ClearML Parameter Search CLI (HPO)

Use the clearml-param-search CLI tool to launch ClearML's automated hyperparameter optimization (HPO). This process finds the optimal values for your experiments' hyperparameters that yield the best performing models.

How Does clearml-param-search Work?

1. Execute clearml-param-search, specifying the base task whose parameters will be optimized, and a set of parameter values and/or ranges to test. This creates an Optimization Task which manages the whole optimization process.
2. clearml-param-search creates multiple clones of the base task: each clone's parameters are set to values from the specified parameter space.
3. Each clone is enqueued for execution by a ClearML Agent.

The Optimization Task records and monitors the cloned tasks' configuration and execution details, and returns a summary of the optimization results in table and graph forms.

Execution Configuration

Command Line Options

Name	Description	Optional
--project-name	Name of the project in which the optimization task will be created. If the project does not exist, it is created. If unspecified, the repository name is used.
--task-name	Name of the optimization task. If unspecified, the base Python script's file name is used.
--task-id	ID of a ClearML task whose hyperparameters will be optimized. Required unless --script is specified.
--script	Script to run the parameter search on. Required unless --task-id is specified.
--queue	Queue to enqueue the experiments on.
--params-search	Parameters space for optimization. See more information in Specifying the Parameter Space.
--params-override	Additional parameters of the base task to override for this parameter search. Use the following JSON format for each parameter: {"name": "param_name", "value": <new_value>}. Windows users, see JSON format note.
--objective-metric-title	Objective metric title to maximize/minimize (e.g. 'validation').
--objective-metric-series	Objective metric series to maximize/minimize (e.g. 'loss').
--objective-metric-sign	Optimization target, whether to maximize or minimize the value of the objective metric specified. Possible values: "min", "max", "min_global", "max_global". For more information, see Optimization Objective.
--optimizer-class	The optimizer to use. Possible values are: OptimizerOptuna (default), OptimizerBOHB, GridSearch, RandomSearch. For more information, see Supported Optimizers.
--optimization-time-limit	The maximum time (minutes) for the optimization to run. The default is None, indicating no time limit.
--compute-time-limit	The maximum compute time in minutes that experiment can consume. If this time limit is exceeded, all jobs are aborted.
--pool-period-min	The time between two consecutive polls (minutes).
--total-max-jobs	The total maximum jobs for the optimization process. The default value is None for unlimited.
--min-iteration-per-job	The minimum iterations (of the objective metric) per single job.
--max-iteration-per-job	The maximum iterations (of the objective metric) per single job. When iteration maximum is exceeded, the job is aborted.
--max-number-of-concurrent-tasks	The maximum number of concurrent Tasks (experiments) running at the same time
--args	List of <argument>=<value> strings to pass to the remote execution. Currently only argparse/click/hydra/fire arguments are supported. Example: --args lr=0.003 batch_size=64
--local	If set, run the experiments locally. Notice that no new python environment will be created. The --script parameter must point to a local file entry point and all arguments must be passed with --args
--save-top-k-tasks-only	Keep only the top \<k> performing tasks, and archive the rest of the experiments. Input -1 to keep all tasks. Default: 10.
--time-limit-per-job	Maximum execution time per single job in minutes. When the time limit is exceeded, the job is aborted. Default: no time limit.

Specifying the Parameter Space

To configure the parameter values to test in the hyperparameter optimization process, pass through the --params-search option the parameter search specification as a list of the parameters definitions.

Use the following JSON format for each parameter:

{
    "name": str,  # Name of the parameter you want to optimize
    "type": Union["LogUniformParameterRange", "UniformParameterRange", "UniformIntegerParameterRange", "DiscreteParameterRange"],
    # Additional fields depending on type - see below
}

The following are the parameter type options and their corresponding fields:

• LogUniformParameterRange
  • "min_value": float - The minimum exponent sample to use for logarithmic uniform random sampling
  • "max_value": float - The maximum exponent sample to use for logarithmic uniform random sampling
  • "base": Optional[float] - The base used to raise the sampled exponent. Default: 10
  • "step_size": Optional[float] - Step size (quantization) for value sampling. Default: None
  • "include_max_value": Optional[bool] - Whether to include the max_value in range. Default: True
• UniformParameterRange
  • "min_value": float - The minimum value to use for uniform random sampling
  • "max_value": float - The maximum sample to use for uniform random sampling
  • "step_size": Optional[float] - Step size (quantization) for value sampling. Default: None
  • "include_max_value": Optional[bool] - Whether to include the max_value in range. Default: True
• UniformIntegerParameterRange
  • "min_value": float - The minimum value to use for uniform random sampling
  • "max_value": float- The maximum value sample to use for uniform random sampling
  • "step_size": Optional[int] - Default: 1
  • "include_max_value": Optional[bool] - Whether to include the max_value in range. Default: True
• DiscreteParameterRange
  • "values": List[Any]- A list of valid parameter values to sample from

For example: to specify a parameter search over uniform ranges of layer_1 and layer_2 sizes between 128 and 512 (in jumps of 128) with varying batch sizes of 96, 128, and 160, use the following command:

clearml-param-search --script keras_simple.py --params-search '{"type": "UniformIntegerParameterRange", "name": "General/layer_1", "min_value": 128, "max_value": 512, "step_size": 128}' '{"type": "UniformIntegerParameterRange", "name": "General/layer_2", "min_value": 128, "max_value": 512, "step_size": 128}' '{"type": "DiscreteParameterRange", "name": "General/batch_size", "values": [96, 128, 160]}' --params-override '{"name": "epochs", "value": 30}'  --objective-metric-title validation --objective-metric-series epoch_accuracy --objective-metric-sign max --optimizer-class OptimizerOptuna --queue default

JSON format for Windows Users

Windows users must add escapes (\) when using quotation marks (") in JSON format inputs. For example:

clearml-param-search --script base_template_keras_simple.py --params-search "{\"type\": \"UniformIntegerParameterRange\", \"name\": \"General/layer_1\", \"min_value\": 128, \"max_value\": 512, \"step_size\": 128}" "{\"type\": \"UniformIntegerParameterRange\", \"name\": \"General/layer_2\", \"min_value\": 128, \"max_value\": 512, \"step_size\": 128}" "{\"type\": \"DiscreteParameterRange\", \"name\": \"General/batch_size\", \"values\": [96, 128, 160]}" --params-override "{\"name\": \"epochs\", \"value\": 30}"  --objective-metric-title validation --objective-metric-series epoch_accuracy --objective-metric-sign max --optimizer-class OptimizerOptuna --max-iteration-per-job 30 --queue default

Optimization Objective

Use the --objective-metric-sign to specify which optimum your optimization process should use. The options are:

• min - Least value of the specified objective metric reported at the end of the experiment
• max - Greatest value of the specified objective metric reported at the end of the experiment
• min_global - Least value of the specified objective metric reported at any time in the experiment
• max_global - Greatest value of the specified objective metric reported at any time in the experiment