Pipeline Component to finetune MedImageInsight Model.

Version: 0.0.3

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

Name	Description	Type	Default	Optional	Enum
mlflow_embedding_model_path	Path to the MLflow model to be imported.	uri_folder		False
eval_image_tsv	Path to the evaluation image TSV file.	uri_file		False
eval_text_tsv	Path to the evaluation text TSV file.	uri_file		False
eval_train_image_tsv	Optional path used for the evaluation task. If not specified, will use the training path.	uri_file		True
eval_train_text_tsv	Optional path used for the evaluation task. If not specified, will use the training path.	uri_file		True
image_tsv	Path to the image TSV file.	uri_file		False
text_tsv	Path to the text TSV file.	uri_file		False
label_file	Path to the label file.	uri_file		False
conf_files	Path to the configuration files.	uri_file		False
instance_type_finetune	Instance type to be used for finetune component in case of serverless compute, eg. standard_nc24rs_v3. The parameter compute_finetune must be set to 'serverless' for instance_type to be used	string	Standard_nc24rs_v3	True
compute_finetune	compute to be used for finetune eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'. Special characters like \ and ' are invalid in the parameter value. If compute cluster name is provided, instance_type field will be ignored and the respective cluster will be used	string	serverless	True
process_count_per_instance	Number of processes to run per instance. This is used to set the number of GPUs to use for training.	integer	1	True
instance_count	Number of instances to use for training.	integer	1	True

Name	Description	Type
save_dir	Directory to save the model and checkpoints, used for pipeline's internal operations.	uri_folder
embedding_mlflow_model	Directory to save the MLflow model.	mlflow_model
classification_mlflow_model	Path to save the output model configured with labels.	mlflow_model