inference-nv-pytorch 25.03 - Container Compute Service - Alibaba Cloud Documentation Center

This topic describes the release notes for inference-nv-pytorch 25.03.

Main features and bug fix lists

Main features

PyTorch in the vLLM image is updated to 2.6.0.
vLLM is updated to v0.8.2.
SGLang is updated to v0.4.4.post1.
ACCL-N is updated to 2.23.4.12. New features and bug fixes are provided.

Bug fix

None

Content

	inference-nv-pytorch	inference-nv-pytorch
Tag	25.03-vllm0.8.2-pytorch2.6-cu124-20250327-serverless	25.03-sglang0.4.4.post1-pytorch2.5-cu124-20250327-serverless
Scenarios	LLM inference	LLM inference
Framework	PyTorch	PyTorch
Requirements	NVIDIA driver release >= 550	NVIDIA driver release >= 550
System components	Ubuntu 22.04 Python 3.10 Torch 2.6.0 CUDA 12.4 ACCL-N 2.23.4.12 accelerate 1.5.2 diffusers 0.32.2 flash_attn 2.7.4.post1 transformer 4.50.1 vllm 0.8.2 ray 2.44.0 triton 3.2.0	Ubuntu 22.04 Python 3.10 Torch 2.5.1 CUDA 12.4 ACCL-N 2.23.4.12 accelerate 1.5.2 diffusers 0.32.2 flash_attn 2.7.4.post1 transformer 4.48.3 vllm 0.7.2 ray 2.44.0 triton 3.2.0 flashinfer-python 0.2.3 sglang 0.4.4.post1 sgl-kernel 0.0.5

Assets

Public image

egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:25.03-vllm0.8.2-pytorch2.6-cu124-20250328-serverless
egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:25.03-sglang0.4.4.post1-pytorch2.5-cu124-20250327-serverless

VPC image

acs-registry-vpc.{region-id}.cr.aliyuncs.com/egslingjun/{image:tag}
{region-id} indicates the region where your ACS is activated, such as cn-beijing and cn-wulanchabu.
{image:tag} indicates the name and tag of the image.

Important

Currently, you can pull only images in the China (Beijing) region over a VPC.

Note

The inference-nv-pytorch:25.03-vllm0.8.2-pytorch2.6-cu124-20250328-serverless and inference-nv-pytorch:25.03-sglang0.4.4.post1-pytorch2.5-cu124-20250327-serverless images are suitable for ACS products and Lingjun multi-tenant products. They are not suitable for Lingjun single-tenant products.

Driver requirements

NVIDIA driver release >= 550

Quick Start

The following example uses only Docker to pull the inference-nv-pytorch image and uses the Qwen2.5-7B-Instruct model to test inference services.

Note

To use the inference-nv-pytorch image in ACS, you must select the image from the artifact center page of the console where you create workloads, or specify the image in a YAML file. For more information, refer to the following topics:

Pull the inference container image.

docker pull egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:[tag]

Download an open source model in the modelscope format.

pip install modelscope
cd /mnt
modelscope download --model Qwen/Qwen2.5-7B-Instruct --local_dir ./Qwen2.5-7B-Instruct

Run the following command to log on to the container.

docker run -d -t --network=host --privileged --init --ipc=host \
--ulimit memlock=-1 --ulimit stack=67108864  \
-v /mnt/:/mnt/ \
egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:[tag]

Run an inference test to test the inference conversation feature of vLLM.

Start the Server service.

python3 -m vllm.entrypoints.openai.api_server \
--model /mnt/Qwen2.5-7B-Instruct \
--trust-remote-code --disable-custom-all-reduce \
--tensor-parallel-size 1

Test on the client.

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "/mnt/Qwen2.5-7B-Instruct",  
    "messages": [
    {"role": "system", "content": "You are a friendly AI assistant."},
    {"role": "user", "content": "Please introduce deep learning."}
    ]}'

For more information about how to work with vLLM, see vLLM.

Known issues

None