install_ascend_cn.md

July 12, 2026 · View on GitHub

[中文版|English]

💫 Huawei Ascend（华为昇腾）ascend (Triton 3.5)

对应的 Triton 版本为 3.5，基于 aarch64 平台
可用于 910B, 910C

1. 构建及运行环境

1.1 使用镜像（910B）

如果网络环境畅通，不必执行后续步骤 1.x，依赖库会在构建时自动拉取。

# Plan A: docker pull (13.3GB)
IMAGE=harbor.baai.ac.cn/flagtree/flagtree-ascend3.5-910b-py311-cann9.0.0-ubuntu22.04-aarch64:202606-torch2.9.0-base
docker pull ${IMAGE}
# Plan B: docker load (4.8GB)
IMAGE=flagtree-ascend3.5-910b-py311-cann9.0.0-ubuntu22.04-aarch64:202606-torch2.9.0-base
wget https://baai-cp-web.ks3-cn-beijing.ksyuncs.com/trans/flagtree-ascend3.5-910b-py311-cann9.0.0-ubuntu22.04-aarch64.202606-torch2.9.0-base.tar.gz
docker load -i flagtree-ascend3.5-910b-py311-cann9.0.0-ubuntu22.04-aarch64.202606-torch2.9.0-base.tar.gz

CONTAINER=flagtree-dev-xxx
docker run -dit -u 0 --user=root \
    --network=host --pid=host --ipc=host --privileged \
    -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
    -v /usr/local/Ascend/add-ons/:/usr/local/Ascend/add-ons/ \
    -v /usr/local/sbin/:/usr/local/sbin/ \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
    --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 /etc/localtime:/etc/localtime:ro \
    -v /data:/data -v /home:/home -v /tmp:/tmp \
    -w /root --name ${CONTAINER} ${IMAGE} bash
docker exec -it ${CONTAINER} /bin/bash

1.2 安装 cann

910B 镜像中已经安装 A2 cann。对于 910C 需在 https://www.hiascend.com/developer/download/community/result?module=cann 注册账号后下载对应平台的 cann-ops。

# cann-toolkit (A2|A3)
chmod +x Ascend-cann-toolkit_9.0.0_linux-aarch64.run
./Ascend-cann-toolkit_9.0.0_linux-aarch64.run --install
# cann-ops for 910B (A2)
chmod +x Ascend-cann-910b-ops_9.0.0_linux-aarch64.run
./Ascend-cann-910b-ops_9.0.0_linux-aarch64.run --install
# cann-ops for 910C (A3)
chmod +x Ascend-cann-A3-ops_9.0.0_linux-aarch64.run
./Ascend-cann-A3-ops_9.0.0_linux-aarch64.run --install

1.3 手动下载 FlagTree 依赖库

mkdir -p ~/.flagtree/ascend; cd ~/.flagtree/ascend
wget https://baai-cp-web.ks3-cn-beijing.ksyuncs.com/trans/llvm-7d5de303-ubuntu-aarch64-python311-compat_v0.6.0.tar.gz
tar zxvf llvm-7d5de303-ubuntu-aarch64-python311-compat_v0.6.0.tar.gz

cd ${YOUR_CODE_DIR}/FlagTree/third_party
git clone https://github.com/flagos-ai/flir.git

cd ${YOUR_CODE_DIR}/FlagTree/third_party/ascend
git clone https://gitcode.com/Ascend/AscendNPU-IR.git
git checkout 4c304921

1.4 手动下载 Triton 依赖库

镜像中已下载安装 Triton 依赖库。如果无需从源码构建 FlagTree 或 Triton，那么无需下载 Triton 依赖库。

cd ${YOUR_CODE_DIR}/FlagTree
# For Triton 3.5 (aarch64)
wget https://baai-cp-web.ks3-cn-beijing.ksyuncs.com/trans/build-deps-triton_3.5.x-linux-aarch64.tar.gz
sh python/scripts/unpack_triton_build_deps.sh ./build-deps-triton_3.5.x-linux-aarch64.tar.gz

执行完上述脚本后，原有的 ~/.triton 目录将被重命名，新的 ~/.triton 目录会被创建并存放预下载包。注意执行脚本过程中会提示手动确认。

2. 安装命令

2.1 免源码安装

# Note: First install PyTorch, then execute the following commands
python3 -m pip uninstall -y triton  # Repeat the cmd until fully uninstalled
RES="--index-url=https://resource.flagos.net/repository/flagos-pypi-hosted/simple"
python3.11 -m pip install flagtree===0.6.0+ascend3.5 $RES

安装 flagtree 后，可通过下列命令查看：

python3 -m pip show flagtree

2.2 从源码构建

cd ${YOUR_CODE_DIR}/FlagTree
git checkout -b triton_v3.5.x origin/triton_v3.5.x
export PATH=~/.flagtree/ascend/llvm-7d5de303-ubuntu-aarch64-python311-compat/bin/:${PATH}  # clang
export FLAGTREE_BACKEND=ascend
MAX_JOBS=32 python3 -m pip install . --no-build-isolation -v

3. 测试验证

测试前需执行 source /usr/local/Ascend/ascend-toolkit/set_env.sh

参考 Tests of ascend3.5 backend

💫 Huawei Ascend（华为昇腾）ascend (Triton 3.2)

对应的 Triton 版本为 3.2，基于 aarch64 平台
可用于 910B, 910C

1. 构建及运行环境

1.1 使用预装镜像（910C）

该预装镜像基于 Dockerfile-ubuntu22.04-python3.11-ascend 执行后续步骤 1.x 并安装 FlagTree 制作而成。使用该预装镜像，则对于 910C 不必执行后续步骤 1.x，对于 910B 也仅需执行步骤 1.2。如果网络环境畅通，也不必执行后续步骤 1.x，依赖库会在构建时自动拉取。

# Plan A: docker pull (26.2GB)
IMAGE=harbor.baai.ac.cn/flagtree/flagtree-ascend-910c-py311-torch2.6.0-cann8.5.0-ubuntu22.04-aarch64:202603
docker pull ${IMAGE}
# Plan B: docker load (8.8GB)
IMAGE=flagtree-ascend-910c-py311-torch2.6.0-cann8.5.0-ubuntu22.04-aarch64:202603
wget https://baai-cp-web.ks3-cn-beijing.ksyuncs.com/trans/flagtree-ascend-910c-py311-torch2.6.0-cann8.5.0-ubuntu22.04-aarch64.202603.tar.gz
docker load -i flagtree-ascend-910c-py311-torch2.6.0-cann8.5.0-ubuntu22.04-aarch64.202603.tar.gz

CONTAINER=flagtree-dev-xxx
docker run -dit -u 0 --user=root \
    --network=host --pid=host --ipc=host --privileged \
    -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
    -v /usr/local/Ascend/add-ons/:/usr/local/Ascend/add-ons/ \
    -v /usr/local/sbin/:/usr/local/sbin/ \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
    --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 /etc/localtime:/etc/localtime:ro \
    -v /data:/data -v /home:/home -v /tmp:/tmp \
    -w /root --name ${CONTAINER} ${IMAGE} bash
docker exec -it ${CONTAINER} /bin/bash