ClearML/clearml-docs
1
0
Fork 0
mirror of https://github.com/clearml/clearml-docs synced 2025-06-26 18:17:44 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

Change headings to title caps (#62)

Browse Source
This commit is contained in:
pollfly
2021-09-09 13:17:46 +03:00
committed by GitHub
parent de82df937e
commit c2d8707572
77 changed files with 337 additions and 336 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md
View File

@@ -7,7 +7,7 @@ example script demonstrates hyperparameter optimization, which is automated by u
<a class="tr_top_negative" name="strategy"></a>
## Set the search strategy for optimization
## Set the Search Strategy for Optimization
A search strategy is required for the optimization, as well as a search strategy optimizer class to implement that strategy.
@@ -57,7 +57,7 @@ the `RandomSearch` for the search strategy.
'we will be using RandomSearch strategy instead')
aSearchStrategy = RandomSearch
## Define a callback
## Define a Callback
When the optimization starts, a callback is provided that returns the best performing set of hyperparameters. In the script,
the `job_complete_callback` function returns the ID of `top_performance_job_id`.
@@ -73,7 +73,7 @@ the `job_complete_callback` function returns the ID of `top_performance_job_id`.
if job_id == top_performance_job_id:
print('WOOT WOOT we broke the record! Objective reached {}'.format(objective_value))
## Initialize the optimization Task
## Initialize the Optimization Task
Initialize the Task, which will be stored in **ClearML Server** when the code runs. After the code runs at least once, it
can be [reproduced](../../../webapp/webapp_exp_reproducing.md) and [tuned](../../../webapp/webapp_exp_tuning.md).
@@ -89,7 +89,7 @@ the project **Hyper-Parameter Optimization**, which can be seen in the **ClearML
task_type=Task.TaskTypes.optimizer,
reuse_last_task_id=False)
## Set up the arguments
## Set Up the Arguments
Create an arguments dictionary that contains the ID of the Task to optimize, and a Boolean indicating whether the
optimizer will run as a service, see [Running as a service](#running-as-a-service).
@@ -112,7 +112,7 @@ to optimize a different experiment, see [tuning experiments](../../../webapp/web
args['template_task_id'] = Task.get_task(
project_name='examples', task_name='Keras HP optimization base').id
## Instantiate the optimizer object
## Instantiate the Optimizer Object
Instantiate an [automation.optimization.HyperParameterOptimizer](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md)
object, setting the optimization parameters, beginning with the ID of the experiment to optimize.
@@ -170,7 +170,7 @@ Specify the remaining parameters, including the time limit per Task (minutes), p
<a class="tr_top_negative" name="service"></a>
## Running as a service
## Running as a Service
The optimization can run as a service, if the `run_as_service` argument is set to `true`. For more information about
running as a service, see [ClearML Agent services container](../../../clearml_agent.md#services-mode)