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.gitattributes

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+vllm-berag-2ca4f5b4-v0.24.0-cu129.sif filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+pretty_name: vLLM-BERAG Apptainer container
+tags:
+  - apptainer
+  - singularity
+  - vllm
+  - cuda
+  - berag
+  - inference
+---
+
+# vLLM-BERAG Apptainer container
+
+This repository distributes a prebuilt Apptainer SIF containing the
+vLLM-BERAG inference environment. It is intended for NVIDIA GPU jobs on HPC
+systems where reading a Python environment containing many small files from a
+shared filesystem is slow.
+
+The image combines the compiled CUDA extensions from
+`vllm/vllm-openai:v0.24.0-cu129` with the BERAG Python implementation from
+source revision `2ca4f5b4fe3aa8018ee5f6bd014a6684570642d9`.
+
+## Artifact
+
+| Property | Value |
+| --- | --- |
+| File | `vllm-berag-2ca4f5b4-v0.24.0-cu129.sif` |
+| Size | 11,181,588,480 bytes |
+| SHA-256 | `7ad083a42a9273547b643610165d71cdef6333e02a399d8754dc1caac109c8fd` |
+| vLLM | 0.24.0 with BERAG extensions |
+| PyTorch | 2.11.0+cu129 |
+| CUDA runtime | 12.9 |
+| Architecture | Linux x86-64 |
+
+Model weights, datasets, and LoRA adapters are not included.
+
+## Requirements
+
+- Apptainer with NVIDIA support.
+- An NVIDIA GPU and a host driver compatible with the CUDA 12.9 runtime.
+- Sufficient node-local storage for the approximately 11 GB SIF and runtime
+  caches.
+
+The image was tested with Apptainer 1.5.2 on an NVIDIA A100-SXM4-80GB.
+
+## Download
+
+Replace `YOUR_HF_USERNAME/vllm-berag-container` with this repository's Hub ID:
+
+```bash
+REPO_ID="YOUR_HF_USERNAME/vllm-berag-container"
+LOCAL_DIR="${SLURM_TMPDIR:-/var/tmp/$USER/vllm-berag-container}"
+
+mkdir -p "$LOCAL_DIR"
+
+hf download "$REPO_ID" \
+    vllm-berag-2ca4f5b4-v0.24.0-cu129.sif \
+    vllm-berag-2ca4f5b4-v0.24.0-cu129.sif.sha256 \
+    --repo-type dataset \
+    --local-dir "$LOCAL_DIR"
+
+cd "$LOCAL_DIR"
+sha256sum -c vllm-berag-2ca4f5b4-v0.24.0-cu129.sif.sha256
+```
+
+## GPU smoke test
+
+Use a node-local directory for JIT and framework caches. Bind an existing
+Hugging Face model cache to `/cache/huggingface` if model access is needed.
+
+```bash
+IMAGE="$LOCAL_DIR/vllm-berag-2ca4f5b4-v0.24.0-cu129.sif"
+RUNTIME_ROOT="${SLURM_TMPDIR:-/var/tmp/$USER/vllm-berag-runtime}"
+HF_CACHE_HOST="/path/to/your/HF_HOME"
+
+mkdir -p \
+    "$RUNTIME_ROOT/xdg" \
+    "$RUNTIME_ROOT/vllm" \
+    "$RUNTIME_ROOT/triton" \
+    "$RUNTIME_ROOT/torchinductor"
+
+env -u CUDA_HOME -u CUDA_PATH -u CUDA_ROOT \
+apptainer exec \
+    --nv \
+    --cleanenv \
+    --bind "$HF_CACHE_HOST:/cache/huggingface" \
+    --bind "$RUNTIME_ROOT:/runtime-cache" \
+    --env VLLM_USE_FLASHINFER_SAMPLER=0 \
+    --env XDG_CACHE_HOME=/runtime-cache/xdg \
+    --env VLLM_CACHE_ROOT=/runtime-cache/vllm \
+    --env FLASHINFER_WORKSPACE_BASE=/runtime-cache \
+    --env TRITON_CACHE_DIR=/runtime-cache/triton \
+    --env TORCHINDUCTOR_CACHE_DIR=/runtime-cache/torchinductor \
+    "$IMAGE" \
+    python3 -c '
+import inspect
+import torch
+import vllm
+import vllm._C_stable_libtorch
+
+print("vLLM:", vllm.__version__)
+print("CUDA:", torch.version.cuda)
+print("GPU:", torch.cuda.get_device_name(0))
+print("generate_berag:", inspect.signature(vllm.LLM.generate_berag))
+'
+```
+
+Successful output should report CUDA 12.9, the allocated GPU, and the
+`LLM.generate_berag` signature.
+
+## BERAG API
+
+The synchronous entry point is:
+
+```python
+LLM.generate_berag(
+    shared_prefix,
+    documents,
+    suffix,
+    sampling_params=None,
+    *,
+    berag_params=None,
+    request_id=None,
+    use_tqdm=True,
+    lora_request=None,
+    priority=None,
+    tokenization_kwargs=None,
+    debug=False,
+)
+```
+
+Per-request BERAG settings use `vllm.berag.BeragParams`, with
+`pruning_top_p` and optional `prior_token_indices` fields.
+
+## Operational notes
+
+- Do not load a host CUDA toolkit into the container. `apptainer exec --nv`
+  provides the host NVIDIA driver libraries; stale `CUDA_HOME` values can
+  point JIT compilation at a nonexistent toolkit.
+- `VLLM_USE_FLASHINFER_SAMPLER=0` avoids runtime FlashInfer sampler
+  compilation in this runtime-only image.
+- Keep model weights on shared storage if necessary, but stage the SIF and
+  runtime caches on node-local storage.
+- This image is intended for trusted offline or batch inference. Review vLLM
+  serving security guidance before exposing an API endpoint.
+
+## Provenance and licensing
+
+The SIF bundles software from multiple upstream projects, including vLLM,
+PyTorch, NVIDIA CUDA runtime libraries, and their transitive dependencies.
+Each component retains its own license and terms. Review the applicable
+upstream licenses and redistribution terms before changing this Hub repository
+from private to public.

vllm-berag-2ca4f5b4-v0.24.0-cu129.sif

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ad083a42a9273547b643610165d71cdef6333e02a399d8754dc1caac109c8fd
+size 11181588480

vllm-berag-2ca4f5b4-v0.24.0-cu129.sif.sha256

@@ -0,0 +1 @@
+7ad083a42a9273547b643610165d71cdef6333e02a399d8754dc1caac109c8fd  vllm-berag-2ca4f5b4-v0.24.0-cu129.sif

vllm-berag.def

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+Bootstrap: localimage
+From: ./vllm-v0.24.0-cu129.sif
+
+%labels
+    org.opencontainers.image.title vllm-berag
+    org.opencontainers.image.base.name vllm/vllm-openai:v0.24.0-cu129
+    org.opencontainers.image.revision 2ca4f5b4fe3aa8018ee5f6bd014a6684570642d9
+    org.opencontainers.image.source https://github.com/EriChen0615/vllm-berag
+
+%files
+    ./build/vllm-berag-src /opt/
+
+%post
+    set -eu
+
+    VLLM_PACKAGE_DIR=/usr/local/lib/python3.12/dist-packages/vllm
+    test -d "$VLLM_PACKAGE_DIR"
+    test -d /opt/vllm-berag-src/vllm
+
+    # Preserve the base image's compiled extensions while replacing and
+    # adding the Python files from the BERAG source snapshot.
+    cp -a /opt/vllm-berag-src/vllm/. "$VLLM_PACKAGE_DIR/"
+
+%environment
+    export HF_HOME=/cache/huggingface
+    export PYTHONUNBUFFERED=1
+    export VLLM_USAGE_SOURCE=production-sif-image
+
+%test
+    VLLM_PACKAGE_DIR=/usr/local/lib/python3.12/dist-packages/vllm
+    test -f "$VLLM_PACKAGE_DIR/berag.py"
+    grep -q 'def generate_berag' "$VLLM_PACKAGE_DIR/entrypoints/llm.py"
+    find "$VLLM_PACKAGE_DIR" -maxdepth 1 \
+        -name '_C_stable_libtorch*.so' -print -quit | grep -q .
+
+%runscript
+    exec vllm serve "$@"
+
+%help
+    vllm-berag based on vllm/vllm-openai:v0.24.0-cu129.
+
+    The BERAG source snapshot is stored at /opt/vllm-berag-src. Its Python
+    files are merged into the upstream package at build time so that the
+    base image's compiled CUDA extensions are preserved.
+
+    Run with NVIDIA GPU support, for example:
+
+        apptainer exec --nv IMAGE.sif python3 -c \
+            'import torch, vllm; print(vllm.__file__); print(torch.__version__)'
+
+        apptainer run --nv IMAGE.sif MODEL_NAME --host 0.0.0.0 --port 8000