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71IENDB`mnist_mindsporeexample_npu/README.md000066400000000000000000000301461441612507300200140ustar00rootroot00000000000000# 如何在启智平台上进行模型训练 - NPU版本 请注意！智算网络NPU训练任务已经改版，请参考最新的样例代码进行修改，用户需要在代码里使用mox拷贝数据集并解压！ - **启智集群和智算网络集群的单数据集训练，多数据集训练，训练使用方式不同，请按需求选择一种训练方式即可，注意区别(以下环境默认是训练环境)**： - 启智集群单数据集单卡或多卡的训练示例请参考示例中[train.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/train.py)的代码注释 - 启智集群单数据集单卡或多卡**加载模型**的训练示例请参考示例中 [pretrain.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/pretrain.py)的代码注释 - 启智集群单数据集单卡的推理示例请参考示例中[inference.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/inference.py)的代码注释 - 启智集群多数据集单卡或多卡的训练示例请参考示例中[train_for_multidataset.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/train_for_multidataset.py)的代码注释 - 启智集群训练任务已经将ImageNet-1k数据集挂载到训练镜像，具体使用方法请参考示例中[read_imagenet.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/read_imagenet.py)的代码注释 - 智算网络集群单数据集或多数据集，单卡或多卡训练示例请参考示例中[train_for_c2net.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/train_for_c2net.py)的代码注释 - 智算网络集群单数据集或多数据集，单卡或多卡**加载模型**的训练示例请参考示例中[pretrain_for_c2net.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/pretrain_for_c2net.py)的代码注释 - 继续训练功能示例代码请参考 启智集群[train_continue.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/train_continue.py) 与 智算集群[train_continue_c2net.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/train_continue_c2net.py) ，启智与智算的用法相同 - 更多关于分布式训练的教程可参考mindspore官网教程[mindspore分布式训练教程](https://www.mindspore.cn/tutorial/training/zh-CN/r1.2/advanced_use/distributed_training_ascend.html) - **NPU启智集群中单数据集和多数据集的区别**： - 超参数不同： 单数据集的超参数通过--data_url传递 多数据集的超参数通过--multi_data_url传递，并且需要保留--data_url - 数据集使用方式不同： 如本示例中单数据集MNISTData.zip的使用方式是:数据集位于/cache/data下 多数据集时MNISTData.zip的使用方式是：数据集位于/cache/data/MNISTData/下 - **NPU启智集群和智算网络集群的区别**： - 启智集群需要使用moxing拷贝数据到obs - 智算网络集群需要moxing拷贝数据到obs,并且需要手动解压数据集 - **NPU启智集群调试镜像和训练镜像的环境的区别**： - 若想要使用调试环境的多卡并行训练，可参考示例[调试环境多卡并行示例](https://git.openi.org.cn/OpenIOSSG/MNIST_Example_NPU_Debug) ## 1 概述 - 本项目以LeNet-MNIST为例，简要介绍如何在启智AI协同平台上使用MindSpore完成训练任务，并提供单数据集的训练，多数据集的训练，智算网络的训练，单数据集推理等训练代码示例，旨在为AI开发者提供启智npu训练示例。对于示例代码有任何问题，欢迎在本项目中提issue。 - 用户可以直接使用本项目提供的数据集和代码文件创建自己的训练任务。 - 启智平台对接ModelArts和OBS，将数据集，代码，训练资源池等整合在启智AI协同平台上供开发者使用。 - ModelArts是华为云提供的面向开发者的一站式AI开发平台，集成了昇腾AI处理器资源池，用户可以在ModelArts下体验MindSpore。 - OBS是华为云提供的存储方式。 ## 2 准备工作 - 启智平台使用准备，本项目需要用户创建启智平台账户，克隆代码到自己的账户，上传数据集，具体操作方法可以通过访问[OpenI_Learning](https://git.openi.org.cn/zeizei/OpenI_Learning)项目学习小白训练营系列课程进行学习。 ### 2.1 数据准备 #### 数据集下载 - 数据集可从本项目的数据集目录中下载，[数据集下载](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/datasets?type=1) - 数据文件说明 - MNISTData数据集是由10类28∗28的灰度图片组成，训练数据集包含60000张图片，测试数据集包含10000张图片。 - 数据集压缩包的目录结构如下： > MNIST_Data.zip > ├── test > │ ├── t10k-images-idx3-ubyte > │ └── t10k-labels-idx1-ubyte > └── train > ├── train-images-idx3-ubyte > └── train-labels-idx1-ubyte > > checkpoint_lenet-1_1875.zip > ├── checkpoint_lenet-1_1875.ckpt > #### 数据集上传 - 由于本示例使用的是Mindspore开发，需要在NPU芯片运行，所以上传的数据集需要传到NPU界面。【注意：如果你需要试运行本示例，则无需再次上传数据集，因为本示例中的数据集MNIST_Example已经设置为公开数据集，可以直接引用或点赞收藏后使用】 - 如下所示： - ![avatar](Example_Picture/数据集上传位置.png) #### imagenet-1k - 平台已经将imagenet-1k挂载到了训练镜像中，可在训练脚本中直接读取，具体用法请参考上述示例代码。 - imagenet-1k在训练镜像中的绝对路径： ``` . └── cache/ ├── ascend ├── outputs ├── user-job-dir └── sfs/ └── data/ └── imagenet/ ├── train/ │ └── n01440764/ │ └── n01440764_11063.JPEG └── val/ └── n01440764/ └── ILSVRC2012_val_00011993.JPEG ``` ### 2.2 执行脚本准备 #### 示例代码 - 示例代码可从本仓库中下载，[代码下载](https://git.openi.org.cn/OpenIOSSG/MNIST_Example) - 代码文件说明 - [train.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/train.py)，启智集群单数据集训练的脚本文件，包括将数据集从obs拷贝到训练镜像中、指定迭代次数、把训练后的模型数据拷贝回obs等。具体说明请参考[train.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/train.py)的代码注释 - [train_for_c2net.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/train_for_c2net.py)，智算网络训练的脚本文件，包括指定迭代次数等。具体说明请参考[train_for_c2net.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/train_for_c2net.py)的代码注释 - [train_for_multidataset.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/train_for_multidataset.py)，启智集群包括多数据集训练的脚本文件，将多数据集从obs拷贝到训练镜像中、指定迭代次数、把训练后的模型数据拷贝回obs等。具体说明请参考[train_for_multidataset.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/train_for_multidataset.py)的代码注释 - [inference.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/inference.py)，启智集群用于推理的脚本文件，包括将数据集从obs拷贝到训练镜像中、指定迭代次数、把训练后的模型数据拷贝回obs等。具体说明请参考[inference.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/inference.py)的代码注释 - [config.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/config.py)，网络配置信息，在单数据集训练，多数据集训练，智算网络训练等训练脚本中会使用到。 - [dataset.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/dataset.py)，对原始数据集进行预处理，产生可用于网络训练的数据集，在单数据集的训练，多数据集的训练，智算网络的训练等训练脚本中会使用到。 - [lenet.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/lenet.py)，使用的训练网络，在单数据集训练，多数据集训练，智算网络训练等训练脚本中会使用到。 - [dataset_distributes.py](https://git.openi.org.cn/OpenIOSSG/MNIST_Example/src/branch/master/dataset_distributes.py)，对原始数据集进行预处理，产生可用于单机多卡训练的数据集。 ## 3 创建训练任务 - 准备好数据和执行脚本以后，需要创建训练任务将MindSpore脚本真正运行起来。首次使用的用户可参考本示例代码。 ### 使用MindSpore作为训练框架创建训练作业，界面截图如下图所示。 ![avatar](Example_Picture/新建训练任务页面.png) 表1 创建训练作业界面参数说明 | 参数名称 | 说明 | | -------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 代码分支 | 选择仓库代码中要使用的代码分支，默认可选择master分支。 | | AI引擎 | AI引擎选择[Ascend-Powered-Engine]和所需的MindSpore版本(本示例图片为 [Mindspore-1.3.0-python3.7-aarch64]，请注意使用与所选版本对应的脚本)。 | | 启动文件 | 启动文件选择代码目录下的启动脚本。 | | 数据集 | 数据集选择已上传到启智平台的数据集。 | | 运行参数 | 单数据集数据存储位置和训练输出位置分别对应运行参数data_url和train_url，注意多数据集需要增加参数multi_data_url并在代码中声明，选择增加运行参数可以向脚本中其他参数传值，如epoch_size。在这里只需填入其他参数传值，data_url和train_url已默认加入运行参数，用户无需重复指定，只需在代码中指定。 | | 资源池 | 规格选择[Ascend: 1 * Ascend 910 CPU：24 核 256GiB]，表示单机单卡 | ## 4 查看运行结果 ### 4.1 在训练作业界面可以查看运行日志 ![avatar](Example_Picture/查看日志页面.png) ### 4.2 训练结束后可以下载模型文件 ![avatar](Example_Picture/模型下载页面.png) ## 对于示例代码有任何问题，欢迎在本项目中提issue。 mnist_mindsporeexample_npu/config.py000066400000000000000000000020401441612507300203440ustar00rootroot00000000000000# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ network config setting, will be used in train.py """ from easydict import EasyDict as edict mnist_cfg = edict({ 'num_classes': 10, 'lr': 0.01, 'momentum': 0.9, 'epoch_size': 10, 'batch_size': 32, 'buffer_size': 1000, 'image_height': 32, 'image_width': 32, 'save_checkpoint_steps': 1875, 'keep_checkpoint_max': 150, 'air_name': "lenet", }) mnist_mindsporeexample_npu/dataset.py000066400000000000000000000046741441612507300205430ustar00rootroot00000000000000# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ Produce the dataset """ import mindspore.dataset as ds import mindspore.dataset.vision.c_transforms as CV import mindspore.dataset.transforms.c_transforms as C from mindspore.dataset.vision import Inter from mindspore.common import dtype as mstype def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1): """ create dataset for train or test """ # define dataset mnist_ds = ds.MnistDataset(data_path) resize_height, resize_width = 32, 32 rescale = 1.0 / 255.0 shift = 0.0 rescale_nml = 1 / 0.3081 shift_nml = -1 * 0.1307 / 0.3081 # define map operations resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) rescale_op = CV.Rescale(rescale, shift) hwc2chw_op = CV.HWC2CHW() type_cast_op = C.TypeCast(mstype.int32) # apply map operations on images mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers) # apply DatasetOps buffer_size = 10000 mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) mnist_ds = mnist_ds.repeat(repeat_size) return mnist_ds mnist_mindsporeexample_npu/dataset_distributed.py000066400000000000000000000045401441612507300231350ustar00rootroot00000000000000 """ Produce the dataset： 与单机不同的是，在数据集接口需要传入num_shards和shard_id参数，分别对应卡的数量和逻辑序号，建议通过HCCL接口获取： get_rank：获取当前设备在集群中的ID。 get_group_size：获取集群数量。 """ import mindspore.dataset as ds import mindspore.dataset.vision.c_transforms as CV import mindspore.dataset.transforms.c_transforms as C from mindspore.dataset.vision import Inter from mindspore.common import dtype as mstype from mindspore.communication.management import get_rank, get_group_size def create_dataset_parallel(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1, shard_id=0, num_shards=8): """ create dataset for train or test """ resize_height, resize_width = 32, 32 rescale = 1.0 / 255.0 shift = 0.0 rescale_nml = 1 / 0.3081 shift_nml = -1 * 0.1307 / 0.3081 # get shard_id and num_shards.Get the ID of the current device in the cluster And Get the number of clusters. shard_id = get_rank() num_shards = get_group_size() # define dataset mnist_ds = ds.MnistDataset(data_path, num_shards=num_shards, shard_id=shard_id) # define map operations resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) rescale_op = CV.Rescale(rescale, shift) hwc2chw_op = CV.HWC2CHW() type_cast_op = C.TypeCast(mstype.int32) # apply map operations on images mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers) # apply DatasetOps buffer_size = 10000 mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) mnist_ds = mnist_ds.repeat(repeat_size) return mnist_ds mnist_mindsporeexample_npu/inference.py000066400000000000000000000136511441612507300210470ustar00rootroot00000000000000""" ######################## single-dataset inference lenet example ######################## This example is a single-dataset inference tutorial. ######################## Instructions for using the inference environment ######################## 1、Inference task requires predefined functions (1)Copy single dataset from obs to inference image. function ObsToEnv(obs_data_url, data_dir) (2)Copy ckpt file from obs to inference image. function ObsUrlToEnv(obs_ckpt_url, ckpt_url) (3)Copy the output result to obs. function EnvToObs(train_dir, obs_train_url) 3、4 parameters need to be defined. --data_url is the dataset you selected on the Qizhi platform --ckpt_url is the weight file you choose on the Qizhi platform --data_url,--ckpt_url,--result_url,--device_target,These 4 parameters must be defined first in a single dataset, otherwise an error will be reported. There is no need to add these parameters to the running parameters of the Qizhi platform, because they are predefined in the background, you only need to define them in your code. 4、How the dataset is used Inference task uses data_url as the input, and data_dir (ie: '/cache/data') as the calling method of the dataset in the image. For details, please refer to the following sample code. """ import os import argparse import moxing as mox import mindspore.nn as nn from mindspore import context from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.train import Model from mindspore.nn.metrics import Accuracy from mindspore import Tensor import numpy as np from glob import glob from dataset import create_dataset from config import mnist_cfg as cfg from lenet import LeNet5 ### Copy single dataset from obs to inference image ### def ObsToEnv(obs_data_url, data_dir): try: mox.file.copy_parallel(obs_data_url, data_dir) print("Successfully Download {} to {}".format(obs_data_url, data_dir)) except Exception as e: print('moxing download {} to {} failed: '.format(obs_data_url, data_dir) + str(e)) return ### Copy ckpt file from obs to inference image### ### To operate on folders, use mox.file.copy_parallel. If copying a file. ### Please use mox.file.copy to operate the file, this operation is to operate the file def ObsUrlToEnv(obs_ckpt_url, ckpt_url): try: mox.file.copy(obs_ckpt_url, ckpt_url) print("Successfully Download {} to {}".format(obs_ckpt_url,ckpt_url)) except Exception as e: print('moxing download {} to {} failed: '.format(obs_ckpt_url, ckpt_url) + str(e)) return ### Copy the output result to obs### def EnvToObs(train_dir, obs_train_url): try: mox.file.copy_parallel(train_dir, obs_train_url) print("Successfully Upload {} to {}".format(train_dir,obs_train_url)) except Exception as e: print('moxing upload {} to {} failed: '.format(train_dir,obs_train_url) + str(e)) return ### --data_url,--ckpt_url,--result_url,--device_target,These 4 parameters must be defined first in a inference task, ### otherwise an error will be reported. ### There is no need to add these parameters to the running parameters of the Qizhi platform, ### because they are predefined in the background, you only need to define them in your code. parser = argparse.ArgumentParser(description='MindSpore Lenet Example') parser.add_argument('--data_url', type=str, default= '/cache/data/', help='path where the dataset is saved') parser.add_argument('--ckpt_url', help='model to save/load', default= '/cache/checkpoint.ckpt') parser.add_argument('--result_url', help='result folder to save/load', default= '/cache/result/') parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'], help='device where the code will be implemented (default: Ascend)') if __name__ == "__main__": args, unknown = parser.parse_known_args() ###Initialize the data and result directories in the inference image### data_dir = '/cache/data' result_dir = '/cache/result' ckpt_url = '/cache/checkpoint.ckpt' if not os.path.exists(data_dir): os.makedirs(data_dir) if not os.path.exists(result_dir): os.makedirs(result_dir) ###Copy dataset from obs to inference image ObsToEnv(args.data_url, data_dir) ###Copy ckpt file from obs to inference image ObsUrlToEnv(args.ckpt_url, ckpt_url) context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) network = LeNet5(cfg.num_classes) net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") repeat_size = cfg.epoch_size net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) model = Model(network, net_loss, net_opt, metrics={"Accuracy"}) print("============== Starting Testing ==============") param_dict = load_checkpoint(os.path.join(ckpt_url)) load_param_into_net(network, param_dict) ds_test = create_dataset(os.path.join(data_dir, "test"), batch_size=1).create_dict_iterator() data = next(ds_test) images = data["image"].asnumpy() labels = data["label"].asnumpy() print('Tensor:', Tensor(data['image'])) output = model.predict(Tensor(data['image'])) predicted = np.argmax(output.asnumpy(), axis=1) pred = np.argmax(output.asnumpy(), axis=1) print('predicted:', predicted) print('pred:', pred) print(f'Predicted: "{predicted[0]}", Actual: "{labels[0]}"') filename = 'result.txt' file_path = os.path.join(result_dir, filename) with open(file_path, 'a+') as file: file.write(" {}: {:.2f} \n".format("Predicted", predicted[0])) ###Copy result data from the local running environment back to obs, ###and download it in the inference task corresponding to the Qizhi platform EnvToObs(result_dir, args.result_url)mnist_mindsporeexample_npu/inference_for_multidataset.py000066400000000000000000000155471441612507300245030ustar00rootroot00000000000000""" ######################## multi-dataset inference lenet example ######################## This example is a single-dataset inference tutorial. ######################## Instructions for using the inference environment ######################## 1、Inference task requires predefined functions (1)Copy multi dataset from obs to inference image. function MultiObsToEnv(obs_data_url, data_dir) (2)Copy ckpt file from obs to inference image. function ObsUrlToEnv(obs_ckpt_url, ckpt_url) (3)Copy the output result to obs. function EnvToObs(train_dir, obs_train_url) 3、5 parameters need to be defined. --data_url is the first dataset you selected on the Qizhi platform --multi_data_url is the multi dataset you selected on the Qizhi platform --ckpt_url is the weight file you choose on the Qizhi platform --result_url is the output --data_url,--multi_data_url,--ckpt_url,--result_url,--device_target,These 5 parameters must be defined first in a single dataset, otherwise an error will be reported. There is no need to add these parameters to the running parameters of the Qizhi platform, because they are predefined in the background, you only need to define them in your code. 4、How the dataset is used Multi-datasets use multi_data_url as input, data_dir + dataset name + file or folder name in the dataset as the calling path of the dataset in the inference image. For example, the calling path of the test folder in the MNIST_Data dataset in this example is data_dir + "/MNIST_Data" +"/test" For details, please refer to the following sample code. """ import os import argparse import moxing as mox import mindspore.nn as nn from mindspore import context from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.train import Model from mindspore.nn.metrics import Accuracy from mindspore import Tensor import numpy as np from glob import glob from dataset import create_dataset from config import mnist_cfg as cfg from lenet import LeNet5 import json ### Copy multiple datasets from obs to inference image ### def MultiObsToEnv(multi_data_url, data_dir): #--multi_data_url is json data, need to do json parsing for multi_data_url multi_data_json = json.loads(multi_data_url) for i in range(len(multi_data_json)): path = data_dir + "/" + multi_data_json[i]["dataset_name"] if not os.path.exists(path): os.makedirs(path) try: mox.file.copy_parallel(multi_data_json[i]["dataset_url"], path) print("Successfully Download {} to {}".format(multi_data_json[i]["dataset_url"],path)) except Exception as e: print('moxing download {} to {} failed: '.format( multi_data_json[i]["dataset_url"], path) + str(e)) return ### Copy ckpt file from obs to inference image### ### To operate on folders, use mox.file.copy_parallel. If copying a file. ### Please use mox.file.copy to operate the file, this operation is to operate the file def ObsUrlToEnv(obs_ckpt_url, ckpt_url): try: mox.file.copy(obs_ckpt_url, ckpt_url) print("Successfully Download {} to {}".format(obs_ckpt_url,ckpt_url)) except Exception as e: print('moxing download {} to {} failed: '.format(obs_ckpt_url, ckpt_url) + str(e)) return ### Copy the output result to obs### def EnvToObs(train_dir, obs_train_url): try: mox.file.copy_parallel(train_dir, obs_train_url) print("Successfully Upload {} to {}".format(train_dir,obs_train_url)) except Exception as e: print('moxing upload {} to {} failed: '.format(train_dir,obs_train_url) + str(e)) return ### --data_url,--multi_data_url,--ckpt_url,--result_url,--device_target,These 5 parameters must be defined first in a multi dataset inference task, ### otherwise an error will be reported. ### There is no need to add these parameters to the running parameters of the Qizhi platform, ### because they are predefined in the background, you only need to define them in your code. parser = argparse.ArgumentParser(description='MindSpore Lenet Example') parser.add_argument('--data_url', type=str, default= '/cache/data1/', help='path where the dataset is saved') parser.add_argument('--multi_data_url', type=str, default= '/cache/data/', help='path where the dataset is saved') parser.add_argument('--ckpt_url', help='model to save/load', default= '/cache/checkpoint.ckpt') parser.add_argument('--result_url', help='result folder to save/load', default= '/cache/result/') parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'], help='device where the code will be implemented (default: Ascend)') if __name__ == "__main__": args, unknown = parser.parse_known_args() ###Initialize the data and result directories in the inference image### data_dir = '/cache/data' result_dir = '/cache/result' ckpt_url = '/cache/checkpoint.ckpt' if not os.path.exists(data_dir): os.makedirs(data_dir) if not os.path.exists(result_dir): os.makedirs(result_dir) ###Copy multiple dataset from obs to inference image MultiObsToEnv(args.multi_data_url, data_dir) ###Copy ckpt file from obs to inference image ObsUrlToEnv(args.ckpt_url, ckpt_url) context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) network = LeNet5(cfg.num_classes) net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") repeat_size = cfg.epoch_size net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) model = Model(network, net_loss, net_opt, metrics={"Accuracy"}) print("============== Starting Testing ==============") param_dict = load_checkpoint(os.path.join(ckpt_url)) load_param_into_net(network, param_dict) ds_test = create_dataset(os.path.join(data_dir + "/MNISTData", "test"), batch_size=1).create_dict_iterator() data = next(ds_test) images = data["image"].asnumpy() labels = data["label"].asnumpy() print('Tensor:', Tensor(data['image'])) output = model.predict(Tensor(data['image'])) predicted = np.argmax(output.asnumpy(), axis=1) pred = np.argmax(output.asnumpy(), axis=1) print('predicted:', predicted) print('pred:', pred) print(f'Predicted: "{predicted[0]}", Actual: "{labels[0]}"') filename = 'result.txt' file_path = os.path.join(result_dir, filename) with open(file_path, 'a+') as file: file.write(" {}: {:.2f} \n".format("Predicted", predicted[0])) ###Copy result data from the local running environment back to obs, ###and download it in the inference task corresponding to the Qizhi platform EnvToObs(result_dir, args.result_url)mnist_mindsporeexample_npu/lenet.py000066400000000000000000000040271441612507300202150ustar00rootroot00000000000000# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """LeNet.""" import mindspore.nn as nn from mindspore.common.initializer import Normal class LeNet5(nn.Cell): """ Lenet network Args: num_class (int): Number of classes. Default: 10. num_channel (int): Number of channels. Default: 1. Returns: Tensor, output tensor Examples: >>> LeNet(num_class=10) """ def __init__(self, num_class=10, num_channel=1, include_top=True): super(LeNet5, self).__init__() self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') self.relu = nn.ReLU() self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) self.include_top = include_top if self.include_top: self.flatten = nn.Flatten() self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) def construct(self, x): x = self.conv1(x) x = self.relu(x) x = self.max_pool2d(x) x = self.conv2(x) x = self.relu(x) x = self.max_pool2d(x) if not self.include_top: return x x = self.flatten(x) x = self.relu(self.fc1(x)) x = self.relu(self.fc2(x)) x = self.fc3(x) return x mnist_mindsporeexample_npu/pretrain.py000066400000000000000000000241501441612507300207310ustar00rootroot00000000000000""" ######################## single-dataset train lenet example ######################## This example is a single-dataset training tutorial. If it is a multi-dataset, please refer to the multi-dataset training tutorial train_for_multidataset.py. This example cannot be used for multi-datasets! ######################## Instructions for using the training environment ######################## The image of the debugging environment and the image of the training environment are two different images, and the working local directories are different. In the training task, you need to pay attention to the following points. 1、(1)The structure of the dataset uploaded for single dataset training in this example MNISTData.zip ├── test └── train 2、Single dataset training requires predefined functions (1)Copy single dataset from obs to training image function ObsToEnv(obs_data_url, data_dir) (2)Copy the output to obs function EnvToObs(train_dir, obs_train_url) (3)Download the input from Qizhi And Init function DownloadFromQizhi(obs_data_url, data_dir) (4)Upload the output to Qizhi function UploadToQizhi(train_dir, obs_train_url) (5)Copy ckpt file from obs to training image. function ObsUrlToEnv(obs_ckpt_url, ckpt_url) 3、4 parameters need to be defined --data_url is the dataset you selected on the Qizhi platform --data_url,--train_url,--ckpt_url,--device_target,These 4 parameters must be defined first in a single dataset task, otherwise an error will be reported. There is no need to add these parameters to the running parameters of the Qizhi platform, because they are predefined in the background, you only need to define them in your code. 4、How the dataset is used A single dataset uses data_url as the input, and data_dir (ie:'/cache/data') as the calling method of the dataset in the image. For details, please refer to the following sample code. 5、How to load the pretrain model checkpoint file The checkpoint file is loaded by the ckpt_url parameter In addition, if you want to get the model file after each training, you can call the UploadOutput. """ import os import argparse import moxing as mox from config import mnist_cfg as cfg from dataset import create_dataset from dataset_distributed import create_dataset_parallel from lenet import LeNet5 import mindspore.nn as nn from mindspore import context from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor from mindspore import load_checkpoint, load_param_into_net from mindspore.train import Model from mindspore.nn.metrics import Accuracy from mindspore.context import ParallelMode from mindspore.communication.management import init, get_rank import mindspore.ops as ops import time from upload import UploadOutput ### Copy single dataset from obs to training image### def ObsToEnv(obs_data_url, data_dir): try: mox.file.copy_parallel(obs_data_url, data_dir) print("Successfully Download {} to {}".format(obs_data_url, data_dir)) except Exception as e: print('moxing download {} to {} failed: '.format(obs_data_url, data_dir) + str(e)) #Set a cache file to determine whether the data has been copied to obs. #If this file exists during multi-card training, there is no need to copy the dataset multiple times. f = open("/cache/download_input.txt", 'w') f.close() try: if os.path.exists("/cache/download_input.txt"): print("download_input succeed") except Exception as e: print("download_input failed") return ### Copy ckpt file from obs to training image### ### To operate on folders, use mox.file.copy_parallel. If copying a file. ### Please use mox.file.copy to operate the file, this operation is to operate the file def ObsUrlToEnv(obs_ckpt_url, ckpt_url): try: mox.file.copy(obs_ckpt_url, ckpt_url) print("Successfully Download {} to {}".format(obs_ckpt_url,ckpt_url)) except Exception as e: print('moxing download {} to {} failed: '.format(obs_ckpt_url, ckpt_url) + str(e)) return ### Copy the output to obs### def EnvToObs(train_dir, obs_train_url): try: mox.file.copy_parallel(train_dir, obs_train_url) print("Successfully Upload {} to {}".format(train_dir,obs_train_url)) except Exception as e: print('moxing upload {} to {} failed: '.format(train_dir,obs_train_url) + str(e)) return def DownloadFromQizhi(obs_data_url, data_dir): device_num = int(os.getenv('RANK_SIZE')) if device_num == 1: ObsToEnv(obs_data_url,data_dir) context.set_context(mode=context.GRAPH_MODE,device_target=args.device_target) if device_num > 1: # set device_id and init for multi-card training context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=int(os.getenv('ASCEND_DEVICE_ID'))) context.reset_auto_parallel_context() context.set_auto_parallel_context(device_num = device_num, parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, parameter_broadcast=True) init() #Copying obs data does not need to be executed multiple times, just let the 0th card copy the data local_rank=int(os.getenv('RANK_ID')) if local_rank%8==0: ObsToEnv(obs_data_url,data_dir) #If the cache file does not exist, it means that the copy data has not been completed, #and Wait for 0th card to finish copying data while not os.path.exists("/cache/download_input.txt"): time.sleep(1) return def UploadToQizhi(train_dir, obs_train_url): device_num = int(os.getenv('RANK_SIZE')) local_rank=int(os.getenv('RANK_ID')) if device_num == 1: EnvToObs(train_dir, obs_train_url) if device_num > 1: if local_rank%8==0: EnvToObs(train_dir, obs_train_url) return ### --data_url,--train_url,--ckpt_url,--device_target,These 4 parameters must be defined first in a single dataset, ### otherwise an error will be reported. ###There is no need to add these parameters to the running parameters of the Qizhi platform, ###because they are predefined in the background, you only need to define them in your code. parser = argparse.ArgumentParser(description='MindSpore Lenet Example') parser.add_argument('--data_url', help='path to training/inference dataset folder', default= '/cache/data/') parser.add_argument('--train_url', help='output folder to save/load', default= '/cache/output/') parser.add_argument('--ckpt_url', help='model to save/load', default= '/cache/checkpoint.ckpt') parser.add_argument( '--device_target', type=str, default="Ascend", choices=['Ascend', 'CPU'], help='device where the code will be implemented (default: Ascend),if to use the CPU on the Qizhi platform:device_target=CPU') parser.add_argument('--epoch_size', type=int, default=5, help='Training epochs.') if __name__ == "__main__": args, unknown = parser.parse_known_args() data_dir = '/cache/data' train_dir = '/cache/output' ckpt_url = '/cache/checkpoint.ckpt' try: if not os.path.exists(data_dir): os.makedirs(data_dir) if not os.path.exists(train_dir): os.makedirs(train_dir) except Exception as e: print("path already exists") ###Initialize and copy data to training image ###Copy ckpt file from obs to training image ObsUrlToEnv(args.ckpt_url, ckpt_url) ###Copy data from obs to training image DownloadFromQizhi(args.data_url, data_dir) ###The dataset path is used here:data_dir +"/train" device_num = int(os.getenv('RANK_SIZE')) if device_num == 1: ds_train = create_dataset(os.path.join(data_dir, "train"), cfg.batch_size) if device_num > 1: ds_train = create_dataset_parallel(os.path.join(data_dir, "train"), cfg.batch_size) if ds_train.get_dataset_size() == 0: raise ValueError("Please check dataset size > 0 and batch_size <= dataset size") network = LeNet5(cfg.num_classes) net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) time_cb = TimeMonitor(data_size=ds_train.get_dataset_size()) ###The ckpt path is used here:ckpt_url load_param_into_net(network, load_checkpoint(ckpt_url)) if args.device_target != "Ascend": model = Model(network, net_loss, net_opt, metrics={"accuracy"}) else: model = Model(network, net_loss, net_opt, metrics={"accuracy"}, amp_level="O2") config_ck = CheckpointConfig( save_checkpoint_steps=cfg.save_checkpoint_steps, keep_checkpoint_max=cfg.keep_checkpoint_max) #Note that this method saves the model file on each card. You need to specify the save path on each card. # In this example, get_rank() is added to distinguish different paths. if device_num == 1: outputDirectory = train_dir + "/" if device_num > 1: outputDirectory = train_dir + "/" + str(get_rank()) + "/" ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", directory=outputDirectory, config=config_ck) print("============== Starting Training ==============") epoch_size = cfg['epoch_size'] if (args.epoch_size): epoch_size = args.epoch_size print('epoch_size is: ', epoch_size) #Custom callback, upload output after each epoch uploadOutput = UploadOutput(train_dir,args.train_url) model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor(), uploadOutput]) ###Copy the trained output data from the local running environment back to obs, ###and download it in the training task corresponding to the Qizhi platform #This step is not required if UploadOutput is called UploadToQizhi(train_dir,args.train_url)mnist_mindsporeexample_npu/pretrain_for_c2net.py000066400000000000000000000300721441612507300226720ustar00rootroot00000000000000""" ######################## Attention! ######################## 智算网络需要在代码里使用mox拷贝数据集并解压，请参考函数C2netMultiObsToEnv，预训练模型文件使用方式有所变化，请参考C2netModelToEnv The intelligent computing network needs to use mox to copy the dataset and decompress it in the code, please refer to the function C2netMultiObsToEnv() ######################## multi-dataset train lenet example ######################## This example is a multi-dataset training tutorial. If it is a single dataset, please refer to the single dataset training tutorial train.py. This example cannot be used for a single dataset! """ """ ######################## Instructions for using the training environment ######################## 1、(1)The structure of the dataset uploaded for multi-dataset training in this example MNISTData.zip ├── test └── train checkpoint_lenet-1_1875.zip ├── checkpoint_lenet-1_1875.ckpt (2)The dataset structure in the training image for multiple datasets in this example workroot ├── MNISTData | ├── test | └── train └── checkpoint_lenet-1_1875 ├── checkpoint_lenet-1_1875.ckpt 2、Multi-dataset training requires predefined functions (1)Copy multi-dataset from obs to training image and unzip function C2netMultiObsToEnv(multi_data_url, data_dir) (2)Copy the output to obs function EnvToObs(train_dir, obs_train_url) (2)Download the input from Qizhi And Init function DownloadFromQizhi(multi_data_url, data_dir) (2)Upload the output to Qizhi function UploadToQizhi(train_dir, obs_train_url) 3、4 parameters need to be defined --multi_data_url is the multi-dataset you selected on the Qizhi platform --multi_data_url,--train_url,--device_target,These 3 parameters must be defined first in a multi-dataset task, otherwise an error will be reported. There is no need to add these parameters to the running parameters of the Qizhi platform, because they are predefined in the background, you only need to define them in your code 4、How the dataset is used Multi-datasets use multi_data_url as input, data_dir + dataset name + file or folder name in the dataset as the calling path of the dataset in the training image. For example, the calling path of the train folder in the MNIST_Data dataset in this example is data_dir + "/MNIST_Data" +"/train" 5、How to load the pretrain model checkpoint file The checkpoint file is loaded by the ckpt_url parameter For details, please refer to the following sample code. """ import os import argparse import moxing as mox from config import mnist_cfg as cfg from dataset import create_dataset from dataset_distributed import create_dataset_parallel from lenet import LeNet5 import json import mindspore.nn as nn from mindspore import context from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor from mindspore.train import Model from mindspore.nn.metrics import Accuracy from mindspore import load_checkpoint, load_param_into_net from mindspore.context import ParallelMode from mindspore.communication.management import init, get_rank import time ### Copy multiple datasets from obs to training image and unzip### def C2netMultiObsToEnv(multi_data_url, data_dir): #--multi_data_url is json data, need to do json parsing for multi_data_url multi_data_json = json.loads(multi_data_url) print("multi_data_json:",multi_data_json) for i in range(len(multi_data_json)): zipfile_path = data_dir + "/" + multi_data_json[i]["dataset_name"] try: mox.file.copy(multi_data_json[i]["dataset_url"], zipfile_path) print("Successfully Download {} to {}".format(multi_data_json[i]["dataset_url"],zipfile_path)) #get filename and unzip the dataset filename = os.path.splitext(multi_data_json[i]["dataset_name"])[0] filePath = data_dir + "/" + filename if not os.path.exists(filePath): os.makedirs(filePath) os.system("unzip {} -d {}".format(zipfile_path, filePath)) except Exception as e: print('moxing download {} to {} failed: '.format( multi_data_json[i]["dataset_url"], zipfile_path) + str(e)) #Set a cache file to determine whether the data has been copied to obs. #If this file exists during multi-card training, there is no need to copy the dataset multiple times. f = open("/cache/download_dataset_input.txt", 'w') f.close() try: if os.path.exists("/cache/download_dataset_input.txt"): print("download_input succeed") except Exception as e: print("download_dataset_input failed") return ### Copy ckpt file from obs to training image### ### To operate on folders, use mox.file.copy_parallel. If copying a file. ### Please use mox.file.copy to operate the file, this operation is to operate the file def ObsUrlToEnv(obs_ckpt_url, ckpt_url): try: mox.file.copy(obs_ckpt_url, ckpt_url) print("Successfully Download {} to {}".format(obs_ckpt_url,ckpt_url)) except Exception as e: print('moxing download {} to {} failed: '.format(obs_ckpt_url, ckpt_url) + str(e)) return ### Copy the output model to obs ### def EnvToObs(train_dir, obs_train_url): try: mox.file.copy_parallel(train_dir, obs_train_url) print("Successfully Upload {} to {}".format(train_dir, obs_train_url)) except Exception as e: print('moxing upload {} to {} failed: '.format(train_dir, obs_train_url) + str(e)) return ### Copy multiple pretrain file from obs to training image and unzip### def C2netModelToEnv(model_url, model_dir): #--ckpt_url is json data, need to do json parsing for ckpt_url_json model_url_json = json.loads(model_url) print("model_url_json:",model_url_json) for i in range(len(model_url_json)): modelfile_path = model_dir + "/" + "checkpoint.ckpt" try: mox.file.copy(model_url_json[i]["model_url"], modelfile_path) print("Successfully Download {} to {}".format(model_url_json[i]["model_url"],modelfile_path)) except Exception as e: print('moxing download {} to {} failed: '.format( model_url_json[i]["model_url"], modelfile_path) + str(e)) return def DownloadDataFromQizhi(multi_data_url, data_dir): device_num = int(os.getenv('RANK_SIZE')) if device_num == 1: C2netMultiObsToEnv(multi_data_url,data_dir) context.set_context(mode=context.GRAPH_MODE,device_target=args.device_target) if device_num > 1: # set device_id and init for multi-card training context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=int(os.getenv('ASCEND_DEVICE_ID'))) context.reset_auto_parallel_context() context.set_auto_parallel_context(device_num = device_num, parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, parameter_broadcast=True) init() #Copying obs data does not need to be executed multiple times, just let the 0th card copy the data local_rank=int(os.getenv('RANK_ID')) if local_rank%8==0: C2netMultiObsToEnv(multi_data_url,data_dir) #If the cache file does not exist, it means that the copy data has not been completed, #and Wait for 0th card to finish copying data while not os.path.exists("/cache/download_dataset_input.txt"): time.sleep(1) return def DownloadModelFromQizhi(model_url, model_dir): device_num = int(os.getenv('RANK_SIZE')) if device_num == 1: C2netModelToEnv(model_url,model_dir) if device_num > 1: #Copying obs data does not need to be executed multiple times, just let the 0th card copy the data local_rank=int(os.getenv('RANK_ID')) if local_rank%8==0: C2netModelToEnv(model_url,model_dir) return parser = argparse.ArgumentParser(description='MindSpore Lenet Example') ### --multi_data_url,--train_url,--device_target,These 3 parameters must be defined first in a multi-dataset, ### otherwise an error will be reported. ### There is no need to add these parameters to the running parameters of the Qizhi platform, ### because they are predefined in the background, you only need to define them in your code. parser.add_argument('--multi_data_url', help='path to multi dataset', default= '/cache/data/') parser.add_argument('--ckpt_url', help='pre_train_model path in obs') # parser.add_argument('--model_url', # help='pre_train_model path in obs') parser.add_argument('--train_url', help='model folder to save/load', default= '/cache/output/') parser.add_argument( '--device_target', type=str, default="Ascend", choices=['Ascend', 'CPU'], help='device where the code will be implemented (default: Ascend),if to use the CPU on the Qizhi platform:device_target=CPU') parser.add_argument('--epoch_size', type=int, default=5, help='Training epochs.') if __name__ == "__main__": args, unknown = parser.parse_known_args() data_dir = '/cache/data' train_dir = '/cache/output' model_dir = '/cache/pretrain' if not os.path.exists(data_dir): os.makedirs(data_dir) if not os.path.exists(train_dir): os.makedirs(train_dir) if not os.path.exists(model_dir): os.makedirs(model_dir) ###Copy ckpt file from obs to training image DownloadModelFromQizhi(args.ckpt_url, model_dir) # ObsUrlToEnv(args.ckpt_url, ckpt_url) ###Initialize and copy data to training image DownloadDataFromQizhi(args.multi_data_url, data_dir) ###The dataset path is used here:data_dir + "/MNIST_Data" +"/train" device_num = int(os.getenv('RANK_SIZE')) #单卡时 if device_num == 1: ds_train = create_dataset(os.path.join(data_dir + "/MNISTData", "train"), cfg.batch_size) #多卡时，使用数据并行训练 if device_num > 1: ds_train = create_dataset_parallel(os.path.join(data_dir + "/MNISTData", "train"), cfg.batch_size) if ds_train.get_dataset_size() == 0: raise ValueError( "Please check dataset size > 0 and batch_size <= dataset size") network = LeNet5(cfg.num_classes) net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) time_cb = TimeMonitor(data_size=ds_train.get_dataset_size()) ###The ckpt path is used here:ckpt_url load_param_into_net(network, load_checkpoint(os.path.join(model_dir, "checkpoint.ckpt"))) if args.device_target != "Ascend": model = Model(network,net_loss,net_opt,metrics={"accuracy": Accuracy()}) else: model = Model(network, net_loss,net_opt,metrics={"accuracy": Accuracy()},amp_level="O2") config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, keep_checkpoint_max=cfg.keep_checkpoint_max) #Note that this method saves the model file on each card. You need to specify the save path on each card. # In this example, get_rank() is added to distinguish different paths. if device_num == 1: outputDirectory = train_dir if device_num > 1: outputDirectory = train_dir + "/" + str(get_rank()) + "/" ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", directory=outputDirectory, config=config_ck) print("============== Starting Training ==============") epoch_size = cfg['epoch_size'] if (args.epoch_size): epoch_size = args.epoch_size print('epoch_size is: ', epoch_size) model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()])mnist_mindsporeexample_npu/read_imagenet.py000066400000000000000000000065061441612507300216760ustar00rootroot00000000000000''' imagnet-1k 数据集已通过磁盘挂载的方式挂载在训练镜像中， 用户可参考下列示例代码读取直接使用。 挂载路径为 . └── cache/ ├── ascend ├── outputs ├── user-job-dir └── sfs/ └── data/ └── imagenet/ ├── train/ │ └── n01440764/ │ └── n01440764_11063.JPEG └── val/ └── n01440764/ └── ILSVRC2012_val_00011993.JPEG mindspore.dataset.ImageFolderDataset - 读取imagenet-1k数据，同一文件夹下的数据为同一类class。 mindspore.dataset.vision.c_transforms - 数据加载和预处理。 mindspore.dataset.ImageFolderDataset - map：给定一组数据增强列表，按顺序将数据增强作用在数据集对象上。 - batch：将数据集中连续 batch_size 条数据合并为一个批处理数据。 - to_json：将数据处理管道序列化为JSON字符串，如果提供了文件名，则转储到文件中。 ''' import os import argparse import moxing as mox import mindspore as ms from mindspore.dataset import ImageFolderDataset import mindspore.dataset.vision.c_transforms as transforms ### Copy the output to obs### def EnvToObs(train_dir, obs_train_url): try: mox.file.copy_parallel(train_dir, obs_train_url) print("Successfully Upload {} to {}".format(train_dir, obs_train_url)) except Exception as e: print('moxing upload {} to {} failed: '.format(train_dir, obs_train_url) + str(e)) return def UploadToQizhi(train_dir, obs_train_url): device_num = int(os.getenv('RANK_SIZE')) local_rank = int(os.getenv('RANK_ID')) if device_num == 1: EnvToObs(train_dir, obs_train_url) if device_num > 1: if local_rank % 8 == 0: EnvToObs(train_dir, obs_train_url) return parser = argparse.ArgumentParser(description='Read big dataset ImageNet Example') parser.add_argument('--train_url', help='output folder to save/load', default= '/cache/output/') if __name__ == "__main__": args, unknown = parser.parse_known_args() data_path = '/cache/sfs/data/imagenet/' modelart_output = '/cache/output' if not os.path.exists(modelart_output): os.makedirs(modelart_output) mean = [0.485 * 255, 0.456 * 255, 0.406 * 255] std = [0.229 * 255, 0.224 * 255, 0.225 * 255] dataset_train = ImageFolderDataset(os.path.join(data_path, "train"), shuffle=True) trans_train = [ transforms.RandomCropDecodeResize(size=224, scale=(0.08, 1.0), ratio=(0.75, 1.333)), transforms.RandomHorizontalFlip(prob=0.5), transforms.Normalize(mean=mean, std=std), transforms.HWC2CHW() ] dataset_train = dataset_train.map(operations=trans_train, input_columns=["image"]) dataset_train = dataset_train.batch(batch_size=16, drop_remainder=True) data_info = dataset_train.to_json(filename= modelart_output + '/data_info.json') print(data_info) UploadToQizhi(modelart_output, args.train_url)mnist_mindsporeexample_npu/train.py000066400000000000000000000216631441612507300202300ustar00rootroot00000000000000""" ######################## single-dataset train lenet example ######################## This example is a single-dataset training tutorial. If it is a multi-dataset, please refer to the multi-dataset training tutorial train_for_multidataset.py. This example cannot be used for multi-datasets! ######################## Instructions for using the training environment ######################## The image of the debugging environment and the image of the training environment are two different images, and the working local directories are different. In the training task, you need to pay attention to the following points. 1、(1)The structure of the dataset uploaded for single dataset training in this example MNISTData.zip ├── test └── train 2、Single dataset training requires predefined functions (1)Copy single dataset from obs to training image function ObsToEnv(obs_data_url, data_dir) (2)Copy the output to obs function EnvToObs(train_dir, obs_train_url) (3)Download the input from Qizhi And Init function DownloadFromQizhi(obs_data_url, data_dir) (4)Upload the output to Qizhi function UploadToQizhi(train_dir, obs_train_url) 3、3 parameters need to be defined --data_url is the dataset you selected on the Qizhi platform --data_url,--train_url,--device_target,These 3 parameters must be defined first in a single dataset task, otherwise an error will be reported. There is no need to add these parameters to the running parameters of the Qizhi platform, because they are predefined in the background, you only need to define them in your code. 4、How the dataset is used A single dataset uses data_url as the input, and data_dir (ie:'/cache/data') as the calling method of the dataset in the image. For details, please refer to the following sample code. In addition, if you want to get the model file after each training, you can call the UploadOutput. """ import os import argparse import moxing as mox from config import mnist_cfg as cfg from dataset import create_dataset from dataset_distributed import create_dataset_parallel from lenet import LeNet5 import mindspore.nn as nn from mindspore import context from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor from mindspore.train import Model from mindspore.nn.metrics import Accuracy from mindspore.context import ParallelMode from mindspore.communication.management import init, get_rank import mindspore.ops as ops import time from upload import UploadOutput ### Copy single dataset from obs to training image### def ObsToEnv(obs_data_url, data_dir): try: mox.file.copy_parallel(obs_data_url, data_dir) print("Successfully Download {} to {}".format(obs_data_url, data_dir)) except Exception as e: print('moxing download {} to {} failed: '.format(obs_data_url, data_dir) + str(e)) #Set a cache file to determine whether the data has been copied to obs. #If this file exists during multi-card training, there is no need to copy the dataset multiple times. f = open("/cache/download_input.txt", 'w') f.close() try: if os.path.exists("/cache/download_input.txt"): print("download_input succeed") except Exception as e: print("download_input failed") return ### Copy the output to obs### def EnvToObs(train_dir, obs_train_url): try: mox.file.copy_parallel(train_dir, obs_train_url) print("Successfully Upload {} to {}".format(train_dir,obs_train_url)) except Exception as e: print('moxing upload {} to {} failed: '.format(train_dir,obs_train_url) + str(e)) return def DownloadFromQizhi(obs_data_url, data_dir): device_num = int(os.getenv('RANK_SIZE')) if device_num == 1: ObsToEnv(obs_data_url,data_dir) context.set_context(mode=context.GRAPH_MODE,device_target=args.device_target) if device_num > 1: # set device_id and init for multi-card training context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=int(os.getenv('ASCEND_DEVICE_ID'))) context.reset_auto_parallel_context() context.set_auto_parallel_context(device_num = device_num, parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, parameter_broadcast=True) init() #Copying obs data does not need to be executed multiple times, just let the 0th card copy the data local_rank=int(os.getenv('RANK_ID')) if local_rank%8==0: ObsToEnv(obs_data_url,data_dir) #If the cache file does not exist, it means that the copy data has not been completed, #and Wait for 0th card to finish copying data while not os.path.exists("/cache/download_input.txt"): time.sleep(1) return def UploadToQizhi(train_dir, obs_train_url): device_num = int(os.getenv('RANK_SIZE')) local_rank=int(os.getenv('RANK_ID')) if device_num == 1: EnvToObs(train_dir, obs_train_url) if device_num > 1: if local_rank%8==0: EnvToObs(train_dir, obs_train_url) return ### --data_url,--train_url,--device_target,These 3 parameters must be defined first in a single dataset, ### otherwise an error will be reported. ###There is no need to add these parameters to the running parameters of the Qizhi platform, ###because they are predefined in the background, you only need to define them in your code. parser = argparse.ArgumentParser(description='MindSpore Lenet Example') parser.add_argument('--data_url', help='path to training/inference dataset folder', default= '/cache/data/') parser.add_argument('--train_url', help='output folder to save/load', default= '/cache/output/') parser.add_argument( '--device_target', type=str, default="Ascend", choices=['Ascend', 'CPU'], help='device where the code will be implemented (default: Ascend),if to use the CPU on the Qizhi platform:device_target=CPU') parser.add_argument('--epoch_size', type=int, default=5, help='Training epochs.') if __name__ == "__main__": args, unknown = parser.parse_known_args() data_dir = '/cache/data' train_dir = '/cache/output' try: if not os.path.exists(data_dir): os.makedirs(data_dir) if not os.path.exists(train_dir): os.makedirs(train_dir) except Exception as e: print("path already exists") ###Initialize and copy data to training image DownloadFromQizhi(args.data_url, data_dir) ###The dataset path is used here:data_dir +"/train" device_num = int(os.getenv('RANK_SIZE')) if device_num == 1: ds_train = create_dataset(os.path.join(data_dir, "train"), cfg.batch_size) if device_num > 1: ds_train = create_dataset_parallel(os.path.join(data_dir, "train"), cfg.batch_size) if ds_train.get_dataset_size() == 0: raise ValueError("Please check dataset size > 0 and batch_size <= dataset size") network = LeNet5(cfg.num_classes) net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) time_cb = TimeMonitor(data_size=ds_train.get_dataset_size()) if args.device_target != "Ascend": model = Model(network, net_loss, net_opt, metrics={"accuracy"}) else: model = Model(network, net_loss, net_opt, metrics={"accuracy"}, amp_level="O2") config_ck = CheckpointConfig( save_checkpoint_steps=cfg.save_checkpoint_steps, keep_checkpoint_max=cfg.keep_checkpoint_max) #Note that this method saves the model file on each card. You need to specify the save path on each card. # In this example, get_rank() is added to distinguish different paths. if device_num == 1: outputDirectory = train_dir + "/" if device_num > 1: outputDirectory = train_dir + "/" + str(get_rank()) + "/" ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", directory=outputDirectory, config=config_ck) print("============== Starting Training ==============") epoch_size = cfg['epoch_size'] if (args.epoch_size): epoch_size = args.epoch_size print('epoch_size is: ', epoch_size) #Custom callback, upload output after each epoch uploadOutput = UploadOutput(train_dir,args.train_url) model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor(), uploadOutput]) ###Copy the trained output data from the local running environment back to obs, ###and download it in the training task corresponding to the Qizhi platform #This step is not required if UploadOutput is called UploadToQizhi(train_dir,args.train_url)mnist_mindsporeexample_npu/train_continue.py000066400000000000000000000216101441612507300221240ustar00rootroot00000000000000##################################################################################################### # 继续训练功能：修改训练任务时，若勾选复用上次结果，则可在新训练任务的输出路径中读取到上次结果 # # 示例用法 # - 增加两个训练参数 # 'ckpt_save_name' 此次任务的输出文件名，用于保存此次训练的模型文件名称（不带后缀） # 'ckpt_load_name' 上一次任务的输出文件名，用于加载上一次输出的模型文件名称（不带后缀），首次训练默认为空，则不读取任何文件 # - 训练代码中判断 'ckpt_load_name' 是否为空，若不为空，则为继续训练任务 ##################################################################################################### import os import argparse import moxing as mox from config import mnist_cfg as cfg from dataset import create_dataset from dataset_distributed import create_dataset_parallel from lenet import LeNet5 import mindspore.nn as nn from mindspore import context from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor from mindspore import load_checkpoint, load_param_into_net from mindspore.train import Model from mindspore.nn.metrics import Accuracy from mindspore.context import ParallelMode from mindspore.communication.management import init, get_rank import mindspore.ops as ops import time ### Copy single dataset from obs to training image### def ObsToEnv(obs_data_url, data_dir): try: mox.file.copy_parallel(obs_data_url, data_dir) print("Successfully Download {} to {}".format(obs_data_url, data_dir)) except Exception as e: print('moxing download {} to {} failed: '.format(obs_data_url, data_dir) + str(e)) #Set a cache file to determine whether the data has been copied to obs. #If this file exists during multi-card training, there is no need to copy the dataset multiple times. f = open("/cache/download_input.txt", 'w') f.close() try: if os.path.exists("/cache/download_input.txt"): print("download_input succeed") except Exception as e: print("download_input failed") return ### Copy the output to obs### def EnvToObs(train_dir, obs_train_url): try: mox.file.copy_parallel(train_dir, obs_train_url) print("Successfully Upload {} to {}".format(train_dir,obs_train_url)) except Exception as e: print('moxing upload {} to {} failed: '.format(train_dir,obs_train_url) + str(e)) return def EnvUrlToObs(ckpt_url, obs_ckpt_url): try: mox.file.copy(ckpt_url, obs_ckpt_url) print("Successfully Download {} to {}".format(ckpt_url,obs_ckpt_url)) except Exception as e: print('moxing download {} to {} failed: '.format(ckpt_url, obs_ckpt_url) + str(e)) return def DownloadFromQizhi(obs_data_url, data_dir): device_num = int(os.getenv('RANK_SIZE')) if device_num == 1: ObsToEnv(obs_data_url,data_dir) context.set_context(mode=context.GRAPH_MODE,device_target=args.device_target) if device_num > 1: # set device_id and init for multi-card training context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=int(os.getenv('ASCEND_DEVICE_ID'))) context.reset_auto_parallel_context() context.set_auto_parallel_context(device_num = device_num, parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, parameter_broadcast=True) init() #Copying obs data does not need to be executed multiple times, just let the 0th card copy the data local_rank=int(os.getenv('RANK_ID')) if local_rank%8==0: ObsToEnv(obs_data_url,data_dir) #If the cache file does not exist, it means that the copy data has not been completed, #and Wait for 0th card to finish copying data while not os.path.exists("/cache/download_input.txt"): time.sleep(1) return def UploadToQizhi(train_dir, obs_train_url): device_num = int(os.getenv('RANK_SIZE')) local_rank=int(os.getenv('RANK_ID')) if device_num == 1: EnvToObs(train_dir, obs_train_url) if device_num > 1: if local_rank%8==0: EnvToObs(train_dir, obs_train_url) return ### --data_url,--train_url,--device_target,These 3 parameters must be defined first in a single dataset, ### otherwise an error will be reported. ###There is no need to add these parameters to the running parameters of the Qizhi platform, ###because they are predefined in the background, you only need to define them in your code. parser = argparse.ArgumentParser(description='MindSpore Lenet Example') parser.add_argument('--data_url', help='path to training/inference dataset folder', default= '/cache/data/') parser.add_argument('--train_url', help='output folder to save/load', default= '/cache/output/') parser.add_argument( '--device_target', type=str, default="Ascend", choices=['Ascend', 'CPU'], help='device where the code will be implemented (default: Ascend),if to use the CPU on the Qizhi platform:device_target=CPU') parser.add_argument('--epoch_size', type=int, default=5, help='Training epochs.') ### continue task parameters parser.add_argument('--ckpt_load_name', help='model name to save/load', default= '') parser.add_argument('--ckpt_save_name', help='model name to save/load', default= 'checkpoint') if __name__ == "__main__": args, unknown = parser.parse_known_args() data_dir = '/cache/data' base_path = '/cache/output' try: if not os.path.exists(data_dir): os.makedirs(data_dir) if not os.path.exists(base_path): os.makedirs(base_path) except Exception as e: print("path already exists") ###Initialize and copy data to training image ###Copy data from obs to training image DownloadFromQizhi(args.data_url, data_dir) ###The dataset path is used here:data_dir +"/train" device_num = int(os.getenv('RANK_SIZE')) if device_num == 1: ds_train = create_dataset(os.path.join(data_dir, "train"), cfg.batch_size) if device_num > 1: ds_train = create_dataset_parallel(os.path.join(data_dir, "train"), cfg.batch_size) if ds_train.get_dataset_size() == 0: raise ValueError("Please check dataset size > 0 and batch_size <= dataset size") network = LeNet5(cfg.num_classes) net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) time_cb = TimeMonitor(data_size=ds_train.get_dataset_size()) ### 继续训练模型加载 if args.ckpt_load_name: ObsToEnv(args.train_url, base_path) load_path = "{}/{}.ckpt".format(base_path,args.ckpt_load_name) param_dict = load_checkpoint(load_path) load_param_into_net(network, param_dict) print("Successfully load ckpt file:{}, saved_net_work:{}".format(load_path,param_dict)) ### 保存已有模型名避免重复回传结果 outputFiles = os.listdir(base_path) if args.device_target != "Ascend": model = Model(network, net_loss, net_opt, metrics={"accuracy": Accuracy()}) else: model = Model(network, net_loss, net_opt, metrics={"accuracy": Accuracy()}, amp_level="O2") config_ck = CheckpointConfig( save_checkpoint_steps=cfg.save_checkpoint_steps, keep_checkpoint_max=cfg.keep_checkpoint_max) #Note that this method saves the model file on each card. You need to specify the save path on each card. # In this example, get_rank() is added to distinguish different paths. if device_num == 1: save_path = base_path + "/" if device_num > 1: save_path = base_path + "/" + str(get_rank()) + "/" ckpoint_cb = ModelCheckpoint(prefix=args.ckpt_save_name, directory=save_path, config=config_ck) print("============== Starting Training ==============") epoch_size = cfg['epoch_size'] if (args.epoch_size): epoch_size = args.epoch_size print('epoch_size is: ', epoch_size) model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()]) ### 将训练容器中的新输出模型 回传到启智社区 outputFilesNew = os.listdir(base_path) new_models = [i for i in outputFilesNew if i not in outputFiles] for n in new_models: ckpt_url = base_path + "/" + n obs_ckpt_url = args.train_url + "/" + n EnvUrlToObs(ckpt_url, obs_ckpt_url)mnist_mindsporeexample_npu/train_for_c2net.py000066400000000000000000000241511441612507300221640ustar00rootroot00000000000000""" ######################## Attention! ######################## 智算网络需要在代码里使用mox拷贝数据集并解压，请参考函数C2netMultiObsToEnv; 不管是单数据集还是多数据集，在智算网络中都使用multi_data_url参数进行传递！ The intelligent computing network needs to use mox to copy the dataset and decompress it in the code, please refer to the function C2netMultiObsToEnv() ######################## multi-dataset train lenet example ######################## This example is a multi-dataset training tutorial. If it is a single dataset, please refer to the single dataset training tutorial train.py. This example cannot be used for a single dataset! """ """ ######################## Instructions for using the training environment ######################## 1、(1)The structure of the dataset uploaded for multi-dataset training in this example MNISTData.zip ├── test └── train checkpoint_lenet-1_1875.zip ├── checkpoint_lenet-1_1875.ckpt (2)The dataset structure in the training image for multiple datasets in this example workroot ├── MNISTData | ├── test | └── train └── checkpoint_lenet-1_1875 ├── checkpoint_lenet-1_1875.ckpt 2、Multi-dataset training requires predefined functions (1)Copy multi-dataset from obs to training image and unzip function C2netMultiObsToEnv(multi_data_url, data_dir) (2)Copy the output to obs function EnvToObs(train_dir, obs_train_url) (2)Download the input from Qizhi And Init function DownloadFromQizhi(multi_data_url, data_dir) (2)Upload the output to Qizhi function UploadToQizhi(train_dir, obs_train_url) 3、4 parameters need to be defined --multi_data_url is the multi-dataset you selected on the Qizhi platform --multi_data_url,--train_url,--device_target,These 3 parameters must be defined first in a multi-dataset task, otherwise an error will be reported. There is no need to add these parameters to the running parameters of the Qizhi platform, because they are predefined in the background, you only need to define them in your code 4、How the dataset is used Multi-datasets use multi_data_url as input, data_dir + dataset name + file or folder name in the dataset as the calling path of the dataset in the training image. For example, the calling path of the train folder in the MNIST_Data dataset in this example is data_dir + "/MNIST_Data" +"/train" For details, please refer to the following sample code. """ import os import argparse import moxing as mox from config import mnist_cfg as cfg from dataset import create_dataset from dataset_distributed import create_dataset_parallel from lenet import LeNet5 import json import mindspore.nn as nn from mindspore import context from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor from mindspore.train import Model from mindspore.nn.metrics import Accuracy from mindspore import load_checkpoint, load_param_into_net from mindspore.context import ParallelMode from mindspore.communication.management import init, get_rank import time ### Copy multiple datasets from obs to training image and unzip### def C2netMultiObsToEnv(multi_data_url, data_dir): #--multi_data_url is json data, need to do json parsing for multi_data_url multi_data_json = json.loads(multi_data_url) for i in range(len(multi_data_json)): zipfile_path = data_dir + "/" + multi_data_json[i]["dataset_name"] try: mox.file.copy(multi_data_json[i]["dataset_url"], zipfile_path) print("Successfully Download {} to {}".format(multi_data_json[i]["dataset_url"],zipfile_path)) #get filename and unzip the dataset filename = os.path.splitext(multi_data_json[i]["dataset_name"])[0] filePath = data_dir + "/" + filename if not os.path.exists(filePath): os.makedirs(filePath) os.system("unzip {} -d {}".format(zipfile_path, filePath)) except Exception as e: print('moxing download {} to {} failed: '.format( multi_data_json[i]["dataset_url"], zipfile_path) + str(e)) #Set a cache file to determine whether the data has been copied to obs. #If this file exists during multi-card training, there is no need to copy the dataset multiple times. f = open("/cache/download_input.txt", 'w') f.close() try: if os.path.exists("/cache/download_input.txt"): print("download_input succeed") except Exception as e: print("download_input failed") return ### Copy the output model to obs ### def EnvToObs(train_dir, obs_train_url): try: mox.file.copy_parallel(train_dir, obs_train_url) print("Successfully Upload {} to {}".format(train_dir, obs_train_url)) except Exception as e: print('moxing upload {} to {} failed: '.format(train_dir, obs_train_url) + str(e)) return def DownloadFromQizhi(multi_data_url, data_dir): device_num = int(os.getenv('RANK_SIZE')) if device_num == 1: C2netMultiObsToEnv(multi_data_url,data_dir) context.set_context(mode=context.GRAPH_MODE,device_target=args.device_target) if device_num > 1: # set device_id and init for multi-card training context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=int(os.getenv('ASCEND_DEVICE_ID'))) context.reset_auto_parallel_context() context.set_auto_parallel_context(device_num = device_num, parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, parameter_broadcast=True) init() #Copying obs data does not need to be executed multiple times, just let the 0th card copy the data local_rank=int(os.getenv('RANK_ID')) if local_rank%8==0: C2netMultiObsToEnv(multi_data_url,data_dir) #If the cache file does not exist, it means that the copy data has not been completed, #and Wait for 0th card to finish copying data while not os.path.exists("/cache/download_input.txt"): time.sleep(1) return def UploadToQizhi(train_dir, obs_train_url): device_num = int(os.getenv('RANK_SIZE')) local_rank=int(os.getenv('RANK_ID')) if device_num == 1: EnvToObs(train_dir, obs_train_url) if device_num > 1: if local_rank%8==0: EnvToObs(train_dir, obs_train_url) return parser = argparse.ArgumentParser(description='MindSpore Lenet Example') ### --multi_data_url,--train_url,--device_target,These 3 parameters must be defined first in a multi-dataset, ### otherwise an error will be reported. ### There is no need to add these parameters to the running parameters of the Qizhi platform, ### because they are predefined in the background, you only need to define them in your code. parser.add_argument('--multi_data_url', help='path to multi dataset', default= '/cache/data/') parser.add_argument('--train_url', help='model folder to save/load', default= '/cache/output/') parser.add_argument( '--device_target', type=str, default="Ascend", choices=['Ascend', 'CPU'], help='device where the code will be implemented (default: Ascend),if to use the CPU on the Qizhi platform:device_target=CPU') parser.add_argument('--epoch_size', type=int, default=5, help='Training epochs.') if __name__ == "__main__": args, unknown = parser.parse_known_args() data_dir = '/cache/data' train_dir = '/cache/output' try: if not os.path.exists(data_dir): os.makedirs(data_dir) if not os.path.exists(train_dir): os.makedirs(train_dir) except Exception as e: print("path already exists") ###Initialize and copy data to training image DownloadFromQizhi(args.multi_data_url, data_dir) ###The dataset path is used here:data_dir + "/MNIST_Data" +"/train" device_num = int(os.getenv('RANK_SIZE')) if device_num == 1: ds_train = create_dataset(os.path.join(data_dir + "/MNISTData", "train"), cfg.batch_size) if device_num > 1: ds_train = create_dataset_parallel(os.path.join(data_dir + "/MNISTData", "train"), cfg.batch_size) if ds_train.get_dataset_size() == 0: raise ValueError( "Please check dataset size > 0 and batch_size <= dataset size") network = LeNet5(cfg.num_classes) net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) time_cb = TimeMonitor(data_size=ds_train.get_dataset_size()) if args.device_target != "Ascend": model = Model(network,net_loss,net_opt,metrics={"accuracy"}) else: model = Model(network, net_loss,net_opt,metrics={"accuracy"},amp_level="O2") config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, keep_checkpoint_max=cfg.keep_checkpoint_max) #Note that this method saves the model file on each card. You need to specify the save path on each card. # In this example, get_rank() is added to distinguish different paths. if device_num == 1: outputDirectory = train_dir if device_num > 1: outputDirectory = train_dir + "/" + str(get_rank()) + "/" ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", directory=outputDirectory, config=config_ck) print("============== Starting Training ==============") epoch_size = cfg['epoch_size'] if (args.epoch_size): epoch_size = args.epoch_size print('epoch_size is: ', epoch_size) # set callback functions callback =[time_cb,LossMonitor()] local_rank=int(os.getenv('RANK_ID')) # for data parallel, only save checkpoint on rank 0 if local_rank==0 : callback.append(ckpoint_cb) model.train(epoch_size, ds_train, callbacks=callback)mnist_mindsporeexample_npu/train_for_multidataset.py000066400000000000000000000236501441612507300236540ustar00rootroot00000000000000""" ######################## multi-dataset train lenet example ######################## This example is a multi-dataset training tutorial. If it is a single dataset, please refer to the single dataset training tutorial train.py. This example cannot be used for a single dataset! """ """ ######################## Instructions for using the training environment ######################## 1、(1)The structure of the dataset uploaded for multi-dataset training in this example MNISTData.zip ├── test └── train checkpoint_lenet-1_1875.zip ├── checkpoint_lenet-1_1875.ckpt (2)The dataset structure in the training image for multiple datasets in this example workroot ├── MNISTData | ├── test | └── train └── checkpoint_lenet-1_1875 ├── checkpoint_lenet-1_1875.ckpt 2、Multi-dataset training requires predefined functions (1)Copy multi-dataset from obs to training image function MultiObsToEnv(multi_data_url, data_dir) (2)Copy the output to obs function EnvToObs(train_dir, obs_train_url) (3)Download the input from Qizhi And Init function DownloadFromQizhi(multi_data_url, data_dir) (4)Upload the output to Qizhi function UploadToQizhi(train_dir, obs_train_url) 3、4 parameters need to be defined --data_url is the first dataset you selected on the Qizhi platform --multi_data_url is the multi-dataset you selected on the Qizhi platform --data_url,--multi_data_url,--train_url,--device_target,These 4 parameters must be defined first in a multi-dataset task, otherwise an error will be reported. There is no need to add these parameters to the running parameters of the Qizhi platform, because they are predefined in the background, you only need to define them in your code 4、How the dataset is used Multi-datasets use multi_data_url as input, data_dir + dataset name + file or folder name in the dataset as the calling path of the dataset in the training image. For example, the calling path of the train folder in the MNIST_Data dataset in this example is data_dir + "/MNIST_Data" +"/train" For details, please refer to the following sample code. """ import os import argparse import moxing as mox from config import mnist_cfg as cfg from dataset import create_dataset from dataset_distributed import create_dataset_parallel from lenet import LeNet5 import json import mindspore.nn as nn from mindspore import context from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor from mindspore.train import Model from mindspore.nn.metrics import Accuracy from mindspore import load_checkpoint, load_param_into_net from mindspore.context import ParallelMode from mindspore.communication.management import init, get_rank import time ### Copy multiple datasets from obs to training image ### def MultiObsToEnv(multi_data_url, data_dir): #--multi_data_url is json data, need to do json parsing for multi_data_url multi_data_json = json.loads(multi_data_url) for i in range(len(multi_data_json)): path = data_dir + "/" + multi_data_json[i]["dataset_name"] if not os.path.exists(path): os.makedirs(path) try: mox.file.copy_parallel(multi_data_json[i]["dataset_url"], path) print("Successfully Download {} to {}".format(multi_data_json[i]["dataset_url"],path)) except Exception as e: print('moxing download {} to {} failed: '.format( multi_data_json[i]["dataset_url"], path) + str(e)) #Set a cache file to determine whether the data has been copied to obs. #If this file exists during multi-card training, there is no need to copy the dataset multiple times. f = open("/cache/download_input.txt", 'w') f.close() try: if os.path.exists("/cache/download_input.txt"): print("download_input succeed") except Exception as e: print("download_input failed") return ### Copy the output model to obs ### def EnvToObs(train_dir, obs_train_url): try: mox.file.copy_parallel(train_dir, obs_train_url) print("Successfully Upload {} to {}".format(train_dir, obs_train_url)) except Exception as e: print('moxing upload {} to {} failed: '.format(train_dir, obs_train_url) + str(e)) return def DownloadFromQizhi(multi_data_url, data_dir): device_num = int(os.getenv('RANK_SIZE')) if device_num == 1: MultiObsToEnv(multi_data_url,data_dir) context.set_context(mode=context.GRAPH_MODE,device_target=args.device_target) if device_num > 1: # set device_id and init for multi-card training context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=int(os.getenv('ASCEND_DEVICE_ID'))) context.reset_auto_parallel_context() context.set_auto_parallel_context(device_num = device_num, parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, parameter_broadcast=True) init() #Copying obs data does not need to be executed multiple times, just let the 0th card copy the data local_rank=int(os.getenv('RANK_ID')) if local_rank%8==0: MultiObsToEnv(multi_data_url,data_dir) #If the cache file does not exist, it means that the copy data has not been completed, #and Wait for 0th card to finish copying data while not os.path.exists("/cache/download_input.txt"): time.sleep(1) return def UploadToQizhi(train_dir, obs_train_url): device_num = int(os.getenv('RANK_SIZE')) local_rank=int(os.getenv('RANK_ID')) if device_num == 1: EnvToObs(train_dir, obs_train_url) if device_num > 1: if local_rank%8==0: EnvToObs(train_dir, obs_train_url) return parser = argparse.ArgumentParser(description='MindSpore Lenet Example') ### --data_url,--multi_data_url,--train_url,--device_target,These 4 parameters must be defined first in a multi-dataset, ### otherwise an error will be reported. ### There is no need to add these parameters to the running parameters of the Qizhi platform, ### because they are predefined in the background, you only need to define them in your code. parser.add_argument('--data_url', help='path to training/inference dataset folder', default= '/cache/data1/') parser.add_argument('--multi_data_url', help='path to multi dataset', default= '/cache/data/') parser.add_argument('--train_url', help='model folder to save/load', default= '/cache/output/') parser.add_argument( '--device_target', type=str, default="Ascend", choices=['Ascend', 'CPU'], help='device where the code will be implemented (default: Ascend),if to use the CPU on the Qizhi platform:device_target=CPU') parser.add_argument('--epoch_size', type=int, default=5, help='Training epochs.') if __name__ == "__main__": args, unknown = parser.parse_known_args() data_dir = '/cache/data' train_dir = '/cache/output' try: if not os.path.exists(data_dir): os.makedirs(data_dir) if not os.path.exists(train_dir): os.makedirs(train_dir) except Exception as e: print("path already exists") ###Initialize and copy data to training image DownloadFromQizhi(args.multi_data_url, data_dir) ###The dataset path is used here:data_dir + "/MNIST_Data" +"/train" device_num = int(os.getenv('RANK_SIZE')) if device_num == 1: ds_train = create_dataset(os.path.join(data_dir + "/MNISTData", "train"), cfg.batch_size) if device_num > 1: ds_train = create_dataset_parallel(os.path.join(data_dir + "/MNISTData", "train"), cfg.batch_size) if ds_train.get_dataset_size() == 0: raise ValueError( "Please check dataset size > 0 and batch_size <= dataset size") network = LeNet5(cfg.num_classes) net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) time_cb = TimeMonitor(data_size=ds_train.get_dataset_size()) ###The dataset path is used here:data_dir + "/checkpoint_lenet-1_1875"+"/checkpoint_lenet-1_1875.ckpt" load_param_into_net(network, load_checkpoint(os.path.join(data_dir + "/checkpoint_lenet-1_1875", "checkpoint_lenet-1_1875.ckpt"))) if args.device_target != "Ascend": model = Model(network,net_loss,net_opt,metrics={"accuracy"}) else: model = Model(network, net_loss,net_opt,metrics={"accuracy"},amp_level="O2") config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, keep_checkpoint_max=cfg.keep_checkpoint_max) #Note that this method saves the model file on each card. You need to specify the save path on each card. # In this example, get_rank() is added to distinguish different paths. if device_num == 1: outputDirectory = train_dir + "/" if device_num > 1: outputDirectory = train_dir + "/" + str(get_rank()) + "/" ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", directory=outputDirectory, config=config_ck) print("============== Starting Training ==============") epoch_size = cfg['epoch_size'] if (args.epoch_size): epoch_size = args.epoch_size print('epoch_size is: ', epoch_size) model.train(epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()]) ###Copy the trained output data from the local running environment back to obs, ###and download it in the training task corresponding to the Qizhi platform UploadToQizhi(train_dir,args.train_url) mnist_mindsporeexample_npu/upload.py000066400000000000000000000011251441612507300203660ustar00rootroot00000000000000from mindspore.train.callback import Callback import moxing as mox class UploadOutput(Callback): def __init__(self, train_dir, obs_train_url): self.train_dir = train_dir self.obs_train_url = obs_train_url def epoch_end(self,run_context): try: mox.file.copy_parallel(self.train_dir , self.obs_train_url ) print("Successfully Upload {} to {}".format(self.train_dir ,self.obs_train_url )) except Exception as e: print('moxing upload {} to {} failed: '.format(self.train_dir ,self.obs_train_url ) + str(e)) return mnist_mindsporeexample_npu/upload_for_c2net.py000066400000000000000000000003301441612507300223240ustar00rootroot00000000000000from mindspore.train.callback import Callback import os class UploadOutput(Callback): def epoch_end(self,run_context): os.system("cd /cache/script_for_grampus/ &&./uploader_for_npu " + "/cache/output/")