wehub-resource-sync
61 lines
1.8 KiB
Markdown
61 lines
1.8 KiB
Markdown
# Sktime Example
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This example trains a `Sktime` NaiveForecaster model using the Longley dataset for
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forecasting with exogenous variables. It shows a custom model type implementation
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that logs the training hyper-parameters, evaluation metrics and the trained model
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as an artifact.
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## Running the code
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Run the `train.py` module to create a new MLflow experiment and to
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compute interval forecasts loading the trained model in native `sktime`
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flavor and `pyfunc` flavor:
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```
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python train.py
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```
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To view the newly created experiment and logged artifacts open the MLflow UI:
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```
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mlflow server
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```
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## Model serving
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This section illustrates an example of serving the `pyfunc` flavor to a local REST
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API endpoint and subsequently requesting a prediction from the served model. To serve the model run the command below where you substitute the run id printed during execution of the `train.py` module:
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```
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mlflow models serve -m runs:/<run_id>/model --env-manager local --host 127.0.0.1
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```
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Open a new terminal and run the `score_model.py` module to request a prediction from the served model (for more details read the [MLflow deployment API reference](https://mlflow.org/docs/latest/models.html#deploy-mlflow-models)):
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```
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python score_model.py
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```
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## Running the code as a project
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You can also run the code as a project as follows:
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```
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mlflow run .
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```
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## Running unit tests
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The `test_sktime_model_export.py` module includes a number of tests that can be
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executed as follows:
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```
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pytest test_sktime_model_export.py
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```
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While these tests will depend on the specifics of each individual flavor and in particular the design of the model wrapper interface (e.g. `_SktimeModelWrapper`), the above module can provide some orientation
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for the type of tests that can be useful when creating a new custom model flavor.
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