Successful Practice of Using Docker+VLLM on 4090

Dockerfile content：

```
FROM nvidia/cuda:12.6.3-cudnn-devel-ubuntu22.04
ENV TZ=Asia/Shanghai
ENV DEBIAN_FRONTEND=noninteractive

RUN sed -i 's|http://archive.ubuntu.com/ubuntu/|https://mirrors.tuna.tsinghua.edu.cn/ubuntu/|g' /etc/apt/sources.list && \
sed -i 's|http://security.ubuntu.com/ubuntu/|https://mirrors.tuna.tsinghua.edu.cn/ubuntu/|g' /etc/apt/sources.list

RUN mkdir -p /tmp/apt-key-gpghome && chmod 1777 /tmp

RUN apt update && apt install -y python3.10 python3.10-dev python3-pip vim curl wget git git-lfs
RUN python3.10 -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple rawpy pillow requests
RUN python3.10 -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
```

vllm_test.py content

```
from vllm import LLM, SamplingParams
from vllm.model_executor.models.deepseek_ocr import NGramPerReqLogitsProcessor
from PIL import Image

llm = LLM(
model="DeepSeek-OCR",
enable_prefix_caching=False,
mm_processor_cache_gb=0,
logits_processors=[NGramPerReqLogitsProcessor]
)

image_1 = Image.open("path/to/your/image_1.png").convert("RGB")
image_2 = Image.open("path/to/your/image_2.png").convert("RGB")
prompt = "\nFree OCR."

model_input = [
{
"prompt": prompt,
"multi_modal_data": {"image": image_1}
},
{
"prompt": prompt,
"multi_modal_data": {"image": image_2}
}
]

sampling_param = SamplingParams(
temperature=0.0,
max_tokens=8192,
# ngram logit processor args
extra_args=dict(
ngram_size=30,
window_size=90,
whitelist_token_ids={128821, 128822}, # whitelist: ,
),
skip_special_tokens=False,
)
model_outputs = llm.generate(model_input, sampling_param)
for output in model_outputs:
print(output.outputs[0].text)
```
success image