wehub-resource-sync
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
import os
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from mlflow.deployments import BaseDeploymentClient, PredictionsResponse
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f_deployment_name = "fake_deployment_name"
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f_endpoint_name = "fake_endpoint_name"
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class PluginDeploymentClient(BaseDeploymentClient):
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def create_deployment(self, name, model_uri, flavor=None, config=None, endpoint=None):
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if config and config.get("raiseError") == "True":
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raise RuntimeError("Error requested")
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return {"name": f_deployment_name, "flavor": flavor}
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def delete_deployment(self, name, config=None, endpoint=None):
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if config and config.get("raiseError") == "True":
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raise RuntimeError("Error requested")
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return None
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def update_deployment(self, name, model_uri=None, flavor=None, config=None, endpoint=None):
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return {"flavor": flavor}
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def list_deployments(self, endpoint=None):
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if os.environ.get("raiseError") == "True":
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raise RuntimeError("Error requested")
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return [{"name": f_deployment_name}]
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def get_deployment(self, name, endpoint=None):
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return {"key1": "val1", "key2": "val2"}
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def predict(self, deployment_name=None, inputs=None, endpoint=None):
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return PredictionsResponse.from_json('{"predictions": [1,2,3]}')
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def explain(self, deployment_name=None, df=None, endpoint=None):
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return "1"
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def create_endpoint(self, name, config=None):
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if config and config.get("raiseError") == "True":
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raise RuntimeError("Error requested")
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return {"name": f_endpoint_name}
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def update_endpoint(self, endpoint, config=None):
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return None
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def delete_endpoint(self, endpoint):
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return None
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def list_endpoints(self):
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return [{"name": f_endpoint_name}]
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def get_endpoint(self, endpoint):
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return {"name": f_endpoint_name}
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def run_local(name, model_uri, flavor=None, config=None):
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print( # noqa: T201
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f"Deployed locally at the key {name} using the model from {model_uri}. "
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f"It's flavor is {flavor} and config is {config}"
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)
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def target_help():
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return "Target help is called"
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