revert stat collection

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10780767

modelscope/hub/utils/utils.py

@@ -87,16 +87,3 @@ def file_integrity_validation(file_path, expected_sha256):
         msg = 'File %s integrity check failed, the download may be incomplete, please try again.' % file_path
         logger.error(msg)
         raise FileIntegrityError(msg)
-
-
-def create_library_statistics(method: str, name: str, cn_name: Optional[str]):
-    try:
-        from modelscope.hub.api import ModelScopeConfig
-        path = f'{get_endpoint()}/api/v1/statistics/library'
-        headers = {'user-agent': ModelScopeConfig.get_user_agent()}
-        params = {'Method': method, 'Name': name, 'CnName': cn_name}
-        r = requests.post(path, params=params, headers=headers)
-        r.raise_for_status()
-    except Exception:
-        pass
-    return

modelscope/pipelines/base.py

@@ -10,7 +10,6 @@ from typing import Any, Dict, Generator, List, Mapping, Union
 
 import numpy as np
 
-from modelscope.hub.utils.utils import create_library_statistics
 from modelscope.models.base import Model
 from modelscope.msdatasets import MsDataset
 from modelscope.outputs import TASK_OUTPUTS
@@ -152,9 +151,6 @@ class Pipeline(ABC):
                  **kwargs) -> Union[Dict[str, Any], Generator]:
         # model provider should leave it as it is
         # modelscope library developer will handle this function
-        for single_model in self.models:
-            if hasattr(single_model, 'name'):
-                create_library_statistics('pipeline', single_model.name, None)
         # place model to cpu or gpu
         if (self.model or (self.has_multiple_models and self.models[0])):
             if not self._model_prepare:

modelscope/trainers/trainer.py

@@ -15,7 +15,6 @@ from torch.utils.data.dataloader import default_collate
 from torch.utils.data.distributed import DistributedSampler
 
 from modelscope.hub.snapshot_download import snapshot_download
-from modelscope.hub.utils.utils import create_library_statistics
 from modelscope.metainfo import Trainers
 from modelscope.metrics import build_metric, task_default_metrics
 from modelscope.models.base import Model, TorchModel
@@ -437,8 +436,6 @@ class EpochBasedTrainer(BaseTrainer):
 
     def train(self, checkpoint_path=None, *args, **kwargs):
         self._mode = ModeKeys.TRAIN
-        if hasattr(self.model, 'name'):
-            create_library_statistics('train', self.model.name, None)
 
         if self.train_dataset is None:
             self.train_dataloader = self.get_train_dataloader()
@@ -459,8 +456,6 @@ class EpochBasedTrainer(BaseTrainer):
         self.train_loop(self.train_dataloader)
 
     def evaluate(self, checkpoint_path=None):
-        if hasattr(self.model, 'name'):
-            create_library_statistics('evaluate', self.model.name, None)
         if checkpoint_path is not None and os.path.isfile(checkpoint_path):
             from modelscope.trainers.hooks import CheckpointHook
             CheckpointHook.load_checkpoint(checkpoint_path, self)