Python版本：3.9
CANN版本：8.0_rc1.alpha002
操作系统版本：Euler2.8
可在MA和BMS上使用

# 启动指令示例 ~~~ docker run -it -u root \ --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/Ascend/driver:/usr/local/Ascend/driver \ -v /usr/local/dcmi:/usr/local/dcmi \ -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ -v /usr/local/Ascend/firmware:/usr/local/Ascend/firmware \ -v /usr/local/sbin/npu-smi:/usr/local/sbin/npu-smi \ -v /home/aicc:/home/ma-user/work/aicc \ --name pytorch_ma \ --entrypoint=/bin/bash \ swr.cn-central-221.ovaijisuan.com/dxy/pytorch_2_1_0:PyTorch2.1.0-cann8.0_rc1.alpha002_py_3.9-euler_2.8.3-64GB ~~~ # dockerfile示例 ~~~ FROM registry-cbu.huawei.com/modelarts-images-manager/cache/euleros_arm:2.8.3 COPY Ascend-cann-toolkit_7.0.0_linux-aarch64.run /root COPY Ascend-hdk-*-npu-driver_23.0.rc2.2_linux-aarch64.run /root COPY Ascend-cann-kernels-*_7.0.0_linux.run /root COPY Miniforge3-Linux-aarch64.sh /root COPY requirements.txt /root USER root RUN cd /root && \ chmod +x A*.run && \ ./Ascend-hdk-*-npu-driver_23.0.rc2.2_linux-aarch64.run --quiet --docker && \ ./Ascend-cann-toolkit_7.0.0_linux-aarch64.run --quiet --full && \ ./Ascend-cann-kernels-*_7.0.0_linux.run --quiet --install && \ chmod 777 Miniforge3-Linux-aarch64.sh && \ mv Miniforge3-Linux-aarch64.sh /home/ma-user && \ chmod 777 requirements.txt && \ mv requirements.txt /home/ma-user && \ rm -rf A*.run && \ rm -rf /tmp/* ENV LD_LIBRARY_PATH=/usr/lib/aarch64-linux-gnu/hdf5/serial:/usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64/common:/usr/local/Ascend/driver/lib64/driver:/usr/local/Ascend/ascend-toolkit/latest/lib64:/usr/local/Ascend/ascend-toolkit/latest/compiler/lib64/plugin/opskernel:/usr/local/Ascend/ascend-toolkit/latest/compiler/lib64/plugin/nnengine:$LD_LIBRARY_PATH \ PYTHONPATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages:/usr/local/Ascend/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe:$PYTHONPATH \ PATH=/usr/local/Ascend/ascend-toolkit/latest/bin:/usr/local/Ascend/ascend-toolkit/latest/compiler/ccec_compiler/bin:$PATH \ ASCEND_AICPU_PATH=/usr/local/Ascend/ascend-toolkit/latest \ ASCEND_OPP_PATH=/usr/local/Ascend/ascend-toolkit/latest/opp \ TOOLCHAIN_HOME=/usr/local/Ascend/ascend-toolkit/latest/toolkit \ ASCEND_AUTOML_PATH=/usr/local/Ascend/ascend-toolkit/latest/tools ENV PYTHONPATH=/usr/local/Ascend/tfplugin/latest/python/site-packages:$PYTHONPATH USER ma-user RUN bash /home/ma-user/bind/Miniforge3-Linux-aarch64.sh -ub -p /home/ma-user/anaconda3 && \ source ~/.bashrc && \ . /home/ma-user/anaconda3/etc/profile.d/conda.sh && \ conda create -y --name MindSpore python=3.9 && \ conda activate MindSpore && \ pip --default-timeout=1000 install /usr/local/Ascend/ascend-toolkit/latest/fwkacllib/lib64/te-*-py3-none-any.whl && \ pip --default-timeout=1000 install /usr/local/Ascend/ascend-toolkit/latest/fwkacllib/lib64/hccl-*-py3-none-any.whl && \ pip --default-timeout=1000 install /usr/local/Ascend/ascend-toolkit/latest/tools/hccl_parser-*-py3-none-any.whl && \ pip install torch-2.1*.whl && \ pip install torch_npu*.whl && \ pip install -r requirements.txt WORKDIR /home/ma-user ~~~

在其他AICC使用本镜像时，
1） 在本地arm宿主机通过docker pull 拉取上面镜像到本地（即执行docker pull remote_image_address）
2） 用docker tag 将局点信息和组织名替换成对应版本（即执行 docker tag local_image_address remote_image_address），
3） 用docker push 将修改后的镜像名称推送到局点的swr服务中（即执行docker push remote_image_address）

若您有任何疑虑，您可以在这里反馈您的建议。