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components mlflow_model_local_validation
github-actions[bot] edited this page Oct 14, 2024
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Validates if a MLFLow model can be loaded on a compute and is usable for inferencing.
Version: 0.0.16
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View in Studio: https://ml.azure.com/registries/azureml/components/mlflow_model_local_validation/version/0.0.16
Name | Description | Type | Default | Optional | Enum |
---|---|---|---|---|---|
model_path | MLFlow model to be validated | mlflow_model | |||
test_data_path | Test dataset for model inferencing | uri_file | True | ||
column_rename_map | Provide mapping of dataset column names that should be renamed before inferencing. eg: col1:ren1; col2:ren2; col3:ren3 | string | 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'] |
Name | Description | Type |
---|---|---|
mlflow_model_folder | Validated input model. Here input model is used to block further steps in pipeline job if local validation fails | uri_folder |
azureml://registries/azureml/environments/python-sdk-v2/versions/23