MLflow code for Spark Summit 2019.
Session: Managing the Complete Machine Learning Lifecycle with MLflow.
pip install mlflow==0.9.1
pip install matplotlib
pip install pyarrow
virtualenv mlflow_server
source mlflow_server/bin/activate
mlflow server --host 0.0.0.0 --port 5000 --backend-store-uri $PWD/mlruns --default-artifact-root $PWD/mlruns
Before running an experiment:
export MLFLOW_TRACKING_URI=http://localhost:5000
- hello_world - Hello World
- sklearn - Scikit learn model
- pyspark - PySpark model
- scala_spark - Scala Spark ML model using the Java client
- search - Shows new MLflow 0.9.1 Search feature
- dump - Shows usage of some mlflow.tracking package methods
- best_run - Finds the best model run