Dockerfile

FROM nvidia/cuda:11.7.1-cudnn8-runtime-ubuntu22.04

ENV VLLM_LOGGING_LEVEL=DEBUG
ENV HF_HOME=/tmp/.cache/huggingface
ENV OMP_NUM_THREADS=1

# https://github.com/vllm-project/vllm/blob/v0.10.0/docs/getting_started/installation/gpu/rocm.inc.md?plain=1#L124
ENV VLLM_USE_TRITON_FLASH_ATTN=0

# https://github.com/vllm-project/vllm/blob/v0.10.0/docs/getting_started/quickstart.md?plain=1#L213
# `FLASH_ATTN` or `FLASHINFER` or `XFORMERS`.
# https://github.com/vllm-project/vllm/blob/main/vllm/envs.py#L426-L435
# Backend for attention computation
# Available options:
# - "TORCH_SDPA": use torch.nn.MultiheadAttention
# - "FLASH_ATTN": use FlashAttention
# - "XFORMERS": use XFormers
# - "ROCM_FLASH": use ROCmFlashAttention
# - "FLASHINFER": use flashinfer
# - "FLASHMLA": use FlashMLA
# Choose XFORMERS that most stable for T4
ENV VLLM_ATTENTION_BACKEND=XFORMERS

# Set environment variables to avoid interactive prompts
ENV DEBIAN_FRONTEND=noninteractive

# Update the package list and install necessary dependencies
RUN apt-get update && \
    apt-get install -y software-properties-common

# Add the 'deadsnakes' PPA to get Python 3.12
RUN add-apt-repository ppa:deadsnakes/ppa

# Update the package list again to include the new repository
RUN apt-get update

# Install Python 3.12, pip, and other necessary development tools
RUN apt-get install -y tzdata git python3.12 python3.12-venv python3.12-dev

# Create a symbolic link to make 'python' and 'pip' commands work
RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.12 1 && \
    update-alternatives --install /usr/bin/pip pip /usr/bin/pip3 1

RUN python --version && python -m pip --version
    
# RUN apt-get update && apt-get install -y python3 python3-pip git
# RUN pip install --upgrade pip

# Install vLLM
RUN pip install vllm==0.10.0 --extra-index-url https://download.pytorch.org/whl/cu113

RUN useradd -m appuser
USER appuser

RUN mkdir -p /tmp/.cache/huggingface

# Download at build time,
# to ensure during restart we won't have to wait for the download from HF (only wait for docker pull).
# In Docker Spaces, the secrets management is different for security reasons.
# Once you create a secret in the Settings tab,
# you can expose the secret by adding the following line in your Dockerfile:
#
# For example, if SECRET_EXAMPLE is the name of the secret you created in the Settings tab,
# you can read it at build time by mounting it to a file, then reading it with $(cat /run/secrets/SECRET_EXAMPLE).
# https://huggingface.co/docs/hub/en/spaces-sdks-docker#buildtime
#
# AFTER TRIAL AND ERROR WE GOT 16GB (16431849854 bytes) OF LAYERS :(
#
# RUN --mount=type=secret,id=HF_TOKEN,mode=0444,required=true HF_TOKEN=$(cat /run/secrets/HF_TOKEN) python /app/download_model.py

EXPOSE 7860

# Export for runtime environment
CMD vllm serve \
  --model "meta-llama/Llama-3.2-3B-Instruct" \
  --task generate \
  --revision "0cb88a4f764b7a12671c53f0838cd831a0843b95" \
  --code-revision "0cb88a4f764b7a12671c53f0838cd831a0843b95" \
  --tokenizer-revision "0cb88a4f764b7a12671c53f0838cd831a0843b95" \
  --seed 42 \
  --host 0.0.0.0 \
  --port 7860 \
  --max-num-batched-tokens 32768 \
  --max-model-len 32768 \
  --dtype float16 \
  --enforce-eager \
  --gpu-memory-utilization 0.9 \
  --enable-prefix-caching \
  --disable-log-requests \
  --trust-remote-code