Component to finetune multimodal models for classification using MMEFT

Version: 0.0.3

View in Studio: https://ml.azure.com/registries/azureml/components/multimodal_classification_finetune/version/0.0.3

Name	Description	Type	Default	Optional	Enum
problem_type	Specify whether its single-label or multi-label multimodal classification task.	string	multimodal-classification-singlelabel	False	['multimodal-classification-singlelabel', 'multimodal-classification-multilabel']

Training parameters

Name	Description	Type	Default	Optional	Enum
number_of_epochs	training epochs	integer	1	True
max_steps	If set to a positive number, the total number of training steps to perform. Overrides '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.	integer	-1	True
training_batch_size	Train batch size	integer	8	True
validation_batch_size	Validation batch size	integer	64	True
auto_find_batch_size	Flag to enable auto finding of batch size. If the provided 'training_batch_size' goes into Out Of Memory (OOM) enabling auto_find_batch_size will find the correct batch size by iteratively reducing 'training_batch_size' by a factor of 2 till the OOM is fixed	string	false	True	['true', 'false']
optimizer	Optimizer to be used while training	string	adamw_hf	True	['adamw_hf', 'adamw_torch', 'adafactor']
learning_rate	Start learning rate. Defaults to linear scheduler.	number	0.001	True
warmup_steps	Number of steps used for a linear warmup from 0 to learning_rate	integer	0	True
weight_decay	The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in AdamW optimizer	number	0.0	True
adam_beta1	The beta1 hyperparameter for the AdamW optimizer	number	0.9	True
adam_beta2	The beta2 hyperparameter for the AdamW optimizer	number	0.999	True
adam_epsilon	The epsilon hyperparameter for the AdamW optimizer	number	1e-08	True
gradient_accumulation_steps	Number of updates steps to accumulate the gradients for, before performing a backward/update pass	integer	64	True
learning_rate_scheduler	The scheduler type to use	string	linear	True	['linear', 'cosine', 'cosine_with_restarts', 'polynomial', 'constant', 'constant_with_warmup']
precision	Apply mixed precision training. This can reduce memory footprint by performing operations in half-precision.	string	32	True	['32', '16']
random_seed	Random seed that will be set at the beginning of training	integer	42	True
enable_full_determinism	Ensure reproducible behavior during distributed training	string	false	True	['true', 'false']
dataloader_num_workers	Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.	integer	0	True
ignore_mismatched_sizes	Whether or not to raise an error if some of the weights from the checkpoint do not have the same size as the weights of the model	string	true	True	['true', 'false']
max_grad_norm	Maximum gradient norm (for gradient clipping)	number	1.0	True
evaluation_strategy	The evaluation strategy to adopt during training	string	epoch	True	['epoch', 'steps']
evaluation_steps_interval	The evaluation steps in fraction of an epoch steps to adopt during training. Overwrites evaluation_steps if not 0.	number	0.0	True
evaluation_steps	Number of update steps between two evals if evaluation_strategy='steps'	integer	500	True
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
primary_metric	Specify the metric to use to compare two different models	string	loss	True	['loss', 'f1_macro', 'mcc', 'accuracy', 'precision_macro', 'recall_macro']
resume_from_checkpoint	Loads Optimizer, Scheduler and Trainer state for finetuning if true	string	false	True	['true', 'false']
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	-1	True

Early Stopping Parameters

Name	Description	Type	Default	Optional	Enum
apply_early_stopping	Enable early stopping	string	false	True	['true', 'false']
early_stopping_patience	Stop training when the specified metric worsens for early_stopping_patience evaluation calls	integer	1	True
early_stopping_threshold	Denotes how much the specified metric must improve to satisfy early stopping conditions	number	0.0	True

Deepspeed Parameters

Name	Description	Type	Default	Optional	Enum
apply_deepspeed	If set to true, will enable deepspeed for training	string	false	True	['true', 'false']
deepspeed_config	Deepspeed config to be used for finetuning	uri_file		True

ORT Parameters

Name	Description	Type	Default	Optional	Enum
apply_ort	If set to true, will use the ONNXRunTime training	string	false	True	['true', 'false']

MLFlow Parameters

Name	Description	Type	Default	Optional	Enum
save_as_mlflow_model	If set to true, will save as mlflow model with pyfunc as flavour	string	true	True	['true', 'false']

Dataset parameterss

Name	Description	Type	Default	Optional	Enum
preprocess_output	output folder of preprocessor containing preprocessed metadata information	uri_folder		False
model_selector_output	output folder of model selector containing model metadata like config, checkpoints, tokenizer config	uri_folder		False

Name	Description	Type
pytorch_model_folder	Output dir to save the finetune model and other metadata	uri_folder
mlflow_model_folder	Output dir to save the finetune model as mlflow model	mlflow_model

azureml://registries/azureml/environments/acft-multimodal-gpu/versions/20