snorkelflow.sdk.FineTuningApp

class snorkelflow.sdk.FineTuningApp(app_uid, model_node_uid, dataset_uid, label_schema_uid, workspace_uid, fine_tuning_app_config)

Bases: object

\_\_init\_\_

__init__

__init__(app_uid, model_node_uid, dataset_uid, label_schema_uid, workspace_uid, fine_tuning_app_config)

Methods

__init__(app_uid, model_node_uid, ...)
create(app_name, fine_tuning_app_config)	Create a new fine tuning application with the given name and configuration The dataset, application, label schema will be setup for you.
create_evaluation_report(split[, ...])	Create an evaluation report for the quality dataset.
delete()	Delete the fine tuning application.
get(application)	Initialize a FineTuningApp object from an existing fine tuning application previously created with the SDK.
get_annotation_batches()	Get the annotation batches associated with the entire fine tuning dataset and label schema.
get_dataframe([split, source_uids, x_uids, ...])	Get data from the dataset associated with the fine tuning application with the given filters applied
get_evaluation_report(evaluation_report_uid)	Get the evaluation report associated with the given evaluation report uid.
get_ft_dataset()	Get the fine tuning dataset associated with the fine tuning application.
get_quality_dataset(model_uid)	Create a QualityDataset object from a trained model's predictions.
get_sources()	Get the sources within the current workspace.
import_data(data, split, source_uid[, name, ...])	Import data into the fine tuning application.
import_ground_truth(gt_df, gt_column, ...[, ...])	Import ground truth labels into the fine tuning dataset.
list_evaluation_reports()	List the evaluation reports associated with the fine tuning application.
list_quality_models()	List the quality models associated with the fine tuning application.
register_custom_metric(metric_name, metric_func)	Register a user-defined metric with the FineTuningApp.
register_model_source(model_name[, metadata])	Register a model source with the given model name and metadata.
register_source(source_name, source_type, ...)	Register a source in the platform
setup_studio()	Setup the studio for the fine tuning application.

Attributes

datasource_metadata

Get metadata about each datasource, include details about the source

create

create

classmethod create(app_name, fine_tuning_app_config)

Create a new fine tuning application with the given name and configuration The dataset, application, label schema will be setup for you.

Parameters

Parameters

Name	Type	Default	Info
app_name	str		The name of the fine tuning application.
fine_tuning_app_config	FineTuningAppConfig		The configuration of the fine tuning application.

Returns

Returns

The fine tuning application object

Return type

Return type

FineTuningApp

create\_evaluation\_report

create_evaluation_report

create_evaluation_report(split, quality_models=None, finetuned_model_sources=None, slices=None)

Create an evaluation report for the quality dataset.

Parameters

Parameters

Name	Type	Default	Info
split	str		The split of the data to evaluate.
quality_models	Union[List[str], List[int], None]	None	The quality models to evaluate (if not provided, the committed quality model or the most recently trained model will be used, in that order).
finetuned_model_sources	Union[List[str], List[int], None]	None	The finetuned model sources to evaluate (if not provided, all finetuned models associated with the datasources will be used).
slices	Union[List[str], List[int], None]	None	The slices to evaluate (if not provided, all slices in the given dataset will be evaluated).

Returns

Returns

A dictionary containing the evaluation results

Return type

Return type

Dict[str, Any]

delete

delete

delete()

Delete the fine tuning application. Dataset must be deleted separately.

Return type

Return type

None

get

get

classmethod get(application)

Initialize a FineTuningApp object from an existing fine tuning application previously created with the SDK.

Parameters

Parameters

Name	Type	Default	Info
application	Union[str, int]		The name or uid of the fine tuning application.

Returns

Returns

The fine tuning application object

Return type

Return type

FineTuningApp

get\_annotation\_batches

get_annotation_batches

get_annotation_batches()

Get the annotation batches associated with the entire fine tuning dataset and label schema.

Return type

Return type

List[Batch]

get\_dataframe

get_dataframe

get_dataframe(split=None, source_uids=None, x_uids=None, datasource_uids=None)

Get data from the dataset associated with the fine tuning application with the given filters applied

Parameters

Parameters

Name	Type	Default	Info
split	Optional[str]	None	The split of the data to get.
source_uids	Optional[List[int]]	None	The source uids to filter by.
x_uids	Optional[List[str]]	None	The x uids to filter by.
datasource_uids	Optional[List[str]]	None	The datasource uids to filter by.

Return type

Return type

DataFrame

get\_evaluation\_report

get_evaluation_report

get_evaluation_report(evaluation_report_uid)

Get the evaluation report associated with the given evaluation report uid.

Parameters

Parameters

Name	Type	Default	Info
evaluation_report_uid	int		The unique identifier for the evaluation report to retrieve.

Returns

Returns

A dictionary containing the details of the evaluation report.

Return type

Return type

Dict[str, Any]

get\_ft\_dataset

get_ft_dataset

get_ft_dataset()

Get the fine tuning dataset associated with the fine tuning application.

Returns

Returns

The fine tuning dataset object

Return type

Return type

FTDataset

get\_quality\_dataset

get_quality_dataset

get_quality_dataset(model_uid)

Create a QualityDataset object from a trained model’s predictions.

Parameters

Parameters

Name	Type	Default	Info
model_uid	int		The unique identifier of the trained model.

Returns

Returns

The QualityDataset object.

Return type

Return type

QualityDataset

get\_sources

get_sources

get_sources()

Get the sources within the current workspace.

Returns

Returns

A list of dictionaries containing the details of the sources.

Return type

Return type

List[Dict[str, Any]]

import\_data

import_data

import_data(data, split, source_uid, name=None, sync=True, refresh_datasources=True, prompt_template=None)

Import data into the fine tuning application.

Parameters

Parameters

Name	Type	Default	Info
data	Union[str, DataFrame]		A file path or a pandas DataFrame of the data to import into the dataset.
split	str		The split of the data.
source_uid	int		The source to associate the data with for data lineage.
name	Optional[str]	None	The name of the data source.
sync	bool	True	Whether to wait for the ingestion job to complete before returning.
refresh_datasources	bool	True	Whether to refresh datasources for the downstream model node after ingestion. Can only be set if sync is True.
prompt_template	Optional[str]	None	The prompt template used when the data was generated.

Returns

Returns

The job_id of the ingestion job

Return type

Return type

str

import\_ground\_truth

import_ground_truth

import_ground_truth(gt_df, gt_column, join_column, source_uid=None, user_format=True)

Import ground truth labels into the fine tuning dataset.

Parameters

Parameters

Name	Type	Default	Info
gt_df	DataFrame		The ground truth labels DataFrame.
gt_column	str		The column in the ground truth DataFrame that contains the labels.
join_column	str		The column to join the gt_df and the fine tuning dataset on to associate the ground truth labels with the fine tuning dataset.
source_uid	Optional[int]	None	The source uid to associate the annotations with. Defaults to the requesting user’s source uid if not set.
user_format	bool	True	Whether the labels are in the user format or not (the label map string value vs the int value). If true, the label map will be used to convert the labels to their integer values.

Return type

Return type

None

list\_evaluation\_reports

list_evaluation_reports

list_evaluation_reports()

List the evaluation reports associated with the fine tuning application.

Return type

Return type

List[Dict[str, Any]]

list\_quality\_models

list_quality_models

list_quality_models()

List the quality models associated with the fine tuning application.

Return type

Return type

DataFrame

register\_custom\_metric

register_custom_metric

register_custom_metric(metric_name, metric_func, overwrite=False)

Register a user-defined metric with the FineTuningApp.

Parameters

Parameters

Name	Type	Default	Info
metric_name	str		The display name of this metric.
metric_func	Callable		A python function to compute this metric.
overwrite	Optional[bool]	False	Overwrite a metric of the same name if one already exists.

Returns

Returns

id of the registered metric.

Return type

Return type

int

register\_model\_source

register_model_source

register_model_source(model_name, metadata=None)

Register a model source with the given model name and metadata.

Parameters

Parameters

Name	Type	Default	Info
model_name	str		The name of the model.
metadata	Optional[ModelSourceMetadata]	None	The metadata associated with the model source. If not provided, the provided model name will be used as the model name in the metadata.

Returns

Returns

The registered model source.

Return type

Return type

Dict[str, Any]

register\_source

register_source

classmethod register_source(source_name, source_type, user_uid, metadata=None)

Register a source in the platform

Parameters

Parameters

Name	Type	Default	Info
source_name	str		The name of the source.
source_type	SvcSourceType		The type of the source.
user_uid	Optional[int]		The user uid to associate with the source.
metadata	Optional[Dict[str, Any]]	None	The metadata to associate with the source.

Returns

Returns

The created source

Return type

Return type

Dict[str, Any]

setup\_studio

setup_studio

setup_studio()

Setup the studio for the fine tuning application. This will refresh any stale datasources associated with the fine tuning application.

Return type

Return type

None

property datasource_metadata: Dict[int, Any]

Get metadata about each datasource, include details about the source