wehub-resource-sync
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
"""
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This script should be executed in a fresh python interpreter process using `subprocess`.
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"""
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import argparse
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from mlflow.pyfunc.scoring_server import _predict
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--model-uri", required=True)
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parser.add_argument("--input-path", required=False)
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parser.add_argument("--output-path", required=False)
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parser.add_argument("--content-type", required=True)
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return parser.parse_args()
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# Guidance for fixing missing module error
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_MISSING_MODULE_HELP_MSG = (
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"Exception occurred while running inference: {e}"
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"\n\n"
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"\033[93m[Hint] It appears that your MLflow Model doesn't contain the required "
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"dependency '{missing_module}' to run model inference. When logging a model, MLflow "
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"detects dependencies based on the model flavor, but it is possible that some "
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"dependencies are not captured. In this case, you can manually add dependencies "
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"using the `extra_pip_requirements` parameter of `mlflow.pyfunc.log_model`.\033[0m"
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"""
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\033[1mSample code:\033[0m
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----
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mlflow.pyfunc.log_model(
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artifact_path="model",
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python_model=your_model,
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extra_pip_requirements=["{missing_module}==x.y.z"]
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)
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----
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For mode guidance on fixing missing dependencies, please refer to the MLflow docs:
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https://www.mlflow.org/docs/latest/deployment/index.html#how-to-fix-dependency-errors-when-serving-my-model
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"""
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)
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def main():
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args = parse_args()
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try:
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_predict(
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model_uri=args.model_uri,
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input_path=args.input_path or None,
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output_path=args.output_path or None,
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content_type=args.content_type,
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)
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except ModuleNotFoundError as e:
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message = _MISSING_MODULE_HELP_MSG.format(e=str(e), missing_module=e.name)
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raise RuntimeError(message) from e
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if __name__ == "__main__":
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main()
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