使用NVIDIA runtime 运行一个 Docker 容器,验证是否能识别 GPU。 确保容器内能够访问 GPU 设备。 # Run nvidia-smi to list GPU devicesnvidia-smi -Lif[$?-ne0];thenecho"nvidia-smi failed to execute."exit1fi# Run a Docker container with NVIDIA runtime to list GPU devicesdocker run --runtime=n...
docker run --runtime nvidia --gpus all\-v ~/.cache/huggingface:/root/.cache/huggingface\--env"HUGGING_FACE_HUB_TOKEN=<secret>"\-p 8000:8000\--ipc=host\vllm/vllm-openai:latest\--model mistralai/Mistral-7B-v0.1 (1)本地实操 - 拉取镜像 (base)ailearn@gpts:~$ docker pull vllm/vl...
# this won't be needed for future versions of this docker image # or future versions of triton. RUN ldconfig /usr/local/cuda-12.1/compat/ WORKDIR /workspace # install build and runtime dependencies COPY requirements.txt requirements.txt RUN --mount=type=cache,target=/root/.cache/pip \ pip...
Dockerfile.ppc64le Dockerfile.rocm Dockerfile.tpu Dockerfile.xpu LICENSE MANIFEST.in README.md SECURITY.md collect_env.py find_cuda_init.py format.sh pyproject.toml python_only_dev.py requirements-build.txt requirements-common.txt requirements-cpu.txt ...
# versions are derived from Dockerfile.rocm # set(TORCH_SUPPORTED_VERSION_CUDA "2.3.0") set(TORCH_SUPPORTED_VERSION_ROCM_5X "2.0.1") set(TORCH_SUPPORTED_VERSION_ROCM_6X "2.1.1") set(TORCH_SUPPORTED_VERSION_ROCM "2.4.0") # # Try to find python package with an executable that exactly...
A high-throughput and memory-efficient inference and serving engine for LLMs - vllm/Dockerfile at main · llm-vlm/vllm
I tried to run vllm from Docker image follow the official tutorial https://docs.vllm.ai/en/latest/serving/deploying_with_docker.html. My model files are stored at /home/appuser/repo/models/Qwen-14b-Chat-AWQ, I launched the docker image w...
Dockerfile.rocm_base Dockerfile.s390x Dockerfile.tpu Dockerfile.xpu LICENSE MANIFEST.in README.md RELEASE.md SECURITY.md collect_env.py find_cuda_init.py format.sh pyproject.toml python_only_dev.py setup.py use_existing_torch.py Latest commit ...
docker exec -it ipex-llm-serving-xpu-container /bin/bash To verify the device is successfully mapped into the container, run sycl-ls to check the result. In a machine with Arc A770, the sampled output is: root@arda-arc12:/# sycl-ls [opencl:acc:0] Intel(R) FPGA Emulation Platform ...
A high-throughput and memory-efficient inference and serving engine for LLMs - vllm/Dockerfile at main · Hyper-Accel/vllm