Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating Custom Libraries
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MLflow is an open-source platform designed to streamline the machine learning lifecycle by providing tools for tracking experiments, packaging models, and supporting various deployment tools. The latest updates include Java API integration, support for popular frameworks like PyTorch and Keras, and enhancements in user experience and functionality for managing multi-step workflows and hyperparameter tuning. The document also offers resources for getting started with MLflow and examples for practical applications such as predicting wine quality.
![Model Based Hyperparam Optimization
1. Select “best parameters” based on current model.
Params[n+1] = Select(Model[n])
2. Obtain new data points by training the model with new parameters
Metric[n+1] = Train(Params[n+1])
3. Use new data points to update the model.
Model[n+1] = Update(Model[n], Metric[n+1])](https://image.slidesharecdn.com/finalmesospheremlflowmeetupslides-180924225236/85/Advanced-MLflow-Multi-Step-Workflows-Hyperparameter-Tuning-and-Integrating-Custom-Libraries-16-320.jpg)
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Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating Custom Libraries
- 1. Advanced MLflow Matei Zaharia, Sid Murching, Tomas Nykodym September 20th, 2018 1
- 2. What is MLflow? Tracking Record and query experiments: code, data, config, results Projects Packaging format for reproducible runs on any platform Models General model format that supports diverse deployment tools Open source platform to accelerate the ML lifecycle
- 3. What’s New in MLflow? Many updates since our last meetup at MLflow 0.2.1! • Java API • PyTorch, Keras, H2O, GCS, SFTP integrations • APIs for tags & querying run history • Optimized Spark ML serving with MLeap • UX improvements 3
- 4. What’s New in MLflow? 4
- 5. What’s New in MLflow? 5
- 6. Ongoing Development R API for MLflow (led by RStudio) • See Javier’s talk later in this meetup! CRUD UI and APIs (naming, annotating & deleting runs) Improved experiment view and search UX 6
- 7. Learning More About MLflow pip install mlflow to get started Find docs & examples at mlflow.org tinyurl.com/mlflow-slack 7
- 8. This Meetup Go beyond the basics to show how to use MLflow’s components for complex ML workflows • Multi-step workflows with caching • Hyperparameter tuning 8
- 10. Project Spec Code DataConfig Local Execution Remote Execution
- 11. MLflow Projects ● With projects: parametrized, dependency-agnostic runs of arbitrary code ● Can chain projects into multistep workflows ● Tracking server: source of truth for output of individual steps 11
- 12. Multistep Workflows Can debug & develop steps independently 12
- 13. ● Find this example at mlflow/examples/multistep_workflow ● MovieLens: given user and movie, predict a rating Demo 13
- 14. Demo 1414
- 15. Hyperparameter Optimization ML Algorithms have many parameters affecting the performance: ● Learning rate, momentum, network layers count and size, … Parameter Selection Strategies: ● Manual ○ Depends on the data scientists skills, high variance, error prone ● Algorithmic ○ Reduced variance, less bias, lower chance of error ○ Strategies include grid search, random search and model based optimization 15
- 16. Model Based Hyperparam Optimization 1. Select “best parameters” based on current model. Params[n+1] = Select(Model[n]) 2. Obtain new data points by training the model with new parameters Metric[n+1] = Train(Params[n+1]) 3. Use new data points to update the model. Model[n+1] = Update(Model[n], Metric[n+1])
- 17. HyperParameter Tuning With MLfLow 17 HyperParam Search Run Train Model Run mlflow.log_metric()mlflow.get_metric() Projects Tracking Models mlflow run ... Logged Model mlflow.log_artifact
- 18. MLflow HyperParameter Example You can find this example at mlflow/examples/hyperparam. Goal: predict wine quality from measured properties • data: acidity, sugar content, chlorides, alcohol, ... " • target: quality score, integer between 3 and 9 • metric: “rmse” The MLproject has following entry points: • train - train deep learning model with Keras, has two tunable parameters: learning rate and momentum • hyperparam train with random, hyperopt, gpyopt 18
- 19. Thank you 19