wehub-resource-sync
44 lines
1.7 KiB
Markdown
44 lines
1.7 KiB
Markdown
## Iris classification example with MLflow
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This example demonstrates training a classification model on the Iris dataset, scripting the model with TorchScript, logging the
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scripted model to MLflow using
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[`mlflow.pytorch.log_model`](https://mlflow.org/docs/latest/python_api/mlflow.pytorch.html#mlflow.pytorch.log_model), and
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loading it back for inference using
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[`mlflow.pytorch.load_model`](https://mlflow.org/docs/latest/python_api/mlflow.pytorch.html#mlflow.pytorch.load_model)
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### Running the code
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To run the example via MLflow, navigate to the `mlflow/examples/pytorch/torchscript/IrisClassification` directory and run the command
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```
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mlflow run .
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```
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This will run `iris_classification.py` with the default set of parameters such as `--max_epochs=5`. You can see the default value in the `MLproject` file.
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In order to run the file with custom parameters, run the command
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```
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mlflow run . -P epochs=X
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```
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where `X` is your desired value for `epochs`.
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If you have the required modules for the file and would like to skip the creation of a conda environment, add the argument `--env-manager=local`.
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```
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mlflow run . --env-manager=local
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```
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Once the code is finished executing, you can view the run's metrics, parameters, and details by running the command
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```
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mlflow server
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```
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and navigating to [http://localhost:5000](http://localhost:5000).
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## Running against a custom tracking server
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To configure MLflow to log to a custom (non-default) tracking location, set the `MLFLOW_TRACKING_URI` environment variable, e.g. via `export MLFLOW_TRACKING_URI=http://localhost:5000/`. For more details, see [the docs](https://mlflow.org/docs/latest/tracking.html#where-runs-are-recorded)
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