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Add pipeline_controller.connect_configuration
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@ -905,6 +905,44 @@ class PipelineController(object):
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self._task.close()
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self._task.close()
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self._task.reset()
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self._task.reset()
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def connect_configuration(self, configuration, name=None, description=None):
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# type: (Union[Mapping, list, Path, str], Optional[str], Optional[str]) -> Union[dict, Path, str]
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"""
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Connect a configuration dictionary or configuration file (pathlib.Path / str) to a the PipelineController object.
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This method should be called before reading the configuration file.
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For example, a local file:
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.. code-block:: py
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config_file = pipe.connect_configuration(config_file)
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my_params = json.load(open(config_file,'rt'))
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A parameter dictionary/list:
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.. code-block:: py
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my_params = pipe.connect_configuration(my_params)
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:param configuration: The configuration. This is usually the configuration used in the model training process.
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Specify one of the following:
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- A dictionary/list - A dictionary containing the configuration. ClearML stores the configuration in
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the **ClearML Server** (backend), in a HOCON format (JSON-like format) which is editable.
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- A ``pathlib2.Path`` string - A path to the configuration file. ClearML stores the content of the file.
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A local path must be relative path. When executing a pipeline remotely in a worker, the contents brought
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from the **ClearML Server** (backend) overwrites the contents of the file.
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:param str name: Configuration section name. default: 'General'
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Allowing users to store multiple configuration dicts/files
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:param str description: Configuration section description (text). default: None
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:return: If a dictionary is specified, then a dictionary is returned. If pathlib2.Path / string is
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specified, then a path to a local configuration file is returned. Configuration object.
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"""
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return self._task.connect_configuration(configuration, name=name, description=description)
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@classmethod
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@classmethod
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def get_logger(cls):
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def get_logger(cls):
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# type: () -> Logger
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# type: () -> Logger
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