 wheel (base tag
v0.28.02cf0a691+ 6 cherry-picked PRs: vllm-project/vllm#44675 vllm-project/vllm#48210 vllm-project/vllm#48363 vllm-project/vllm#48402 vllm-project/vllm#50040 vllm-project/vllm#51742) for the DJL 0.37.0 / LMI 29.0.0 release.Wheel: https://publish.djl.ai/vllm/vllm-0.28.0-cp312-cp312-linux_x86_64.whl
sha256
100e71ec575de176e717fb396f97a5e3cb207b41d5457117b8690ca2fa55a9b3, built py3.12 / CUDA 13.0 (cu130),TORCH_CUDA_ARCH_LIST=8.0;8.6;8.9;9.0;10.0;12.0.torch2.11.0 → 2.13.0 is required, not optional. vLLM 0.28.0 pinstorch==2.13.0(requirements/cuda.txt), and the wheel's compiled.sofiles are torch-ABI-locked — so the container runtime must move together with the wheel.torchvisionmoves 0.26.0 → 0.28.0 for the same reason. This is the one difference in shape from the LMI 28.0.0 bump, which was wheel-URL only.Two notes for reviewers:
torchaudiostays at 2.11.0 and is deliberately not pinned here. 2.11.0 is the newest released torchaudio — the torch/torchaudio version numbers have decoupled this cycle, sotorchaudio==2.13.0does not exist. This file pins neithertorchaudionortorchcodec, so both resolve transitively; all four (torch 2.13.0+cu130,torchvision 0.28.0+cu130,torchaudio 2.11.0+cu130,torchcodec 0.14+) are confirmed present on thecu130index.pyproject.toml[test]pin bump is not cosmetic. A stalevllm==pin there breakstest_vllm_embeddingwithmodule 'djl_python.lmi_vllm' has no attribute 'vllm_async_service'.--extra-index-url .../whl/cu130is unchanged, andlmi.Dockerfileneeds no change (alreadynvidia/cuda:13.0.2-devel-ubuntu24.04/cuda_version=cu130/python_version=3.12).Type of change
Checklist:
pytest tests.py -k "TestVllm1" -m "vllm"Feature/Issue validation/testing
Pre-merge verification completed against the built artifact (not predicted):
https://publish.djl.ai/vllm/vllm-0.28.0-cp312-cp312-linux_x86_64.whlreturns HTTP 200,content-length: 625092004. The object was read back out of S3 and re-hashed to100e71ec…, matching the build byte-for-byte.METADATA, not inferred:Requires-Dist: torch==2.13.0, torchaudio==2.11.0, torchvision==0.28.0, torchcodec>=0.14.lmi-container-requirements.txtand vLLM 0.28.0'srequirements/{common,cuda}.txtagree —torch/torchvisionnow match exactly,transformers >= 5.5.3andcompressed-tensors == 0.17.0were already byte-identical,huggingface-hubresolves. 0 conflicts.djl_python↔ vLLM 0.28.0 API compatibility check clean (static, AST). 35/35 distinctvllm.*import statements resolve (module found and imported symbol present), and 4/4 constructor call sites accept every kwarg passed against the real__init__signatures (OnlineRenderer,OpenAIServingChat,OpenAIServingCompletion,ServingEmbedding). No glue adaptation appears to be needed for this bump.cuobjdump:_C_stable_libtorch.abi3.soand_moe_C_stable_libtorch.abi3.soboth carry compiled SASS for sm_80, sm_86, sm_89, sm_90, sm_90a, sm_100 and sm_120.Full container integration testing runs on the nightly build that this merge enables.