Installation

This document describes how to install vllm-ascend manually.

Requirements

• OS: Linux
• Python: >= 3.10, < 3.13
• Hardware with Ascend NPUs. It's usually the Atlas 800 A2 series and Atlas 300I DUO.

• Software:

  Atlas A2 inference products / Atlas A3 inference productsAtlas 300I DUO

  Software	Supported version	Note
  Ascend HDK	Refer to the documentation CANN 9.0.1	Required for CANN
  CANN	== 9.0.1	Required for vllm-ascend and torch-npu
  torch-npu	== 2.10.0.post2	Required for vllm-ascend, No need to install manually, it will be auto installed in below steps
  torch	== 2.10.0	Required for torch-npu and vllm, No need to install manually, it will be auto installed in below steps
  NNAL	== 9.0.1	Required for libatb.so, enables advanced tensor operations

  Software	Supported version	Note
  Ascend HDK	Refer to the documentation CANN 9.1.0-beta.1	Required for CANN
  CANN	== 9.1.0-beta.1	Required for vllm-ascend and torch-npu
  torch-npu	== 2.10.0.post2	Required for vllm-ascend, No need to install manually, it will be auto installed in below steps
  torch	== 2.10.0	Required for torch-npu and vllm, No need to install manually, it will be auto installed in below steps
  NNAL	== 9.1.0-beta.1	Required for libatb.so, enables advanced tensor operations
  triton / triton-ascend	Not supported	Uninstalled in Dockerfile.310p

Install a matched software stack

Treat vLLM Ascend, vLLM, PyTorch, torch-npu, CANN, and Triton Ascend as one compatibility set. For a release installation, select one complete row from the release compatibility matrix. For main-branch development, use the exact vLLM commit recorded in .github/vllm-main-verified.commit; an arbitrary vLLM tag or PyPI release can have different transitive dependencies.

There are two installation methods:

• Using pip: first prepare the environment manually or via a CANN image, then install vllm-ascend using pip.
• Using docker: use the vllm-ascend pre-built docker image directly.

Configure Ascend CANN environment

Before installation, you need to make sure firmware/driver, and CANN are installed correctly, refer to CANN Installation for more details.

Configure hardware environment

To verify that the Ascend NPU firmware and driver were correctly installed, run:

npu-smi info

Refer to CANN Installation for more details.

Configure software environment

Before using pipBefore using docker

The easiest way to prepare your software environment is using CANN image directly:

Note

The CANN prebuilt image includes NNAL (Ascend Neural Network Acceleration Library), which provides libatb.so for advanced tensor operations. No additional installation is required when using the prebuilt image.

# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci7
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/cann:9.0.1-910b-ubuntu22.04-py3.12
docker run --rm \
    --name vllm-ascend-env \
    --shm-size=1g \
    --device $DEVICE \
    --device /dev/davinci_manager \
    --device /dev/devmm_svm \
    --device /dev/hisi_hdc \
    -v /usr/local/dcmi:/usr/local/dcmi \
    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
    -v /root/.cache:/root/.cache \
    -it $IMAGE bash

Click here to see 'Install CANN manually'

You can also install CANN manually:

Warning

If you encounter "libatb.so not found" errors during runtime, please ensure NNAL is properly installed as shown in the manual installation steps below.

# Create a virtual environment.
python -m venv vllm-ascend-env
source vllm-ascend-env/bin/activate

# Install required Python packages.
python -m pip install --upgrade pip
pip3 install attrs numpy decorator sympy cffi pyyaml pathlib2 psutil protobuf scipy requests absl-py wheel typing_extensions

# Download and install the CANN package.
wget --header="Referer: https://www.hiascend.com/" https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%209.0.1/Ascend-cann-toolkit_9.0.1_linux-"$(uname -i)".run
chmod +x ./Ascend-cann-toolkit_9.0.1_linux-"$(uname -i)".run
./Ascend-cann-toolkit_9.0.1_linux-"$(uname -i)".run --full
source /usr/local/Ascend/ascend-toolkit/set_env.sh
export ASCEND_TOOLKIT_HOME="${ASCEND_TOOLKIT_HOME:-/usr/local/Ascend/ascend-toolkit/latest}"

wget --header="Referer: https://www.hiascend.com/" https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%209.0.1/Ascend-cann-910b-ops_9.0.1_linux-"$(uname -i)".run
chmod +x ./Ascend-cann-910b-ops_9.0.1_linux-"$(uname -i)".run
./Ascend-cann-910b-ops_9.0.1_linux-"$(uname -i)".run --install

wget --header="Referer: https://www.hiascend.com/" https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%209.0.1/Ascend-cann-nnal_9.0.1_linux-"$(uname -i)".run
chmod +x ./Ascend-cann-nnal_9.0.1_linux-"$(uname -i)".run
./Ascend-cann-nnal_9.0.1_linux-"$(uname -i)".run --install

source /usr/local/Ascend/nnal/atb/set_env.sh

No extra steps are needed if you are using the vllm-ascend prebuilt Docker image.

Once this is done, you can start to set up vllm and vllm-ascend.

Set up using Python

First, install system dependencies and configure the pip mirror:

# Using apt-get with mirror
sed -i 's|ports.ubuntu.com|mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list
apt-get update -y && apt-get install -y gcc g++ cmake ninja-build libnuma-dev wget git curl jq
# Or using yum
# yum update -y && yum install -y gcc g++ cmake ninja-build numactl-devel wget git curl jq
# Config pip mirror, only versions 0.11.0 and earlier are supported, if using a version later than 0.11.0, do not execute this command
pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple

[Optional] Then configure the extra-index of pip if you are working on an x86 machine or using torch-npu dev version:

# For torch-npu dev version or x86 machine
pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/"

Then you can install vllm and vllm-ascend from a pre-built wheel using one of the following methods:

Original installationuv-wheelnext installation

# Install vllm-project/vllm. The newest supported version is v0.22.1.
pip install vllm==0.22.1

# Install vllm-project/vllm-ascend.
pip install \
--extra-index-url https://mirrors.huaweicloud.com/ascend/repos/pypi/variant \
vllm-ascend==0.22.1rc1

The uv-wheelnext installation downloads only the delta on top of vllm, resulting in a smaller download size. First install uv-wheelnext to support incremental wheels:

# install uv-wheelnext
curl -LsSf https://astral.sh/uv/install.sh | sed 's/verify_checksum "$_file"/true/' | INSTALLER_DOWNLOAD_URL=https://wheelnext.astral.sh sh
source $HOME/.local/bin/env

# Install vllm-project/vllm. The newest supported version is v0.22.1.
pip install vllm==0.22.1

# Install vllm-project/vllm-ascend from wheelnext index.
uv pip install --system \
--extra-index-url https://mirrors.huaweicloud.com/ascend/repos/pypi/variant   \
--index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple \
vllm-ascend==0.22.1rc1

Note

If you encounter errors during uv pip install (e.g., corrupted cache or stale package data), try clearing the uv cache first and then re-run the install command:

uv cache clean

Click here to see 'Build from source code'

or build from source code:

Note

To install triton-ascend, run:

pip install triton-ascend==3.2.1 --extra-index-url https://mirrors.huaweicloud.com/ascend/repos/pypi

If you are installing via uv, make sure to install triton-ascend last, after all other packages have been installed, to avoid dependency resolution conflicts.

# Install vLLM.
git clone --depth 1 --branch v0.22.1 https://github.com/vllm-project/vllm
cd vllm
VLLM_TARGET_DEVICE=empty pip install -e .
cd ..

# Install vLLM Ascend.
git clone --depth 1 --branch v0.22.1rc1 https://github.com/vllm-project/vllm-ascend.git
cd vllm-ascend
git submodule update --init --recursive
pip install -e .
cd ..

If you are building custom operators for Atlas A3, you should run git submodule update --init --recursive manually, or ensure your environment has internet access.

Atlas 300I DUO

Atlas 300I DUO does not support triton or triton-ascend. Source installations can pull these packages as dependencies; remove them before running on Atlas 300I DUO:

pip uninstall -y triton-ascend triton

CPU-only build verification

CPU-only verification checks that the Python package can be built when no Ascend device is visible. It does not validate NPU runtime loading, inference examples, custom kernels, or NPU-specific tests. A CANN toolkit is still required because the build reads its headers and libraries.

Install the Python build backend and native build tools first. The editable build uses setuptools-scm directly, and arctic-inference requires CMake and Ninja when a compatible wheel is not available:

python -m pip install --upgrade \
    pip "setuptools>=64" "setuptools-scm>=8" wheel \
    attrs googleapis-common-protos \
    "cmake>=3.26" ninja

This build-only procedure intentionally does not install vLLM. If you continue with combined vLLM and vLLM Ascend testing on the main branch, use the exact vLLM commit recorded in .github/vllm-main-verified.commit and verify the combined environment as described below.

On x86, install the CPU variants of the PyTorch packages from the PyTorch CPU index before installing the remaining Ascend dependencies:

python -m pip install \
    --index-url https://download.pytorch.org/whl/cpu/ \
    torch==2.10.0 torchvision==0.25.0 torchaudio==2.10.0
python -m pip install \
    --extra-index-url https://mirrors.huaweicloud.com/ascend/repos/pypi \
    torch-npu==2.10.0.post2 triton-ascend==3.2.1
python -m pip install \
    --extra-index-url https://mirrors.huaweicloud.com/ascend/repos/pypi \
    -r requirements.txt

Set the build target explicitly and disable device backend auto-loading before building vLLM Ascend:

export ASCEND_TOOLKIT_HOME="${ASCEND_TOOLKIT_HOME:-/usr/local/Ascend/ascend-toolkit/latest}"
export TORCH_DEVICE_BACKEND_AUTOLOAD=0
export COMPILE_CUSTOM_KERNELS=0
export SOC_VERSION=ascend910b1  # Atlas A2; use the matching value below for other products
python -m pip install \
    --no-build-isolation \
    --no-deps \
    --extra-index-url https://mirrors.huaweicloud.com/ascend/repos/pypi \
    -e .

Together, the explicit build dependencies above and requirements.txt supply the complete build-system requirements before the non-isolated editable build. --no-build-isolation only reuses packages from the current build environment; it does not make incompatible vLLM, PyTorch, and torch-npu versions compatible. Before treating an environment as runtime-capable, run python -m pip check and resolve every reported conflict. Skip inference examples and NPU-specific tests when no device is available.

Note

To build custom operators, gcc/g++ higher than 8 and C++17 or higher are required. If you are using pip install -e . and encounter a torch-npu version conflict, please install with pip install --no-build-isolation -e . to build on system env. If you encounter other problems during compiling, it is probably because an unexpected compiler is being used, you may export CXX_COMPILER and C_COMPILER in the environment to specify your g++ and gcc locations before compiling.

If you are building in a CPU-only environment where npu-smi is unavailable, you need to set SOC_VERSION before pip install -e . so the build can target the correct chip. You can refer to Dockerfile* defaults, for example:

• Atlas A2: export SOC_VERSION=ascend910b1
• Atlas A3: export SOC_VERSION=ascend910_9391
• Atlas 300I DUO: export SOC_VERSION=ascend310p1
• Ascend 950 Products: export SOC_VERSION=<value starting with "ascend950">

Note

To enable the batch invariance feature, set VLLM_BATCH_INVARIANT=1 before building vllm-ascend to install the batch invariance custom operator library during the installation process. For usage guidance on the batch invariance feature, see https://github.com/vllm-project/vllm-ascend/blob/main/docs/source/user_guide/feature_guide/batch_invariance.md

Set up using Docker

vllm-ascend offers Docker images for deployment. You can just pull the prebuilt image from the image repository ascend/vllm-ascend and run it with bash.

Supported images as following.

image name	Hardware	OS
vllm-ascend:v0.22.1rc1	Atlas A2	Ubuntu
vllm-ascend:v0.22.1rc1-openeuler	Atlas A2	openEuler
vllm-ascend:v0.22.1rc1-a3	Atlas A3	Ubuntu
vllm-ascend:v0.22.1rc1-a3-openeuler	Atlas A3	openEuler
vllm-ascend:v0.22.1rc1-310p	Atlas 300I DUO	Ubuntu
vllm-ascend:v0.22.1rc1-310p-openeuler	Atlas 300I DUO	openEuler

Click here to see 'Build from Dockerfile'

or build IMAGE from source code:

git clone https://github.com/vllm-project/vllm-ascend.git
cd vllm-ascend
docker build -t vllm-ascend-dev-image:latest -f ./Dockerfile .

A2/A3Atlas 300I DUOAtlas 200I Pro

# Update --device according to your device (Atlas A2: /dev/davinci[0-7] Atlas A3:/dev/davinci[0-15]).
# Update the vllm-ascend image according to your environment.
# Note you should download the weight to /root/.cache in advance.
export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1
docker run --rm \
    --name vllm-ascend-env \
    --shm-size=1g \
    --net=host \
    --device /dev/davinci0 \
    --device /dev/davinci1 \
    --device /dev/davinci2 \
    --device /dev/davinci3 \
    --device /dev/davinci4 \
    --device /dev/davinci5 \
    --device /dev/davinci6 \
    --device /dev/davinci7 \
    --device /dev/davinci_manager \
    --device /dev/devmm_svm \
    --device /dev/hisi_hdc \
    -v /usr/local/dcmi:/usr/local/dcmi \
    -v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
    -v /root/.cache:/root/.cache \
    -it $IMAGE bash

The default workdir is /workspace, vLLM and vLLM Ascend code are placed in /vllm-workspace and installed in development mode (pip install -e) to help developers immediately make changes without requiring a new installation.

Adjust /dev/davinci0 to the NPU you want to use.

export DEVICE=/dev/davinci0
export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1-310p

docker run --rm \
    --name vllm-ascend \
    --shm-size=1g \
    --device $DEVICE \
    --device /dev/davinci_manager \
    --device /dev/devmm_svm \
    --device /dev/hisi_hdc \
    -v /usr/local/dcmi:/usr/local/dcmi \
    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
    -v /root/.cache:/root/.cache \
    -p 8000:8000 \
    -it $IMAGE bash

Atlas 200I Pro requires additional device nodes, driver libraries, and configuration files so that npu-smi and other driver commands work inside the container. Adjust /dev/davinci0 to the NPU you want to use.

export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1-310p

docker run --rm \
    --privileged \
    --name vllm-ascend \
    --shm-size=10g \
    --device=/dev/davinci0:/dev/davinci0 \
    --device=/dev/davinci_manager \
    --device=/dev/ascend_manager \
    --device=/dev/user_config \
    -v /etc/sys_version.conf:/etc/sys_version.conf \
    -v /etc/ld.so.conf.d/mind_so.conf:/etc/ld.so.conf.d/mind_so.conf \
    -v /etc/hdcBasic.cfg:/etc/hdcBasic.cfg \
    -v /var/dmp_daemon:/var/dmp_daemon \
    -v /usr/lib64/libmmpa.so:/usr/lib64/libmmpa.so \
    -v /usr/lib64/libcrypto.so.1.1:/usr/lib64/libcrypto.so.1.1 \
    -v /usr/local/sbin/npu-smi:/usr/local/sbin/npu-smi \
    -v /usr/lib64/libstackcore.so:/usr/lib64/libstackcore.so \
    -v /usr/lib/aarch64-linux-gnu/libyaml-0.so.2:/usr/lib64/libyaml-0.so.2 \
    -v /etc/slog.conf:/etc/slog.conf \
    -v /var/slogd:/var/slogd \
    -v /usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64 \
    -v /usr/lib64/libtensorflow.so:/usr/lib64/libtensorflow.so \
    -v /root/.cache:/root/.cache \
    -p 8000:8000 \
    -it $IMAGE bash

For openEuler, keep the same command structure and make the following substitutions:

• Set IMAGE to quay.io/ascend/vllm-ascend:v0.22.1rc1-310p-openeuler.
• Add -v /usr/lib64/libsemanage.so.2:/usr/lib64/libsemanage.so.2.
• Replace the libyaml mount with -v /usr/lib64/libyaml-0.so.2.0.9:/usr/lib64/libyaml-0.so.2.

Extra information

Verify installation

Create and run a simple inference test. The example.py can be like:

from vllm import LLM, SamplingParams

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]

# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Create an LLM.
llm = LLM(model="Qwen/Qwen3-0.6B")

# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

Then run:

python example.py

If you encounter a connection error with Hugging Face (e.g., We couldn't connect to 'https://huggingface.co' to load the files, and couldn't find them in the cached files.), run the following commands to use ModelScope as an alternative:

export VLLM_USE_MODELSCOPE=True
pip install modelscope
python example.py

This section shows ascend platform is successfully detected in vllm:

INFO 05-27 11:40:38 [__init__.py:44] Available plugins for group vllm.platform_plugins:
INFO 05-27 11:40:38 [__init__.py:46] - ascend -> vllm_ascend:register
INFO 05-27 11:40:38 [__init__.py:49] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load.
INFO 05-27 11:40:38 [__init__.py:238] Platform plugin ascend is activated

This section shows the final output:

Prompt: 'Hello, my name is', Generated text: ' Lucy and I am an 8 year old who loves to draw and write stories'
Prompt: 'The president of the United States is', Generated text: " a key leader in the federal government, and the president's role in the executive"
Prompt: 'The capital of France is', Generated text: ' a city. What is the capital of France? The capital of France is Paris'
Prompt: 'The future of AI is', Generated text: ' a topic that is being discussed in various contexts. In the business world, AI'

This section shows process exits after offline inference, and does not affect actual inference:

(EngineCore pid=970) INFO 05-12 11:36:00 [core.py:1201] Shutdown initiated (timeout=0)
(EngineCore pid=970) INFO 05-12 11:36:00 [core.py:1224] Shutdown complete
ERROR 05-12 11:36:01 [core_client.py:704] Engine core proc EngineCore died unexpectedly, shutting down client.
sys:1: DeprecationWarning: builtin type swigvarlink has no __module__ attribute

Multi-node Deployment

Verify Multi-Node Communication

First, check physical layer connectivity, then verify each node, and finally verify the inter-node connectivity.

Physical Layer Requirements

• The physical machines must be located on the same LAN, with network connectivity.
• All NPUs are connected with optical modules, and the connection status must be normal.

Each Node Verification

Execute the following commands on each node in sequence. The results must all be success and the status must be UP:

A2 seriesA3 series

 # Check the remote switch ports
 for i in {0..7}; do hccn_tool -i $i -lldp -g | grep Ifname; done 
 # Get the link status of the Ethernet ports (UP or DOWN)
 for i in {0..7}; do hccn_tool -i $i -link -g ; done
 # Check the network health status
 for i in {0..7}; do hccn_tool -i $i -net_health -g ; done
 # View the network detected IP configuration
 for i in {0..7}; do hccn_tool -i $i -netdetect -g ; done
 # View gateway configuration
 for i in {0..7}; do hccn_tool -i $i -gateway -g ; done
 # View NPU network configuration
 cat /etc/hccn.conf

 # Check the remote switch ports
 for i in {0..15}; do hccn_tool -i $i -lldp -g | grep Ifname; done 
 # Get the link status of the Ethernet ports (UP or DOWN)
 for i in {0..15}; do hccn_tool -i $i -link -g ; done
 # Check the network health status
 for i in {0..15}; do hccn_tool -i $i -net_health -g ; done
 # View the network detected IP configuration
 for i in {0..15}; do hccn_tool -i $i -netdetect -g ; done
 # View gateway configuration
 for i in {0..15}; do hccn_tool -i $i -gateway -g ; done
 # View NPU network configuration
 cat /etc/hccn.conf

Interconnect Verification

1. Get NPU IP Addresses

A2 seriesA3 series

for i in {0..7}; do hccn_tool -i $i -ip -g | grep ipaddr; done

for i in {0..15}; do hccn_tool -i $i -ip -g | grep ipaddr; done

2. Cross-Node PING Test

# Execute on the target node (replace with actual IP)
hccn_tool -i 0 -ping -g address x.x.x.x

Run Container In Each Node

Using vLLM-ascend official container is more efficient to run multi-node environment.

Run the following command to start the container in each node (You should download the weight to /root/.cache in advance):

A2 seriesA3 series

# Update the vllm-ascend image
# openEuler:
# export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1-openeuler
# Ubuntu:
# export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1
export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1

# Run the container using the defined variables
# Note if you are running bridge network with docker, Please expose available ports
# for multiple nodes communication in advance
docker run --rm \
--name vllm-ascend \
--net=host \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-it $IMAGE bash

# Update the vllm-ascend image
# openEuler:
# export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1-a3-openeuler
# Ubuntu:
# export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1-a3
export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1-a3

# Run the container using the defined variables
# Note if you are running bridge network with docker, Please expose available ports
# for multiple nodes communication in advance
docker run --rm \
--name vllm-ascend \
--net=host \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci8 \
--device /dev/davinci9 \
--device /dev/davinci10 \
--device /dev/davinci11 \
--device /dev/davinci12 \
--device /dev/davinci13 \
--device /dev/davinci14 \
--device /dev/davinci15 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-it $IMAGE bash