Deploying ClearML as an Azure ML Alternative

To complete and run the ClearML tutorial for hyperparameter optimization, follow these steps:

1. Ensure you have completed the previous experiments:

   • Run 01_first_experiment.py to create a baseline model.
   • Run 02_pipeline.py to set up a pipeline that processes data and trains models.

2. Run Hyperparameter Optimization (03_hpo.py):

   The script provided sets up hyperparameter optimization using ClearML's automation capabilities. Here are the key steps in the script:

   python
    
   1from clearml import Task
   2from clearml.automation import (
   3    HyperParameterOptimizer,
   4    DiscreteParameterRange,
   5    UniformIntegerParameterRange,
   6    RandomSearch
   7)
   8
   9# Find the last completed task (baseline model)
   10tasks = Task.get_tasks(project_name='ClearML Tutorial', 
   11                       task_filter={'status': ['completed', 'published']}, 
   12                       task_name='01_First_Experiment')
   13base_task_id = tasks[-1].id
   14
   15# Initialize a new ClearML task for HPO
   16Task.init(project_name='ClearML Tutorial', task_name='03_Hyperparameter_Optimization', 
   17          task_type=Task.Task

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