components convert_model_to_mlflow

Convert models to MLflow

convert_model_to_mlflow

Overview

Component converts models from supported frameworks to MLflow model packaging format

Version: 0.0.35

Tags

Preview

View in Studio: https://ml.azure.com/registries/azureml/components/convert_model_to_mlflow/version/0.0.35

Inputs

Name	Description	Type	Default	Optional	Enum
model_id	Huggingface model id (https://huggingface.co/<model_id>). A required parameter for Huggingface model framework. Can be provided as input here or in model_download_metadata JSON file.	string		True
model_flavor	Flavor of MLFlow to which the model is converted to.	string	HFTransformersV2	False	['HFTransformersV2', 'OSS']
vllm_enabled	Enable vllm in the converted model	boolean	False	False
model_framework	Framework from which model is imported from.	string	Huggingface	False	['Huggingface', 'MMLab', 'llava', 'AutoML']
task_name	A Hugging face task on which model was trained on. A required parameter for transformers MLflow flavor. Can be provided as input here or in model_download_metadata JSON file.	string		True	['chat-completion', 'fill-mask', 'token-classification', 'question-answering', 'summarization', 'text-generation', 'text2text-generation', 'text-classification', 'translation', 'image-classification', 'image-classification-multilabel', 'image-object-detection', 'image-instance-segmentation', 'image-to-text', 'text-to-image', 'text-to-image-inpainting', 'image-text-to-text', 'image-to-image', 'zero-shot-image-classification', 'mask-generation', 'video-multi-object-tracking', 'visual-question-answering', 'image-feature-extraction']
hf_config_args	Provide args that should be used to load Huggingface model config. eg: trust_remote_code=True;	string		True
hf_tokenizer_args	Provide args that should be used to load Huggingface model tokenizer. eg: trust_remote_code=True, device_map=auto,	string		True
hf_model_args	Provide args that should be used to load Huggingface model. eg: trust_remote_code=True, device_map=auto, low_cpu_mem_usage=True	string		True
hf_pipeline_args	Provide pipeline args that should be used while loading the hugging face model. Dont use quotes. If value cannot be eval'ed it will be taken as as string. eg: trust_remote_code=True, device_map=auto	string		True
hf_config_class	AutoConfig class may not be sufficient to load config for some of the models. You can use this parameter to send Config class name as it is	string		True
hf_model_class	AutoModel classes may not be sufficient to load some of the models. You can use this parameter to send Model class name as it is	string		True
hf_tokenizer_class	AutoTokenizer class may not be sufficient to load tokenizer for some of the models. You can use this parameter to send Config class name as it is	string		True
hf_use_experimental_features	Enable experimental features for hugging face MLflow model conversion	boolean	False	True
extra_pip_requirements	Extra pip dependencies that MLflow model should capture as part of conversion. This would be used to create environment while loading the model for inference. Pip dependencies expressed as below. Do not use quotes for passing. eg: pkg1==1.0, pkg2, pkg3==1.0	string		True
inference_base_image	The docker image to use in model inference. This image id is assigned to azureml.base_image key in metadata section of mlmodel file.	string		True
model_download_metadata	JSON file containing model download details.	uri_file		True
model_path	Path to the model.	uri_folder		True
model_path_mmd	Path to the MMD model.	uri_folder		True
license_file_path	Path to the license file	uri_file		True

Outputs

Name	Description	Type
mlflow_model_folder	Output path for the converted MLflow model.	mlflow_model

Environment

azureml://registries/azureml/environments/model-management/versions/36