components transformers_image_classification_pipeline

Image Classification HuggingFace Transformers Pipeline

transformers_image_classification_pipeline

Overview

Pipeline component for image classification using HuggingFace transformers models.

Version: 0.0.19

View in Studio: https://ml.azure.com/registries/azureml/components/transformers_image_classification_pipeline/version/0.0.19

Inputs

Name	Description	Type	Default	Optional	Enum
compute_model_import	Compute to be used for model_import eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'	string		False
compute_finetune	Compute to be used for finetune eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'	string		False
instance_count	Number of nodes to be used for finetuning (used for distributed training)	integer	1	True
process_count_per_instance	Number of gpus to be used per node for finetuning, should be equal to number of gpu per node in the compute SKU used for finetune	integer	1	True
compute_model_evaluation	Compute to be used for model evaluation eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'	string		True

Model Selector Component Model family

Name	Description	Type	Default	Optional	Enum
model_family	Which framework the model belongs to.	string	HuggingFaceImage	True	['HuggingFaceImage']
model_name	Please select models from AzureML Model Assets for all supported models. For HuggingFace models, which are not supported in AuzreML model registry, input HuggingFace model_name here. The Model will be downloaded from HuggingFace hub using this model_name and are subject to third party license terms available on the HuggingFace model details page. It is the user responsibility to comply with the model's license terms.	string		True
pytorch_model	Pytorch Model registered in AzureML Asset.	custom_model		True
mlflow_model	Mlflow Model registered in AzureML Asset.	mlflow_model		True
download_from_source	Download model directly from HuggingFace instead of system registry	boolean	False	True

Finetuning Component component input: training mltable

Name	Description	Type	Default	Optional	Enum
training_data	Path to the mltable of the training dataset.	mltable		False

optional component input: validation mltable

Name	Description	Type	Default	Optional	Enum
validation_data	Path to the mltable of the validation dataset.	mltable		True
image_width	Final image width after augmentation that is input to the network. Default value is -1 which means it would be overwritten by default image width in Hugging Face feature extractor. If either image_width or image_height is set to -1, default value would be used for both width and height.	integer	-1	True
image_height	Final image height after augmentation that is input to the network. Default value is -1 which means it would be overwritten by default image height in Hugging Face feature extractor. If either image_width or image_height is set to -1, default value would be used for both width and height.	integer	-1	True
task_name	Which task the model is solving.	string			['image-classification', 'image-classification-multilabel']

primary metric

Name	Description	Type	Default	Optional	Enum
metric_for_best_model	Specify the metric to use to compare two different models. If left empty, will be chosen automatically based on the task type and model selected.	string		True	['loss', 'f1_score_macro', 'accuracy', 'precision_score_macro', 'recall_score_macro', 'iou', 'iou_macro', 'iou_micro', 'iou_weighted']

Augmentation parameters

Name	Description	Type	Default	Optional	Enum
apply_augmentations	If set to true, will enable data augmentations for training.	boolean	True	True
number_of_workers	Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process.	integer	8	True

Deepspeed Parameters

Name	Description	Type	Default	Optional	Enum
apply_deepspeed	If set to true, will enable deepspeed for training. If left empty, will be chosen automatically based on the task type and model selected.	boolean		True

optional component input: deepspeed config

Name	Description	Type	Default	Optional	Enum
deepspeed_config	Deepspeed config to be used for finetuning.	uri_file		True
apply_ort	If set to true, will use the ONNXRunTime training. If left empty, will be chosen automatically based on the task type and model selected.	boolean		True

Training parameters

Name	Description	Type	Default	Optional	Enum
number_of_epochs	Number of training epochs. If left empty, will be chosen automatically based on the task type and model selected.	integer		True
max_steps	If set to a positive number, the total number of training steps to perform. Overrides 'number_of_epochs'. In case of using a finite iterable dataset the training may stop before reaching the set number of steps when all data is exhausted. If left empty, will be chosen automatically based on the task type and model selected.	integer		True
training_batch_size	Train batch size. If left empty, will be chosen automatically based on the task type and model selected.	integer		True
validation_batch_size	Validation batch size. If left empty, will be chosen automatically based on the task type and model selected.	integer		True
auto_find_batch_size	Flag to enable auto finding of batch size. If the provided 'per_device_train_batch_size' goes into Out Of Memory (OOM) enabling auto_find_batch_size will find the correct batch size by iteratively reducing 'per_device_train_batch_size' by a factor of 2 till the OOM is fixed.	boolean	False	True

learning rate and learning rate scheduler

Name	Description	Type	Default	Optional	Enum
learning_rate	Start learning rate. Defaults to linear scheduler. If left empty, will be chosen automatically based on the task type and model selected.	number		True
learning_rate_scheduler	The scheduler type to use. If left empty, will be chosen automatically based on the task type and model selected.	string		True	['warmup_linear', 'warmup_cosine', 'warmup_cosine_with_restarts', 'warmup_polynomial', 'constant', 'warmup_constant']
warmup_steps	Number of steps used for a linear warmup from 0 to learning_rate. If left empty, will be chosen automatically based on the task type and model selected.	integer		True

optimizer

Name	Description	Type	Default	Optional	Enum
optimizer	Optimizer to be used while training. 'adamw_ort_fused' optimizer is only supported for ORT training. If left empty, will be chosen automatically based on the task type and model selected.	string		True	['adamw_hf', 'adamw', 'sgd', 'adafactor', 'adagrad', 'adamw_ort_fused']
weight_decay	The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in AdamW and SGD optimizer. If left empty, will be chosen automatically based on the task type and model selected.	number		True
extra_optim_args	Optional additional arguments that are supplied to SGD Optimizer. The arguments should be semi-colon separated key value pairs and should be enclosed in double quotes. For example, "momentum=0.5; nesterov=True" for sgd. Please make sure to use a valid parameter names for the chosen optimizer. For exact parameter names, please refer https://pytorch.org/docs/1.13/generated/torch.optim.SGD.html#torch.optim.SGD for SGD. Parameters supplied in extra_optim_args will take precedence over the parameter supplied via other arguments such as weight_decay. If weight_decay is provided via "weight_decay" parameter and via extra_optim_args both, values specified in extra_optim_args will be used.	string		True

gradient accumulation

Name	Description	Type	Default	Optional	Enum
gradient_accumulation_step	Number of update steps to accumulate the gradients for, before performing a backward/update pass. If left empty, will be chosen automatically based on the task type and model selected.	integer		True

mixed precision training

Name	Description	Type	Default	Optional	Enum
precision	Apply mixed precision training. This can reduce memory footprint by performing operations in half-precision.	string	32	True	['32', '16']

label smoothing factor

Name	Description	Type	Default	Optional	Enum
label_smoothing_factor	The label smoothing factor to use in range [0.0, 1,0). Zero means no label smoothing, otherwise the underlying onehot-encoded labels are changed from 0s and 1s to label_smoothing_factor/num_labels and 1 - label_smoothing_factor + label_smoothing_factor/num_labels respectively. Not applicable to multi-label classification. If left empty, will be chosen automatically based on the task type and model selected.	number		True

random seed

Name	Description	Type	Default	Optional	Enum
random_seed	Random seed that will be set at the beginning of training.	integer	42	True

evaluation strategy parameters

Name	Description	Type	Default	Optional	Enum
evaluation_strategy	The evaluation strategy to adopt during training. Please note that the save_strategy and evaluation_strategy should match.	string	epoch	True	['epoch', 'steps']
evaluation_steps	Number of update steps between two evals if evaluation_strategy='steps'. Please note that the saving steps should be a multiple of the evaluation steps.	integer	500	True

logging strategy parameters

Name	Description	Type	Default	Optional	Enum
logging_strategy	The logging strategy to adopt during training.	string	epoch	True	['epoch', 'steps']
logging_steps	Number of update steps between two logs if logging_strategy='steps'.	integer	500	True

Save strategy

Name	Description	Type	Default	Optional	Enum
save_strategy	The checkpoint save strategy to adopt during training. Please note that the save_strategy and evaluation_strategy should match.	string	epoch	True	['epoch', 'steps']
save_steps	Number of updates steps before two checkpoint saves if save_strategy="steps". Please note that the saving steps should be a multiple of the evaluation steps.	integer	500	True

model checkpointing limit

Name	Description	Type	Default	Optional	Enum
save_total_limit	If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in output_dir. If the value is -1 saves all checkpoints".	integer	5	True

Early Stopping Parameters

Name	Description	Type	Default	Optional	Enum
early_stopping	Enable early stopping.	boolean	False	True
early_stopping_patience	Stop training when the specified metric worsens for early_stopping_patience evaluation calls.	integer	1	True

Grad Norm

Name	Description	Type	Default	Optional	Enum
max_grad_norm	Maximum gradient norm (for gradient clipping). If left empty, will be chosen automatically based on the task type and model selected.	number		True

resume from the input model

Name	Description	Type	Default	Optional	Enum
resume_from_checkpoint	Loads optimizer, Scheduler and Trainer state for finetuning if true.	boolean	False	True

save mlflow model

Name	Description	Type	Default	Optional	Enum
save_as_mlflow_model	Save as mlflow model with pyfunc as flavour.	boolean	True	True

Model prediction Component component input: test mltable

Name	Description	Type	Default	Optional	Enum
test_data	Path to the mltable of the test dataset.	mltable		False
test_batch_size	Test batch size.	integer	4	True
label_column_name	Label column name in provided test dataset, for example "label".	string	label	True
input_column_names	Input column names in provided test dataset, for example column1. Add comma delimited values in case of multiple input columns, for example column1,column2.	string	image_url	True
evaluation_config	Additional parameters for Computing Metrics.	uri_file		True
evaluation_config_params	Additional parameters as JSON serialized string.	string		True

Outputs

########################### Finetuning Component ########################### #

Name	Description	Type
mlflow_model_folder	Output dir to save the finetune model as mlflow model.	mlflow_model
pytorch_model_folder	Output dir to save the finetune model as torch model.	custom_model

Compute metrics Component

Name	Description	Type
evaluation_result	Test Data Evaluation Results	uri_folder