wehub-resource-sync
41 lines
2.5 KiB
Markdown
41 lines
2.5 KiB
Markdown
## MLflow examples
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### Quick Start example
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- `quickstart/mlflow_tracking.py` is a basic example to introduce MLflow concepts.
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## Tutorials
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Various examples that depict MLflow tracking, project, and serving use cases.
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- `h2o` depicts how MLflow can be use to track various random forest architectures to train models
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for predicting wine quality.
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- `hyperparam` shows how to do hyperparameter tuning with MLflow and some popular optimization libraries.
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- `keras` modifies
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[a Keras classification example](https://github.com/keras-team/keras/blob/ed07472bc5fc985982db355135d37059a1f887a9/examples/reuters_mlp.py)
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and uses MLflow's `mlflow.tensorflow.autolog()` API to automatically log metrics and parameters
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to MLflow during training.
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- `multistep_workflow` is an end-to-end of a data ETL and ML training pipeline built as an MLflow
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project. The example shows how parts of the workflow can leverage from previously run steps.
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- `pytorch` uses CNN on MNIST dataset for character recognition. The example logs TensorBoard events
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and stores (logs) them as MLflow artifacts.
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- `remote_store` has a usage example of REST based backed store for tracking.
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- `r_wine` demonstrates how to log parameters, metrics, and models from R.
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- `sklearn_elasticnet_diabetes` uses the sklearn diabetes dataset to predict diabetes progression
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using ElasticNet.
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- `sklearn_elasticnet_wine_quality` is an example for MLflow projects. This uses the Wine
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Quality dataset and Elastic Net to predict quality. The example uses `MLproject` to set up a
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Conda environment, define parameter types and defaults, entry point for training, etc.
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- `sklearn_logistic_regression` is a simple MLflow example with hooks to log training data to MLflow
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tracking server.
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- `supply_chain_security` shows how to strengthen the security of ML projects against supply-chain attacks by enforcing hash checks on Python packages.
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- `tensorflow` contains end-to-end one run examples from train to predict for TensorFlow 2.8+ It includes usage of MLflow's
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`mlflow.tensorflow.autolog()` API, which captures TensorBoard data and logs to MLflow with no code change.
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- `docker` demonstrates how to create and run an MLflow project using docker (rather than conda)
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to manage project dependencies
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- `johnsnowlabs` gives you access to [20.000+ state-of-the-art enterprise NLP models in 200+ languages](https://nlp.johnsnowlabs.com/models) for medical, finance, legal and many more domains.
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## Demos
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- `demos` folder contains notebooks used during MLflow presentations.
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