mirror of
https://github.com/vale981/ray
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29 lines
875 B
Python
29 lines
875 B
Python
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# flake8: noqa
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# isort: skip_file
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# __mlflow_checkpoint_start__
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from ray.air.checkpoint import Checkpoint
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from sklearn.ensemble import RandomForestClassifier
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import mlflow.sklearn
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# Create an sklearn classifier
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clf = RandomForestClassifier(max_depth=7, random_state=0)
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# ... e.g. train model with clf.fit()
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# Save model using MLflow
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mlflow.sklearn.save_model(clf, "model_directory")
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# Create checkpoint object from path
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checkpoint = Checkpoint.from_directory("model_directory")
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# Write it to some other directory
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checkpoint.to_directory("other_directory")
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# You can also use `checkpoint.to_uri/from_uri` to
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# read from/write to cloud storage
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# We can now use MLflow to re-load the model
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clf = mlflow.sklearn.load_model("other_directory")
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# It is guaranteed that the original data was recovered
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assert isinstance(clf, RandomForestClassifier)
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# __mlflow_checkpoint_end__
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