deflog_execution_metrics_from_client(self,metrics_name:str,custom_properties:t.Optional[t.Dict]=None)->mlpb.Artifact:""" Logs execution metrics from a client. Data from pre-existing metrics from client side is used to create identical metrics on server side.
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Source code in cmflib/cmf.py
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deflog_execution_metrics(
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deflog_execution_metrics(self,metrics_name:str,custom_properties:t.Optional[t.Dict]=None)->mlpb.Artifact:"""Log the metadata associated with the execution (coarse-grained tracking).
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Source code in cmflib/cmf.py
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deflog_metric(
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deflog_metric(self,metrics_name:str,custom_properties:t.Optional[t.Dict]=None)->None:"""Stores the fine-grained (per step or per epoch) metrics to memory.
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defcommit_existing_metrics(self,metrics_name:str,uri:str,props:t.Optional[t.Dict]=None,custom_properties:t.Optional[t.Dict]=None):""" Commits existing metrics associated with the given URI to MLMD. Example:
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defcreate_dataslice(self,name:str)->"Cmf.DataSlice":"""Creates a dataslice object. Once created, users can add data instances to this data slice with [add_data][cmflib.cmf.Cmf.DataSlice.add_data] method. Users are also responsible for committing data slices by calling the
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defupdate_dataslice(self,name:str,record:str,custom_properties:t.Dict):"""Updates a dataslice record in a Parquet file with the provided custom properties. Example: ```python
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