Atlas 200I DK A2安装MindSpore Ascend版本

2024-05-26 09:52
文章标签 安装 版本 atlas mindspore a2 ascend dk 200i

本文主要是介绍Atlas 200I DK A2安装MindSpore Ascend版本，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

一、参考资料

mindspore快速安装

二、重要说明

经过博主多次尝试多个版本，Atlas 200I DK A2无法安装MindSpore Ascend版本。

也有其他博主测试，也未尝成功，例如：【MindSpore易点通·漫游世界】在Atlas 200I DK A2 (CANN6.2.RC2)上安装MindSpore Ascend版的踩坑记录

mindspore 1.5.2 报错无法运行（./tensor_add_sample: symbol lookup error: /home/HwHiAiUser/.local/lib/python3.9/site-packages/mindspore/lib/libmindspore.so: undefined symbol: _ZN2ge5Model8SetGraphERKNS_5GraphE）

mindspore 1.6.2 报错无法运行（./tensor_add_sample: symbol lookup error: /home/HwHiAiUser/.local/lib/python3.9/site-packages/mindspore/lib/libmindspore.so: undefined symbol: _ZN2ge5Model8SetGraphERKNS_5GraphE）

mindspore 1.7.1 报错无法运行 （./tensor_add_sample: error while loading shared libraries: libhccl.so: cannot open shared object file: No such file or directory）

mindspore 1.8.1 报错无法运行（./tensor_add_sample: error while loading shared libraries: libhccl.so: cannot open shared object file: No such file or directory）

mindspore 1.9.0 报错无法运行（./tensor_add_sample: symbol lookup error: /home/HwHiAiUser/.local/lib/python3.9/site-packages/mindspore/lib/libmindspore.so: undefined symbol: _ZN2ge5Model8SetGraphERKNS_5GraphE）

mindspore 1.10.1 报错无法运行（./tensor_add_sample: symbol lookup error: /home/HwHiAiUser/.local/lib/python3.9/site-packages/mindspore/lib/libmindspore.so: undefined symbol: _ZN2ge5Model8SetGraphERKNS_5GraphE）

mindspore 2.0.0 报错无法运行（Unsupported device target Ascend）

mindspore 2.1.0 报错无法运行（Unsupported device target Ascend）

三、准备工作

1. 测试环境

设备型号：Atlas 200I DK A2
Operating System + Version: Ubuntu 22.04 LTS
CPU Type: 4核TAISHANV200M处理器
AI CPU number: 0
control CPU number: 4
RAM: 4GB 
miscroSD: 128GB
CANN Vertion: 7.0.RC1

HwHiAiUser@davinci-mini:~$ npu-smi info -t aicpu-config -i 0 -c 0Current AI CPU number          : 0Current control CPU number     : 4Number of AI CPUs set          : 0Number of control CPUs set     : 4

2. MindSpore与CANN版本对齐

通过 链接 查询MindSpore与Ascend配套软件包的版本配套关系。

在这里插入图片描述

3. 安装mindspore_ascend

详细过程，请参考：pip方式安装MindSpore Ascend 310版本

4. 验证是否安装成功

4.1 方法一

import mindspore as ms# ms.set_context(device_target='CPU')
# ms.set_context(device_target='GPU')
ms.set_context(device_target="Ascend")
ms.set_context(device_id=0)
mindspore.run_check()

如果输出以下结果，则说明mindspore_ascend安装成功。

MindSpore version: 版本号
The result of multiplication calculation is correct, MindSpore has been installed on platform [Ascend] successfully!

4.2 方法二

import numpy as np
import mindspore as ms
import mindspore.ops as opsms.set_context(device_target="Ascend")
x = ms.Tensor(np.ones([1,3,3,4]).astype(np.float32))
y = ms.Tensor(np.ones([1,3,3,4]).astype(np.float32))
print(ops.add(x, y))

如果输出以下结果，则说明mindspore_ascend安装成功。

[[[[2. 2. 2. 2.][2. 2. 2. 2.][2. 2. 2. 2.]][[2. 2. 2. 2.][2. 2. 2. 2.][2. 2. 2. 2.]][[2. 2. 2. 2.][2. 2. 2. 2.][2. 2. 2. 2.]]]]

4.3 方法三

ascend310_single_op_sample

这是一个[1, 2, 3, 4]与[2, 3, 4, 5]相加的简单样例，代码工程目录结构如下：

└─ascend310_single_op_sample├── CMakeLists.txt                    // 编译脚本├── README.md                         // 使用说明├── main.cc                           // 主函数└── tensor_add.mindir                 // MindIR模型文件

unzip ascend310_single_op_sample.zip
cd ascend310_single_op_sample# 编译
cmake . -DMINDSPORE_PATH=`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`
make# 执行
./tensor_add_sample

如果输出以下结果，则说明mindspore_ascend安装成功。

3
5
7
9

四、测试代码

1. 示例一

用MindSpore搭建模型，并进行测试。

"""
MindSpore implementation of `MobileNetV1`.
Refer to MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.
"""
import timefrom mindspore import nn, Tensor, ops
import mindspore.common.initializer as init
import mindspore as ms
from PIL import Image
from mindcv.data import create_transforms
import numpy as npdef depthwise_separable_conv(inp: int, oup: int, stride: int) -> nn.SequentialCell:return nn.SequentialCell(# dwnn.Conv2d(inp, inp, 3, stride, pad_mode="pad", padding=1, group=inp, has_bias=False),nn.BatchNorm2d(inp),nn.ReLU(),# pwnn.Conv2d(inp, oup, 1, 1, pad_mode="pad", padding=0, has_bias=False),nn.BatchNorm2d(oup),nn.ReLU(),)class MobileNetV1(nn.Cell):r"""MobileNetV1 model class, based on`"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" <https://arxiv.org/abs/1704.04861>`_Args:alpha: scale factor of model width. Default: 1.in_channels: number the channels of the input. Default: 3.num_classes: number of classification classes. Default: 1000."""def __init__(self,alpha: float = 1.,in_channels: int = 3,num_classes: int = 1000) -> None:super().__init__()input_channels = int(32 * alpha)# Setting of depth-wise separable conv# c: number of output channel# s: stride of depth-wise convblock_setting = [# c, s[64, 1],[128, 2],[128, 1],[256, 2],[256, 1],[512, 2],[512, 1],[512, 1],[512, 1],[512, 1],[512, 1],[1024, 2],[1024, 1],]features = [nn.Conv2d(in_channels, input_channels, 3, 2, pad_mode="pad", padding=1, has_bias=False),nn.BatchNorm2d(input_channels),nn.ReLU()]for c, s in block_setting:output_channel = int(c * alpha)features.append(depthwise_separable_conv(input_channels, output_channel, s))input_channels = output_channelself.features = nn.SequentialCell(features)# self.pool = GlobalAvgPooling()self.pool = nn.AdaptiveAvgPool2d(output_size=(1, 1))self.classifier = nn.Dense(input_channels, num_classes)self._initialize_weights()def _initialize_weights(self) -> None:"""Initialize weights for cells."""for _, cell in self.cells_and_names():if isinstance(cell, nn.Conv2d):cell.weight.set_data(init.initializer(init.XavierUniform(),cell.weight.shape,cell.weight.dtype))if isinstance(cell, nn.Dense):cell.weight.set_data(init.initializer(init.TruncatedNormal(),cell.weight.shape,cell.weight.dtype))def forward_features(self, x: Tensor) -> Tensor:x = self.features(x)return xdef forward_head(self, x: Tensor) -> Tensor:squeeze = ops.Squeeze(0)x = squeeze(x)x = self.pool(x)squeeze = ops.Squeeze(2)x = squeeze(x)x = x.transpose()x = self.classifier(x)return xdef construct(self, x: Tensor) -> Tensor:x = self.forward_features(x)x = self.forward_head(x)return xdef mobilenet_v1_100_224(pretrained: bool = False, num_classes: int = 1000, in_channels=3, **kwargs) -> MobileNetV1:"""Get MobileNetV1 model without width scaling.Refer to the base class `models.MobileNetV1` for more details."""model = MobileNetV1(alpha=1.0, in_channels=in_channels, num_classes=num_classes, **kwargs)return modelif __name__ == '__main__':# ms.set_context(device_target='GPU')# ms.set_context(device_target='CPU')ms.set_context(device_target="Ascend")ms.set_context(device_id=0)ms.set_seed(1)ms.set_context(mode=ms.PYNATIVE_MODE)img = Image.open("image.jpg").convert("RGB")# create transformtransform_list = create_transforms(dataset_name="imagenet",is_training=False,)transform_list.pop(0)for transform in transform_list:img = transform(img)img = np.expand_dims(img, axis=0)# create modelnetwork = mobilenet_v1_100_224()for i in range(100):# warmupnetwork(ms.Tensor(img))time_begin = time.time()for i in range(1000):# predictnetwork(ms.Tensor(img))time_total = (time.time() - time_begin) * 1000 / 1000print(f"total time is: {time_total}")# print(network)

2. 示例二

调用 mindcv库中的预训练模型进行测试。

"""MindSpore Inference Script
"""import numpy as np
from PIL import Imageimport mindspore as msfrom mindcv.data import create_transforms
from mindcv.models import create_model
import time# ms.set_context(device_target='CPU')
# ms.set_context(device_target='GPU')ms.set_context(device_target='Ascend')
ms.set_context(device_id=0)
ms.set_context(max_device_memory="3.5GB")def main():ms.set_seed(1)ms.set_context(mode=ms.PYNATIVE_MODE)img = Image.open("image.jpg").convert("RGB")# create transformtransform_list = create_transforms(dataset_name="imagenet",is_training=False,)transform_list.pop(0)for transform in transform_list:img = transform(img)img = np.expand_dims(img, axis=0)# create modelnetwork = create_model(model_name="mobilenet_v1_100",  # mobilenet_v1_100_224pretrained=False,)network.set_train(False)for i in range(100):# warmupnetwork(ms.Tensor(img))time_begin = time.time()for i in range(1000):# predictnetwork(ms.Tensor(img))time_total = (time.time() - time_begin) * 1000 / 1000print(f"total time is: {time_total}")if __name__ == "__main__":main()

五、FAQ

Q：RuntimeError: Load op info form json config failed, version: Ascend310B4

[WARNING] ME(230369:255086392991776,MainProcess):2024-05-25-17:29:28.302.942 [mindspore/run_check/_check_version.py:375] MindSpore version 2.1.1 and "te" wheel package version 7.0 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install
[WARNING] ME(230369:255086392991776,MainProcess):2024-05-25-17:29:28.305.619 [mindspore/run_check/_check_version.py:382] MindSpore version 2.1.1 and "hccl" wheel package version 7.0 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install
[WARNING] ME(230369:255086392991776,MainProcess):2024-05-25-17:29:28.305.849 [mindspore/run_check/_check_version.py:396] Please pay attention to the above warning, countdown: 3
[WARNING] ME(230369:255086392991776,MainProcess):2024-05-25-17:29:29.307.139 [mindspore/run_check/_check_version.py:396] Please pay attention to the above warning, countdown: 2
[WARNING] ME(230369:255086392991776,MainProcess):2024-05-25-17:29:30.308.249 [mindspore/run_check/_check_version.py:396] Please pay attention to the above warning, countdown: 1
[ERROR] KERNEL(230369,e7ffaf56f120,python):2024-05-25-17:29:35.761.869 [mindspore/ccsrc/kernel/oplib/op_info_utils.cc:172] LoadOpInfoJson] Get op info json suffix path failed, soc_version: Ascend310B4
[ERROR] KERNEL(230369,e7ffaf56f120,python):2024-05-25-17:29:35.762.199 [mindspore/ccsrc/kernel/oplib/op_info_utils.cc:111] GenerateOpInfos] Load op info json failed, version: Ascend310B4
Traceback (most recent call last):File "/root/Downloads/mindspore_ascend_demo.py", line 8, in <module>print(ops.add(x, y))File "/usr/local/miniconda3/envs/mindspore22/lib/python3.9/site-packages/mindspore/common/_stub_tensor.py", line 49, in funreturn method(*arg, **kwargs)File "/usr/local/miniconda3/envs/mindspore22/lib/python3.9/site-packages/mindspore/common/tensor.py", line 486, in __str__return str(self.asnumpy())File "/usr/local/miniconda3/envs/mindspore22/lib/python3.9/site-packages/mindspore/common/tensor.py", line 924, in asnumpyreturn Tensor_.asnumpy(self)
RuntimeError: Load op info form json config failed, version: Ascend310B4----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_kernel_runtime.cc:431 Init[ERROR] PIPELINE(230369,e7ffedd76020,python):2024-05-25-17:29:35.824.442 [mindspore/ccsrc/pipeline/jit/pipeline.cc:2311] ClearResAtexit] Check exception before process exit: Load op info form json config failed, version: Ascend310B4----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_kernel_runtime.cc:431 Init

mindspore_ascend 2.1.1 测试失败。

Q：RuntimeError: The device address type is wrong: type name in address:CPU, type name in context:Ascend

RuntimeError: The device address type is wrong: type name in address:CPU, type name in context:Ascend----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/ccsrc/plugin/device/ascend/hal/hardware/ge_device_res_manager.cc:72 AllocateMemory

mindspore_ascend 2.2.0 测试失败。

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