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components import_model
github-actions[bot] edited this page Dec 6, 2024
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Import a model into a workspace or a registry
Version: 0.0.41
Preview
View in Studio: https://ml.azure.com/registries/azureml/components/import_model/version/0.0.41
pipeline specific compute
Name | Description | Type | Default | Optional | Enum |
---|---|---|---|---|---|
compute | Common compute for model download, MLflow conversion and registration. eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'. Special characters like \ and ' are invalid in the parameter value. If compute name is provided, instance_type field will be ignored and the respective cluster will be used | string | serverless | True | |
instance_type | Instance type to be used for the component in case of serverless compute, eg. STANDARD_NC6s_v3. The parameter compute must be set to 'serverless' for instance_type to be used | string | STANDARD_NC6s_v3 | True |
Inputs for download model
Name | Description | Type | Default | Optional | Enum |
---|---|---|---|---|---|
model_source | Storage containers from where model will be sourced from | string | Huggingface | ['AzureBlob', 'GIT', 'Huggingface'] | |
model_id | A valid model id for the model source selected. For example you can specify bert-base-uncased for importing HuggingFace bert base uncased model. Please specify the complete URL if GIT or AzureBlob is selected in model_source
|
string | |||
model_flavor | Flavor of MLFlow to which model the model is converted to. | string | HFTransformersV2 | False | ['HFTransformersV2', 'OSS'] |
model_framework | Framework from which model is imported from. | string | Huggingface | False | ['Huggingface', 'MMLab', 'llava', 'AutoML'] |
vllm_enabled | Enable vllm in the converted model | boolean | False | False | |
token | If set use it to access the private models or authenticate the user. For example, user can get the token for HF private model by creating account in Huggingface, accept the condition for models that needs to be downloaded and create access token from browser. For more details please visit - https://huggingface.co/docs/hub/security-tokens | string | True |
Inputs for the MlFLow conversion
Name | Description | Type | Default | Optional | Enum |
---|---|---|---|---|---|
license_file_path | Path to the license file | uri_file | True | ||
task_name | A Hugging face task on which model was trained on | 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 |
Inputs for MLflow local validation
Name | Description | Type | Default | Optional | Enum |
---|---|---|---|---|---|
local_validation_test_data | Test data for MLflow local validation. Validation will be skipped if test data is not provided | uri_file | True | ||
local_validation_column_rename_map | Provide mapping for local validation test data column names, that should be renamed before inferencing eg: col1:ren1; col2:ren2; col3:ren3 | string | True |
Inputs for Model registration
Name | Description | Type | Default | Optional | Enum |
---|---|---|---|---|---|
custom_model_name | Model name to use in the registration. If name already exists, the version will be auto incremented | string | True | ||
model_version | Model version in workspace/registry. If the same model name and version exists, the version will be auto incremented | string | True | ||
model_description | Description of the model that will be shown in AzureML registry or workspace | string | True | ||
registry_name | Name of the AzureML asset registry where the model will be registered. Model will be registered in a workspace if this is unspecified | string | True | ||
model_metadata | A JSON or a YAML file that contains model metadata confirming to Model V2 contract | uri_file | True | ||
update_existing_model | If set to true, will update the existing model. If set to false, will create a new model. | boolean | False | True |
Pipeline outputs
Name | Description | Type | Default | Optional | Enum |
---|
Name | Description | Type |
---|---|---|
mlflow_model_folder | Output path for the converted MLflow model | mlflow_model |
model_registration_details | Output folder with a file which captures transformations applied above and registration details in JSON file | uri_folder |