141 lines
6.6 KiB
Python
141 lines
6.6 KiB
Python
"""
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示例选用的数据集是MNISTData.zip
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数据集结构是：
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MNISTData.zip
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├── test
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│ ├── t10k-images-idx3-ubyte
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│ └── t10k-labels-idx1-ubyte
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└── train
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├── train-images-idx3-ubyte
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└── train-labels-idx1-ubyte
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######################## Attention! ########################
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使用注意事项：
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1、本示例需要用户定义的参数有--multi_data_url,这个参数任务中是必选的，model_url参数是可选的
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具体的含义如下：
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--multi_data_url是启智平台上选择的数据集的obs路径
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--model_url是训练结果回传到启智平台的obs路径
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2、用户需要调用OpenI.C2NETMultiDatasetToEnv等函数，来实现数据集、预训练模型文件的拷贝
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3、智算网络区别于启智：
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(1)智算的数据集拷贝到训练镜像后需要解压，请使用C2NETMultiDatasetToEnv函数
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(2)智算任务结果不需要用户调用函数回传，需要将结果输出到/cache/output文件夹下，才能在启智平台下载结果
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在某些特殊情况下，若用户想要手动上传结果，可以使用model_url参数回传结果到启智平台，model_url用法与启智集群的train_url参数用法一样
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"""
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import os
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import argparse
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from config import mnist_cfg as cfg
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from dataset import create_dataset
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from dataset_distributed import create_dataset_parallel
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from lenet import LeNet5
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
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from mindspore.train import Model
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from mindspore.context import ParallelMode
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from mindspore.communication.management import init, get_rank
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import time
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from openi import c2net_multidataset_to_env as DatasetToEnv
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from openi import env_to_openi
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from openi import EnvToOpenIEpochEnd
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parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
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parser.add_argument('--multi_data_url',
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help='必选；使用数据集，需要定义的参数',
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default= '[{}]')
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parser.add_argument('--model_url',
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help='可选；需要手动回传结果到启智才需要定义的参数',
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default= '')
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parser.add_argument(
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'--device_target',
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type=str,
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default="Ascend",
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choices=['Ascend', 'CPU'],
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help='device where the code will be implemented (default: Ascend),if to use the CPU on the Qizhi platform:device_target=CPU')
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parser.add_argument('--epoch_size',
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type=int,
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default=5,
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help='Training epochs.')
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if __name__ == "__main__":
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args, unknown = parser.parse_known_args()
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data_dir = '/cache/data'
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train_dir = '/cache/output'
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if not os.path.exists(data_dir):
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os.makedirs(data_dir)
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if not os.path.exists(train_dir):
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os.makedirs(train_dir)
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###Initialize and copy data to training image
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#DatasetToEnv(args.multi_data_url, data_dir)
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device_num = int(os.getenv('RANK_SIZE'))
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#使用单卡时
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if device_num == 1:
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DatasetToEnv(args.multi_data_url,data_dir)
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context.set_context(mode=context.GRAPH_MODE,device_target=args.device_target)
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#使用数据集的方式
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ds_train = create_dataset(os.path.join(data_dir + "/MNISTData", "train"), cfg.batch_size)
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#使用多卡时
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if device_num > 1:
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# set device_id and init for multi-card training
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=int(os.getenv('ASCEND_DEVICE_ID')))
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(device_num = device_num, parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, parameter_broadcast=True)
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init()
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#Copying obs data does not need to be executed multiple times, just let the 0th card copy the data
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local_rank=int(os.getenv('RANK_ID'))
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if local_rank%8==0:
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DatasetToEnv(args.multi_data_url,data_dir)
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#Set a cache file to determine whether the data has been copied to obs.
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#If this file exists during multi-card training, there is no need to copy the dataset multiple times.
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f = open("/cache/download_input.txt", 'w')
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f.close()
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try:
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if os.path.exists("/cache/download_input.txt"):
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print("download_input succeed")
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except Exception as e:
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print("download_input failed")
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while not os.path.exists("/cache/download_input.txt"):
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time.sleep(1)
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ds_train = create_dataset_parallel(os.path.join(data_dir + "/MNISTData", "train"), cfg.batch_size)
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network = LeNet5(cfg.num_classes)
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net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
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time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
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if args.device_target != "Ascend":
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model = Model(network,net_loss,net_opt,metrics={"accuracy"})
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else:
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model = Model(network, net_loss,net_opt,metrics={"accuracy"},amp_level="O2")
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config_ck = CheckpointConfig(save_checkpoint_steps=int(cfg.save_checkpoint_steps / device_num),
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keep_checkpoint_max=cfg.keep_checkpoint_max)
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#Note that this method saves the model file on each card. You need to specify the save path on each card.
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# In this example, get_rank() is added to distinguish different paths.
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if device_num == 1:
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outputDirectory = train_dir
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if device_num > 1:
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outputDirectory = train_dir + "/" + str(get_rank()) + "/"
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ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
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directory=outputDirectory,
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config=config_ck)
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print("============== Starting Training ==============")
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epoch_size = cfg['epoch_size']
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if (args.epoch_size):
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epoch_size = args.epoch_size
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print('epoch_size is: ', epoch_size)
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# set callback functions
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callback =[time_cb,LossMonitor()]
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local_rank=int(os.getenv('RANK_ID'))
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#非必选，每个epoch结束后，都手动上传训练结果到启智平台，注意这样使用会占用很多内存，只有在部分特殊需要手动上传的任务才需要使用
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uploadOutput = EnvToOpenIEpochEnd(train_dir,args.model_url)
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callback.append(uploadOutput)
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# for data parallel, only save checkpoint on rank 0
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if local_rank==0 :
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callback.append(ckpoint_cb)
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model.train(epoch_size,ds_train,callbacks=callback) |