def _get_config_with_export_list(
     self,
     task_class: Type[NewTask],
     model_class: Type[Model],
     test_file_metadata: TestFileMetadata,
 ) -> PyTextConfig:
     return PyTextConfig(
         task=task_class.Config(
             data=Data.Config(
                 source=TSVDataSource.Config(
                     train_filename=test_file_metadata.filename,
                     eval_filename=test_file_metadata.filename,
                     test_filename=test_file_metadata.filename,
                     field_names=test_file_metadata.field_names,
                 ),
                 batcher=PoolingBatcher.Config(train_batch_size=1,
                                               test_batch_size=1),
             ),
             trainer=TaskTrainer.Config(epochs=1),
             model=model_class.Config(
                 inputs=type(model_class.Config.inputs)(
                     dense=FloatListTensorizer.Config(
                         column=test_file_metadata.dense_col_name,
                         error_check=True,
                         dim=test_file_metadata.dense_feat_dim,
                     ))),
         ),
         use_tensorboard=False,
         use_cuda_if_available=False,
         export=ExportConfig(
             export_torchscript_path="/tmp/model_torchscript.pt"),
         version=LATEST_VERSION,
     )
Example #2
0
 def _get_pytext_config(
     self,
     test_file_name: TestFileName,
     task_class: Type[NewTask],
     model_class: Type[Model],
 ) -> PyTextConfig:
     test_file_metadata = get_test_file_metadata(test_file_name)
     return PyTextConfig(
         task=task_class.Config(
             data=Data.Config(
                 source=TSVDataSource.Config(
                     train_filename=test_file_metadata.filename,
                     eval_filename=test_file_metadata.filename,
                     test_filename=test_file_metadata.filename,
                     field_names=test_file_metadata.field_names,
                 ),
                 batcher=Batcher.Config(
                 ),  # Use Batcher to avoid shuffling.
             ),
             trainer=TaskTrainer.Config(epochs=1),
             model=model_class.Config(
                 inputs=type(model_class.Config.inputs)(
                     dense=FloatListTensorizer.Config(
                         column=test_file_metadata.dense_col_name,
                         dim=test_file_metadata.dense_feat_dim,
                     ))),
         ),
         use_tensorboard=False,
         use_cuda_if_available=False,
         version=LATEST_VERSION,
     )
Example #3
0
 def __init__(
     self,
     data: Data,
     model: BaseModel,
     metric_reporter: Optional[MetricReporter] = None,
     trainer: Optional[TaskTrainer] = None,
 ):
     self.data = data
     self.model = model
     # Attempt to build a default metric reporter
     self.metric_reporter = metric_reporter or self.create_metric_reporter(
         self.Config.metric_reporter, model)
     self.trainer = trainer or TaskTrainer()
Example #4
0
 class Config(ConfigBase):
     data: Data.Config = Data.Config()
     trainer: TaskTrainer.Config = TaskTrainer.Config()
     # TODO: deprecate this
     use_elastic: Optional[bool] = None
Example #5
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 class Config(ConfigBase):
     data: Data.Config = Data.Config()
     trainer: TaskTrainer.Config = TaskTrainer.Config()
Example #6
0
 class Config(ConfigBase):
     data: Data.Config = Data.Config()
     trainer: TaskTrainer.Config = TaskTrainer.Config()
     use_elastic: Optional[bool] = None