machine-learning-at-scale/examples/train/ray-flaml

FLAML AutoML with RAY

This example shows how to use FLAML to train a model on a dataset using RAY on Azure Machine Learning.

FLAML with RAY

FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. FLAML support Ray Tune for distributed search.

Prerequisites

• Azure Machine Learning Workspace
  • Compute Clusters for Ray
  • Compute Instance with Azure ML CLI 2.0 installed

Getting Started

1. Create a job (Azure ML CLI 2.0 + YML configuration file) from VSCode Azure ML Extension.

   cd examples/train/ray-flaml/
   az ml job create --file job.yml --stream
   

2. Access to Azure ML studio and see Experiment logs.

• In Experiment, paramters & metrics is logged. And you can check system performance in Monitoring tab like below.

4. Launch tensorboard to see the metrics.

• Install Python packages for Tensorboard with Azure Machine Learning.

  pip install azureml-tensorboard tensorboard
  

• Input Run ID in the first cell of azureml-tensorboard.ipynb.

  # if you Run ID is xxxxxxx
  run_id = "xxxxxxx"
  run = Run.get(workspace=ws, run_id=run_id)
  

• Start Tensorboard in the second cell and see the list of trials like below.

  tb = Tensorboard([run], local_root="logs/azureml", port=6006)
  tb.start()
  

  

Reference

• FLAML: A Framework for Learning from Data