finetuner package#

Subpackages#

Submodules#

Module contents#

finetuner.login()[source]#
finetuner.list_callbacks()[source]#

List available callbacks.

Return type

Dict[str, ~CallbackStubType]

finetuner.list_models()[source]#

List available models for training.

Return type

List[str]

finetuner.list_model_options()[source]#

List available options per model.

Return type

Dict[str, List[Dict[str, Any]]]

finetuner.describe_models()[source]#

Describe available models in a table.

Return type

None

finetuner.fit(model, train_data, eval_data=None, run_name=None, description=None, experiment_name=None, model_options=None, loss='TripletMarginLoss', miner=None, optimizer='Adam', learning_rate=None, epochs=5, batch_size=64, callbacks=None, scheduler_step='batch', freeze=False, output_dim=None, cpu=True, num_workers=4)[source]#

Start a finetuner run!

Parameters

  • model (str) – The name of model to be fine-tuned. Run finetuner.list_models() or finetuner.describe_models() to see the available model names.

  • train_data (Union[str, DocumentArray]) – Either a DocumentArray for training data or a name of the DocumentArray that is pushed on Hubble.

  • eval_data (Union[str, DocumentArray, None]) – Either a DocumentArray for evaluation data or a name of the DocumentArray that is pushed on Hubble.

  • run_name (Optional[str]) – Name of the run.

  • description (Optional[str]) – Run description.

  • experiment_name (Optional[str]) – Name of the experiment.

  • model_options (Optional[Dict[str, Any]]) – Additional arguments to pass to the model construction. These are model specific options and are different depending on the model you choose. Run finetuner.list_model_options() to see available options for every model.

  • loss (str) – Name of the loss function used for fine-tuning. Default is TripletMarginLoss. Options: CosFaceLoss, NTXLoss, AngularLoss, ArcFaceLoss, BaseMetricLossFunction, MultipleLosses, CentroidTripletLoss, CircleLoss, ContrastiveLoss, CrossBatchMemory, FastAPLoss, GenericPairLoss, IntraPairVarianceLoss, LargeMarginSoftmaxLoss, GeneralizedLiftedStructureLoss, LiftedStructureLoss, MarginLoss, EmbeddingRegularizerMixin, WeightRegularizerMixin, MultiSimilarityLoss, NPairsLoss, NCALoss, NormalizedSoftmaxLoss, ProxyAnchorLoss, ProxyNCALoss, SignalToNoiseRatioContrastiveLoss, SoftTripleLoss, SphereFaceLoss, SupConLoss, TripletMarginLoss, TupletMarginLoss, VICRegLoss, CLIPLoss.

  • miner (Optional[str]) – Name of the miner to create tuple indices for the loss function. Options: AngularMiner, BaseMiner, BaseSubsetBatchMiner, BaseTupleMiner, BatchEasyHardMiner, BatchHardMiner, DistanceWeightedMiner, HDCMiner, EmbeddingsAlreadyPackagedAsTriplets, MaximumLossMiner, PairMarginMiner, MultiSimilarityMiner, TripletMarginMiner, UniformHistogramMiner.

  • optimizer (str) – Name of the optimizer used for fine-tuning. Options: Adadelta, Adagrad, Adam, AdamW, SparseAdam, Adamax, ASGD, LBFGS, NAdam, RAdam, RMSprop, Rprop, SGD.

  • learning_rate (Optional[float]) – learning rate for the optimizer.

  • epochs (int) – Number of epochs for fine-tuning.

  • batch_size (int) – Number of items to include in a batch.

  • callbacks (Optional[List[~CallbackStubType]]) – List of callback stub objects. See the finetuner.callback subpackage for available options, or run finetuner.list_callbacks().

  • scheduler_step (str) – At which interval should the learning rate scheduler’s step function be called. Valid options are batch and epoch.

  • freeze (bool) – If set to True, will freeze all layers except the last one.

  • output_dim (Optional[int]) – The expected output dimension as int. If set, will attach a projection head.

  • cpu (bool) – Whether to use the CPU. If set to False a GPU will be used.

  • num_workers (int) – Number of CPU workers. If cpu: False this is the number of workers used by the dataloader.

Return type

Run

finetuner.create_run(model, train_data, eval_data=None, run_name=None, description=None, experiment_name=None, model_options=None, loss='TripletMarginLoss', miner=None, optimizer='Adam', learning_rate=None, epochs=5, batch_size=64, callbacks=None, scheduler_step='batch', freeze=False, output_dim=None, cpu=True, num_workers=4)#

Start a finetuner run!

Parameters

  • model (str) – The name of model to be fine-tuned. Run finetuner.list_models() or finetuner.describe_models() to see the available model names.

  • train_data (Union[str, DocumentArray]) – Either a DocumentArray for training data or a name of the DocumentArray that is pushed on Hubble.

  • eval_data (Union[str, DocumentArray, None]) – Either a DocumentArray for evaluation data or a name of the DocumentArray that is pushed on Hubble.

  • run_name (Optional[str]) – Name of the run.

  • description (Optional[str]) – Run description.

  • experiment_name (Optional[str]) – Name of the experiment.

  • model_options (Optional[Dict[str, Any]]) – Additional arguments to pass to the model construction. These are model specific options and are different depending on the model you choose. Run finetuner.list_model_options() to see available options for every model.

  • loss (str) – Name of the loss function used for fine-tuning. Default is TripletMarginLoss. Options: CosFaceLoss, NTXLoss, AngularLoss, ArcFaceLoss, BaseMetricLossFunction, MultipleLosses, CentroidTripletLoss, CircleLoss, ContrastiveLoss, CrossBatchMemory, FastAPLoss, GenericPairLoss, IntraPairVarianceLoss, LargeMarginSoftmaxLoss, GeneralizedLiftedStructureLoss, LiftedStructureLoss, MarginLoss, EmbeddingRegularizerMixin, WeightRegularizerMixin, MultiSimilarityLoss, NPairsLoss, NCALoss, NormalizedSoftmaxLoss, ProxyAnchorLoss, ProxyNCALoss, SignalToNoiseRatioContrastiveLoss, SoftTripleLoss, SphereFaceLoss, SupConLoss, TripletMarginLoss, TupletMarginLoss, VICRegLoss, CLIPLoss.

  • miner (Optional[str]) – Name of the miner to create tuple indices for the loss function. Options: AngularMiner, BaseMiner, BaseSubsetBatchMiner, BaseTupleMiner, BatchEasyHardMiner, BatchHardMiner, DistanceWeightedMiner, HDCMiner, EmbeddingsAlreadyPackagedAsTriplets, MaximumLossMiner, PairMarginMiner, MultiSimilarityMiner, TripletMarginMiner, UniformHistogramMiner.

  • optimizer (str) – Name of the optimizer used for fine-tuning. Options: Adadelta, Adagrad, Adam, AdamW, SparseAdam, Adamax, ASGD, LBFGS, NAdam, RAdam, RMSprop, Rprop, SGD.

  • learning_rate (Optional[float]) – learning rate for the optimizer.

  • epochs (int) – Number of epochs for fine-tuning.

  • batch_size (int) – Number of items to include in a batch.

  • callbacks (Optional[List[~CallbackStubType]]) – List of callback stub objects. See the finetuner.callback subpackage for available options, or run finetuner.list_callbacks().

  • scheduler_step (str) – At which interval should the learning rate scheduler’s step function be called. Valid options are batch and epoch.

  • freeze (bool) – If set to True, will freeze all layers except the last one.

  • output_dim (Optional[int]) – The expected output dimension as int. If set, will attach a projection head.

  • cpu (bool) – Whether to use the CPU. If set to False a GPU will be used.

  • num_workers (int) – Number of CPU workers. If cpu: False this is the number of workers used by the dataloader.

Return type

Run

finetuner.get_run(run_name, experiment_name=None)[source]#

Get run by its name and (optional) experiment.

If an experiment name is not specified, we’ll look for the run in the default experiment.

Parameters

  • run_name (str) – Name of the run.

  • experiment_name (Optional[str]) – Optional name of the experiment.

Return type

Run

Returns

A Run object.

finetuner.list_runs(experiment_name=None)[source]#

List every run.

If an experiment name is not specified, we’ll list every run across all experiments.

Parameters

experiment_name (Optional[str]) – Optional name of the experiment.

Return type

List[Run]

Returns

A list of Run objects.

finetuner.delete_run(run_name, experiment_name=None)[source]#

Delete a run.

If an experiment name is not specified, we’ll look for the run in the default experiment.

Parameters

  • run_name (str) – Name of the run. View your runs with list_runs.

  • experiment_name (Optional[str]) – Optional name of the experiment.

Return type

None

finetuner.delete_runs(experiment_name=None)[source]#

Delete every run.

If an experiment name is not specified, we’ll delete every run across all experiments.

Parameters

experiment_name (Optional[str]) – Optional name of the experiment. View your experiment names with list_experiments().

Return type

None

finetuner.create_experiment(name=None)[source]#

Create an experiment.

Parameters

name (Optional[str]) – Optional name of the experiment. If None, the experiment is named after the current directory.

Return type

Experiment

Returns

An Experiment object.

finetuner.get_experiment(name)[source]#

Get an experiment by its name.

Parameters

name (str) – Name of the experiment.

Return type

Experiment

Returns

An Experiment object.

finetuner.list_experiments()[source]#

List every experiment.

Return type

List[Experiment]

finetuner.delete_experiment(name)[source]#

Delete an experiment by its name. :type name: str :param name: Name of the experiment.

View your experiment names with list_experiments().

Return type

Experiment

Returns

Deleted experiment.

finetuner.delete_experiments()[source]#

Delete every experiment. :rtype: List[Experiment] :return: List of deleted experiments.