[Bug]:使用model-zoo上的slowfast模型跑训练，对于小批量数据单卡和8卡都可以，但是对于大批量150G的数据8卡训练报错

### 问题描述

使用model-zoo上的slowfast模型跑训练，对于小批量数据单卡和8卡都可以，但是对于大批量150G的数据8卡训练报错

### 环境信息

- **Hardware Environment(`Ascend`/`GPU`/`CPU`)  / 硬件环境**: Ascend
> Please delete the backend not involved / 请删除不涉及的后端:
> /device Ascend

- **Software Environment / 软件环境 (Mandatory / 必填)**:
-- MindSpore version (e.g., 2.0.0) :2.1.0
-- Python version (e.g., Python 3.7.5) :3.9.5
-- OS platform and distribution (e.g., Linux Ubuntu 16.04):Euler2.8.3
-- GCC/Compiler version (if compiled from source):

- **Execute Mode / 执行模式 (Mandatory / 必填)(`PyNative`/`Graph`)**: PyNative
> Please delete the mode not involved / 请删除不涉及的模式:
> /mode pynative

### 关联用例

https://gitee.com/mindspore/models/tree/master/research/cv/slowfast#%E8%AE%AD%E7%BB%83%E8%BF%87%E7%A8%8B

### 重现步骤

1.代码为model-zoo上代码，https://gitee.com/mindspore/models/tree/master/research/cv/slowfast#%E8%AE%AD%E7%BB%83%E8%BF%87%E7%A8%8B
2.数据集为客户自定义数据据，大小由150G
3.参考的文档是model-zoo上的文档，按步骤来操作的
4.使用分布式8卡训练，报错EI0002:

### 预期结果

8卡分布式训练能够正常运行，生成相应的权重文件

### 日志/截图

[图片上传中…(image-LjUtxE0Mi031aprP4eZ7)]

[12/12 11:33:15][INFO] train.py:  96: AUG:
  AA_TYPE: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  ENABLE: False
  INTERPOLATION: bicubic
  NUM_SAMPLE: 1
  RE_COUNT: 1
  RE_MODE: pixel
  RE_PROB: 0.25
  RE_SPLIT: False
AVA:
  ANNOTATION_DIR: /home/ma-user/work/slowfast/data/ava/ava_annotations
  BGR: False
  DETECTION_SCORE_THRESH: 0.8
  EXCLUSION_FILE: ava_val_excluded_timestamps_v2.2.csv
  FRAME_DIR: /home/ma-user/work/slowfast/data/ava/rawframes
  FRAME_LIST_DIR: /home/ma-user/work/slowfast/data/ava/ava_annotations
  FULL_TEST_ON_VAL: False
  GROUNDTRUTH_FILE: ava_val_v2.2.csv
  IMG_PROC_BACKEND: cv2
  LABEL_MAP_FILE: ava_action_list_v2.1_for_activitynet_2018.pbtxt
  TEST_FORCE_FLIP: False
  TEST_LISTS: ['val.csv']
  TEST_PREDICT_BOX_LISTS: ['ava_detection_val_boxes_and_labels.csv']
  TRAIN_GT_BOX_LISTS: ['ava_train_v2.2.csv']
  TRAIN_LISTS: ['train.csv']
  TRAIN_PCA_JITTER_ONLY: True
  TRAIN_PREDICT_BOX_LISTS: ['ava_train_v2.2.csv', 'ava_detection_train_boxes_and_labels_include_negative_v2.2.csv']
  TRAIN_USE_COLOR_AUGMENTATION: False
BENCHMARK:
  LOG_PERIOD: 100
  NUM_EPOCHS: 5
  SHUFFLE: True
BN:
  NORM_TYPE: batchnorm
  NUM_BATCHES_PRECISE: 200
  NUM_SPLITS: 1
  NUM_SYNC_DEVICES: 1
  USE_PRECISE_STATS: False
  WEIGHT_DECAY: 0.0
DATA:
  DECODING_BACKEND: pyav
  ENSEMBLE_METHOD: sum
  INPUT_CHANNEL_NUM: [3, 3]
  INV_UNIFORM_SAMPLE: False
  MAX_NUM_BOXES_PER_FRAME: 28
  MEAN: [0.45, 0.45, 0.45]
  MULTI_LABEL: False
  NUM_FRAMES: 32
  PATH_LABEL_SEPARATOR:  
  PATH_PREFIX: 
  PATH_TO_DATA_DIR: 
  PATH_TO_PRELOAD_IMDB: 
  RANDOM_FLIP: True
  REVERSE_INPUT_CHANNEL: False
  SAMPLING_RATE: 2
  STD: [0.225, 0.225, 0.225]
  TARGET_FPS: 25
  TEST_CROP_SIZE: 224
  TEST_SCALE_HEIGHT: 256
  TEST_SCALE_WIDTH: 384
  TRAIN_CROP_SIZE: 224
  TRAIN_JITTER_ASPECT_RELATIVE: []
  TRAIN_JITTER_MOTION_SHIFT: False
  TRAIN_JITTER_SCALES: [256, 320]
  TRAIN_JITTER_SCALES_RELATIVE: []
  TRAIN_PCA_EIGVAL: [0.225, 0.224, 0.229]
  TRAIN_PCA_EIGVEC: [[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.814], [-0.5836, -0.6948, 0.4203]]
  USE_OFFSET_SAMPLING: False
DATA_LOADER:
  ENABLE_MULTI_THREAD_DECODE: False
  NUM_WORKERS: 2
  PIN_MEMORY: True
DEMO:
  BUFFER_SIZE: 0
  CLIP_VIS_SIZE: 10
  COMMON_CLASS_NAMES: ['watch (a person)', 'talk to (e.g., self, a person, a group)', 'listen to (a person)', 'touch (an object)', 'carry/hold (an object)', 'walk', 'sit', 'lie/sleep', 'bend/bow (at the waist)']
  COMMON_CLASS_THRES: 0.7
  DETECTRON2_CFG: COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml
  DETECTRON2_THRESH: 0.9
  DETECTRON2_WEIGHTS: detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl
  DISPLAY_HEIGHT: 0
  DISPLAY_WIDTH: 0
  ENABLE: False
  FPS: 25
  GT_BOXES: 
  INPUT_FORMAT: BGR
  INPUT_VIDEO: 
  LABEL_FILE_PATH: 
  NUM_CLIPS_SKIP: 0
  NUM_VIS_INSTANCES: 2
  OUTPUT_FILE: 
  OUTPUT_FPS: -1
  PREDS_BOXES: 
  SLOWMO: 1
  STARTING_SECOND: 0
  THREAD_ENABLE: False
  UNCOMMON_CLASS_THRES: 0.3
  VIS_MODE: thres
  WEBCAM: -1
DETECTION:
  ALIGNED: True
  ENABLE: True
  ROI_XFORM_RESOLUTION: 7
  SPATIAL_SCALE_FACTOR: 16
LOG_MODEL_INFO: True
LOG_PERIOD: 1
MIXUP:
  ALPHA: 0.8
  CUTMIX_ALPHA: 1.0
  ENABLE: False
  LABEL_SMOOTH_VALUE: 0.1
  PROB: 1.0
  SWITCH_PROB: 0.5
MODEL:
  ACT_CHECKPOINT: False
  ARCH: slowfast
  DROPCONNECT_RATE: 0.0
  DROPOUT_RATE: 0.5
  FC_INIT_STD: 0.01
  HEAD_ACT: sigmoid
  LOSS_FUNC: bce
  MODEL_NAME: SlowFast
  MULTI_PATHWAY_ARCH: ['slowfast']
  NUM_CLASSES: 80
  SINGLE_PATHWAY_ARCH: ['2d', 'c2d', 'i3d', 'slow', 'x3d', 'mvit']
MULTIGRID:
  BN_BASE_SIZE: 8
  DEFAULT_B: 0
  DEFAULT_S: 0
  DEFAULT_T: 0
  EPOCH_FACTOR: 1.5
  EVAL_FREQ: 3
  LONG_CYCLE: False
  LONG_CYCLE_FACTORS: [(0.25, 0.7071067811865476), (0.5, 0.7071067811865476), (0.5, 1), (1, 1)]
  LONG_CYCLE_SAMPLING_RATE: 0
  SHORT_CYCLE: False
  SHORT_CYCLE_FACTORS: [0.5, 0.7071067811865476]
MVIT:
  CLS_EMBED_ON: True
  DEPTH: 16
  DIM_MUL: []
  DROPOUT_RATE: 0.0
  DROPPATH_RATE: 0.1
  EMBED_DIM: 96
  HEAD_MUL: []
  MLP_RATIO: 4.0
  MODE: conv
  NORM: layernorm
  NORM_STEM: False
  NUM_HEADS: 1
  PATCH_2D: False
  PATCH_KERNEL: [3, 7, 7]
  PATCH_PADDING: [2, 4, 4]
  PATCH_STRIDE: [2, 4, 4]
  POOL_FIRST: False
  POOL_KVQ_KERNEL: None
  POOL_KV_STRIDE: None
  POOL_KV_STRIDE_ADAPTIVE: None
  POOL_Q_STRIDE: []
  QKV_BIAS: True
  SEP_POS_EMBED: False
  ZERO_DECAY_POS_CLS: True
NONLOCAL:
  GROUP: [[1, 1], [1, 1], [1, 1], [1, 1]]
  INSTANTIATION: dot_product
  LOCATION: [[[], []], [[], []], [[], []], [[], []]]
  POOL: [[[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]]]
NUM_GPUS: 8
NUM_SHARDS: 1
OUTPUT_DIR: .
RESNET:
  DEPTH: 50
  INPLACE_RELU: True
  NUM_BLOCK_TEMP_KERNEL: [[3, 3], [4, 4], [6, 6], [3, 3]]
  NUM_GROUPS: 1
  SPATIAL_DILATIONS: [[1, 1], [1, 1], [1, 1], [2, 2]]
  SPATIAL_STRIDES: [[1, 1], [2, 2], [2, 2], [1, 1]]
  STRIDE_1X1: False
  TRANS_FUNC: bottleneck_transform
  WIDTH_PER_GROUP: 64
  ZERO_INIT_FINAL_BN: True
RNG_SEED: 0
SHARD_ID: 0
SLOWFAST:
  ALPHA: 4
  BETA_INV: 8
  FUSION_CONV_CHANNEL_RATIO: 2
  FUSION_KERNEL_SZ: 7
SOLVER:
  BASE_LR: 0.15
  BASE_LR_SCALE_NUM_SHARDS: False
  CLIP_GRAD_L2NORM: None
  CLIP_GRAD_VAL: None
  COSINE_AFTER_WARMUP: False
  COSINE_END_LR: 0.0
  DAMPENING: 0.0
  GAMMA: 0.1
  LRS: [1, 0.1, 0.01, 0.001]
  LR_POLICY: steps_with_relative_lrs
  MAX_EPOCH: 20
  MOMENTUM: 0.9
  NESTEROV: False
  OPTIMIZING_METHOD: sgd
  STEPS: [0, 10, 15, 20]
  STEP_SIZE: 1
  WARMUP_EPOCHS: 5.0
  WARMUP_FACTOR: 0.1
  WARMUP_START_LR: 0.000125
  WEIGHT_DECAY: 1e-07
  ZERO_WD_1D_PARAM: False
TENSORBOARD:
  CATEGORIES_PATH: 
  CLASS_NAMES_PATH: 
  CONFUSION_MATRIX:
    ENABLE: False
    FIGSIZE: [8, 8]
    SUBSET_PATH: 
  ENABLE: False
  HISTOGRAM:
    ENABLE: False
    FIGSIZE: [8, 8]
    SUBSET_PATH: 
    TOPK: 10
  LOG_DIR: 
  MODEL_VIS:
    ACTIVATIONS: False
    COLORMAP: Pastel2
    ENABLE: False
    GRAD_CAM:
      COLORMAP: viridis
      ENABLE: True
      LAYER_LIST: []
      USE_TRUE_LABEL: False
    INPUT_VIDEO: False
    LAYER_LIST: []
    MODEL_WEIGHTS: False
    TOPK_PREDS: 1
  PREDICTIONS_PATH: 
  WRONG_PRED_VIS:
    ENABLE: False
    SUBSET_PATH: 
    TAG: Incorrectly classified videos.
TEST:
  BATCH_SIZE: 8
  CHECKPOINT_FILE_PATH: 
  CHECKPOINT_TYPE: pytorch
  DATASET: ava
  ENABLE: True
  NUM_ENSEMBLE_VIEWS: 10
  NUM_SPATIAL_CROPS: 3
  SAVE_BINS_RESULTS_PATH: ./preprocess_Result
  SAVE_RESULTS_PATH: 
TRAIN:
  AUTO_RESUME: True
  BATCH_SIZE: 8
  CHECKPOINT_CLEAR_NAME_PATTERN: ()
  CHECKPOINT_EPOCH_RESET: False
  CHECKPOINT_FILE_PATH: /home/ma-user/work/slowfast/SLOWFAST_8x8_R50.pkl.ckpt
  CHECKPOINT_INFLATE: False
  CHECKPOINT_PERIOD: 1
  CHECKPOINT_TYPE: caffe2
  DATASET: ava
  ENABLE: True
  EVAL_PERIOD: 5
  MIXED_PRECISION: False
X3D:
  BN_LIN5: False
  BOTTLENECK_FACTOR: 1.0
  CHANNELWISE_3x3x3: True
  DEPTH_FACTOR: 1.0
  DIM_C1: 12
  DIM_C5: 2048
  SCALE_RES2: False
  WIDTH_FACTOR: 1.0
[12/12 11:33:18][INFO] ava_helper.py:  74: Finished loading image paths from: ['/home/ma-user/work/slowfast/data/ava/ava_annotations/train.csv'],len is 346
******************** ['/home/ma-user/work/slowfast/data/ava/ava_annotations/ava_train_v2.2.csv', '/home/ma-user/work/slowfast/data/ava/ava_annotations/ava_train_v2.2.csv', '/home/ma-user/work/slowfast/data/ava/ava_annotations/ava_detection_train_boxes_and_labels_include_negative_v2.2.csv'] [True, False, False] 0.8 1
[12/12 11:33:19][INFO] ava_helper.py: 126: Finished loading annotations from: ['/home/ma-user/work/slowfast/data/ava/ava_annotations/ava_train_v2.2.csv', '/home/ma-user/work/slowfast/data/ava/ava_annotations/ava_train_v2.2.csv', '/home/ma-user/work/slowfast/data/ava/ava_annotations/ava_detection_train_boxes_and_labels_include_negative_v2.2.csv'],
[12/12 11:33:19][INFO] ava_helper.py: 129: Detection threshold: 0.8
[12/12 11:33:19][INFO] ava_helper.py: 130: Number of unique boxes: 20357
[12/12 11:33:19][INFO] ava_helper.py: 131: Number of annotations: 59989
[12/12 11:33:19][INFO] ava_helper.py: 176: 20253 keyframes used.
[12/12 11:33:19][INFO] ava_dataset.py: 111: === AVA dataset summary ===
[12/12 11:33:19][INFO] ava_dataset.py: 112: Split: train
[12/12 11:33:19][INFO] ava_dataset.py: 113: Number of videos: 346
[12/12 11:33:19][INFO] ava_dataset.py: 117: Number of frames: 533209
[12/12 11:33:19][INFO] ava_dataset.py: 118: Number of key frames: <src.datasets.ava_dataset.Ava object at 0xffff41857340>
[12/12 11:33:19][INFO] ava_dataset.py: 119: Number of boxes: 20357.
loading /home/ma-user/work/slowfast/SLOWFAST_8x8_R50.pkl.ckpt
[WARNING] ME(299854:281473426987936,MainProcess):2023-12-12-11:33:22.584.587 [mindspore/train/serialization.py:1237] For 'load_param_into_net', 2 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint.
[WARNING] ME(299854:281473426987936,MainProcess):2023-12-12-11:33:22.584.805 [mindspore/train/serialization.py:1242] net.head.projection.weight is not loaded.
[WARNING] ME(299854:281473426987936,MainProcess):2023-12-12-11:33:22.584.874 [mindspore/train/serialization.py:1242] net.head.projection.bias is not loaded.
[12/12 11:33:23][INFO] train.py: 134: ============== Starting Training ==============
[12/12 11:33:23][INFO] train.py: 135: total_epoch=20, steps_per_epoch=317
Training.20
<mindspore.dataset.engine.datasets.BatchDataset object at 0xffff41895610>
[WARNING] CORE(299854,ffffa3a11ba0,python):2023-12-12-11:33:23.545.398 [mindspore/core/utils/numa_interface.cc:145] NumaBind] Try to bind numa id: 0, but execute set_mempolicy failed, errno: Operation not permitted. Please use mindspore.dataset.config.set_numa_enable(False) to disable numa bind.
[WARNING] CORE(299854,ffffa3a11ba0,python):2023-12-12-11:33:26.596.314 [mindspore/core/utils/numa_interface.cc:145] NumaBind] Try to bind numa id: 0, but execute set_mempolicy failed, errno: Operation not permitted. Please use mindspore.dataset.config.set_numa_enable(False) to disable numa bind.
[WARNING] CORE(299854,ffffa3a11ba0,python):2023-12-12-11:33:57.176.550 [mindspore/core/utils/numa_interface.cc:145] NumaBind] Try to bind numa id: 0, but execute set_mempolicy failed, errno: Operation not permitted. Please use mindspore.dataset.config.set_numa_enable(False) to disable numa bind.
[WARNING] MD(299854,fffba3fff1e0,python):2023-12-12-11:38:20.901.966 [mindspore/ccsrc/minddata/dataset/engine/datasetops/data_queue_op.cc:912] DetectPerBatchTime] Bad performance attention, it takes more than 25 seconds to fetch a batch of data from dataset pipeline, which might result `GetNext` timeout problem. You may test dataset processing performance(with creating dataset iterator) and optimize it.
[WARNING] DEVICE(299854,fffd2cff91e0,python):2023-12-12-12:04:07.125.519 [mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_kernel_runtime.cc:801] GetDumpPath] The environment variable 'MS_OM_PATH' is not set, the files will save to the process local path, as ./rank_id/node_dump/...
[ERROR] DEVICE(299854,fffd2cff91e0,python):2023-12-12-12:04:07.125.659 [mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_kernel_runtime.cc:819] PrintDebugInfoAndDumpFailNode] Task fail infos, rt task_id: 14, rt stream_id: 7, tid: 299854, device_id: 0, retcode: 507011 ( model execute failed)
[WARNING] DEVICE(299854,fffd2cff91e0,python):2023-12-12-12:04:07.129.962 [mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_kernel_runtime.cc:801] GetDumpPath] The environment variable 'MS_OM_PATH' is not set, the files will save to the process local path, as ./rank_id/exec_order/...
[ERROR] DEVICE(299854,fffd2cff91e0,python):2023-12-12-12:04:07.220.624 [mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_kernel_runtime.cc:906] DumpDebugInfoFile] Execute order has saved at /home/ma-user/work/slowfast/train_parallel0/rank_0/exec_order/kernel_graph_1.csv
Traceback (most recent call last):
  File "/home/ma-user/work/slowfast/train_parallel0/train.py", line 139, in <module>
    train()
  File "/home/ma-user/work/slowfast/train_parallel0/train.py", line 136, in train
    model.train(cfg.SOLVER.MAX_EPOCH, dataset, callbacks=callbacks, dataset_sink_mode=bool(args.dataset_sink_mode))
  File "/home/ma-user/anaconda3/envs/python3.9/lib/python3.9/site-packages/mindspore/train/model.py", line 1063, in train
    self._train(epoch,
  File "/home/ma-user/anaconda3/envs/python3.9/lib/python3.9/site-packages/mindspore/train/model.py", line 113, in wrapper
    func(self, *args, **kwargs)
  File "/home/ma-user/anaconda3/envs/python3.9/lib/python3.9/site-packages/mindspore/train/model.py", line 621, in _train
    self._train_dataset_sink_process(epoch, train_dataset, list_callback,
  File "/home/ma-user/anaconda3/envs/python3.9/lib/python3.9/site-packages/mindspore/train/model.py", line 705, in _train_dataset_sink_process
    outputs = train_network(*inputs)
  File "/home/ma-user/anaconda3/envs/python3.9/lib/python3.9/site-packages/mindspore/nn/cell.py", line 639, in __call__
    out = self.compile_and_run(*args, **kwargs)
  File "/home/ma-user/anaconda3/envs/python3.9/lib/python3.9/site-packages/mindspore/nn/cell.py", line 966, in compile_and_run
    return _cell_graph_executor(self, *new_args, phase=self.phase)
  File "/home/ma-user/anaconda3/envs/python3.9/lib/python3.9/site-packages/mindspore/common/api.py", line 1674, in __call__
    return self.run(obj, *args, phase=phase)
  File "/home/ma-user/anaconda3/envs/python3.9/lib/python3.9/site-packages/mindspore/common/api.py", line 1713, in run
    return self._exec_pip(obj, *args, phase=phase_real)
  File "/home/ma-user/anaconda3/envs/python3.9/lib/python3.9/site-packages/mindspore/common/api.py", line 106, in wrapper
    results = fn(*arg, **kwargs)
  File "/home/ma-user/anaconda3/envs/python3.9/lib/python3.9/site-packages/mindspore/common/api.py", line 1693, in _exec_pip
    return self._graph_executor(args, phase)
RuntimeError: Run task for graph:kernel_graph_1 error! The details refer to 'Ascend Error Message'.

----------------------------------------------------
- Ascend Error Message:
----------------------------------------------------
EI0002: The wait execution of the Notify register times out. Reason: The Notify register has not received the Notify record from remote rank [4].base information: [streamID:[7], taskID[14], taskType[Notify Wait], tag[HcomAllReduce_6629421139219749105_0].] task information: [notify id:[0x00000000000000a0], stage:[ffffffff], remote rank:[4].]
        Possible Cause: 1. An exception occurs during the execution on some NPUs in the cluster. As a result, collective communication operation failed.2. The execution speed on some NPU in the cluster is too slow to complete a communication operation within the timeout interval. (default 1800s, You can set the interval by using HCCL_EXEC_TIMEOUT.)3. The number of training samples of each NPU is inconsistent.4. Packet loss or other connectivity problems occur on the communication link.
        Solution: 1. If this error is reported on part of these ranks, check other ranks to see whether other errors have been reported earlier.2. If this error is reported for all ranks, check whether the error reporting time is consistent (the maximum difference must not exceed 1800s). If not, locate the cause or adjust the locate the cause or set the HCCL_EXEC_TIMEOUT environment variable to a larger value.3. Check whether the completion queue element (CQE) of the error exists in the plog(grep -rn 'error cqe'). If so, check the network connection status. (For details, see the TLS command and HCCN connectivity check examples.)4. Ensure that the number of training samples of each NPU is consistent. For details:https://www.hiascend.com/document
        TraceBack (most recent call last):
        Notify wait execute failed, device_id=0, stream_id=7, task_id=14, flip_num=0, notify_id=20[FUNC:GetError][FILE:stream.cc][LINE:1418]
        rtStreamSynchronize execute failed, reason=[the model stream execute failed][FUNC:FuncErrorReason][FILE:error_message_manage.cc][LINE:50]

(Please search "Ascend Error Message" at https://www.mindspore.cn for error code description)

----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/ccsrc/plugin/device/ascend/hal/hardware/ascend_graph_executor.cc:296 RunGraph

[WARNING] MD(299854,ffffa3a11ba0,python):2023-12-12-12:04:09.257.359 [mindspore/ccsrc/minddata/dataset/engine/datasetops/data_queue_op.cc:115] ~DataQueueOp] 
preprocess_batch: 28;
batch_queue: 0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
            push_start_time -> push_end_time
2023-12-12-11:38:49.190.646 -> 2023-12-12-11:38:49.206.462
2023-12-12-11:38:49.704.597 -> 2023-12-12-11:38:49.717.272
2023-12-12-11:38:53.740.464 -> 2023-12-12-11:38:53.753.396
2023-12-12-11:38:54.816.448 -> 2023-12-12-11:38:54.838.486
2023-12-12-11:38:58.286.482 -> 2023-12-12-11:38:58.301.204
2023-12-12-11:38:59.418.431 -> 2023-12-12-11:38:59.432.061
2023-12-12-11:39:02.766.988 -> 2023-12-12-11:39:02.783.474
2023-12-12-11:39:03.989.076 -> 2023-12-12-11:39:04.003.052
2023-12-12-11:39:06.062.494 -> 2023-12-12-11:39:06.085.283
2023-12-12-11:39:07.613.613 -> 2023-12-12-11:39:07.626.919
For more details, please refer to the FAQ at https://www.mindspore.cn/docs/en/master/faq/data_processing.html.

### 备注

麻烦帮忙看一下，谢谢

MindSpore/models

内容风险标识

评论 (4)

MindSpore/models .gitee-modal { width: 500px !important; }

内容风险标识