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docs/apps/clearml_param_search.md
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@@ -3,7 +3,7 @@ title: 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.
the optimal values for your tasks' hyperparameters that yield the best performing models.
## How Does `clearml-param-search` Work?
@@ -25,11 +25,11 @@ of the optimization results in table and graph forms.
|Name | Description| Mandatory |
|---|----|---|
|`--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`|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--compute-time-limit`|The maximum compute time in minutes that experiment can consume. If this time limit is exceeded, all jobs are aborted.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--compute-time-limit`|The maximum compute time in minutes that a task can consume. If this time limit is exceeded, all jobs are aborted.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--max-iteration-per-job`|The maximum iterations (of the objective metric) per single job. When iteration maximum is exceeded, the job is aborted.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--max-number-of-concurrent-tasks`|The maximum number of concurrent Tasks (experiments) running at the same time|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--max-number-of-concurrent-tasks`|The maximum number of concurrent Tasks running at the same time|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--min-iteration-per-job`|The minimum iterations (of the objective metric) per single job.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--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`| <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--local`| If set, run the tasks 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`| <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--objective-metric-series`| Objective metric series to maximize/minimize (e.g. 'loss').|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--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](#optimization-objective). |<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--objective-metric-title`| Objective metric title to maximize/minimize (e.g. 'validation').|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
@@ -39,8 +39,8 @@ of the optimization results in table and graph forms.
|`--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](#json_note).|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--pool-period-min`|The time between two consecutive polls (minutes).|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--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.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--queue`|Queue to enqueue the experiments on.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--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`.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--queue`|Queue to enqueue the tasks on.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--save-top-k-tasks-only`| Keep only the top \<k\> performing tasks, and archive the rest of them. Input `-1` to keep all tasks. Default: `10`.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--script`|Script to run the parameter search on. Required unless `--task-id` is specified.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--task-id`|ID of a ClearML task whose hyperparameters will be optimized. Required unless `--script` is specified.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
|`--task-name`|Name of the optimization task. If unspecified, the base Python script's file name is used.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
@@ -109,8 +109,8 @@ clearml-param-search --script base_template_keras_simple.py --params-search "{\"
### 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
* `min` - Least value of the specified objective metric reported at the end of the task
* `max` - Greatest value of the specified objective metric reported at the end of the task
* `min_global` - Least value of the specified objective metric reported at any time in the task
* `max_global` - Greatest value of the specified objective metric reported at any time in the task