wehub-resource-sync
54 lines
1.2 KiB
Markdown
54 lines
1.2 KiB
Markdown
# Pyfunc model example
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This example demonstrates the use of a pyfunc model with custom inference logic.
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More specifically:
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- train a simple classification model
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- create a _pyfunc_ model that encapsulates the classification model with an attached module for custom inference logic
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## Structure of this example
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This examples contains a `train.py` file that trains a scikit-learn model with iris dataset and uses MLflow Tracking APIs to log the model. The nested **mlflow run** delivers the packaging of `pyfunc` model and `custom_code` module is attached
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to act as a custom inference logic layer in inference time.
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```
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├── train.py
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├── infer_model_code_path.py
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└── custom_code.py
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```
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## Running this example
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1. Train and log the model
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```
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$ python train.py
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```
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or train and log the model using inferred code paths
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```
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$ python infer_model_code_paths.py
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```
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2. Serve the pyfunc model
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```bash
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# Replace <pyfunc_run_id> with the run ID obtained in the previous step
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$ mlflow models serve -m "runs:/<pyfunc_run_id>/model" -p 5001
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```
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3. Send a request
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```
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$ curl http://127.0.0.1:5001/invocations -H 'Content-Type: application/json' -d '{
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"dataframe_records": [[1, 1, 1, 1]]
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}'
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```
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The response should look like this:
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```
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[0]
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```
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