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

Small edits

Browse Source
This commit is contained in:
revital 2023-07-23 08:40:37 +03:00
parent 7f9a148285
commit 24c551f917
3 changed files with 14 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
docs/fundamentals/hpo.md
View File

@ -121,14 +121,14 @@ optimization.
## Optimizer Execution Options
The `HyperParameterOptimizer` provides options to launch the optimization tasks locally or through a ClearML [queue](agents_and_queues.md#what-is-a-queue).
Start a `HyperParameterOptimizer` instance using either [`HyperParameterOptimizer.start`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md#start)
or [`HyperParameterOptimizer.start_locally`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md#start_locally).
Both methods run the optimizer controller locally. The `start` method launches the base task clones through a queue
specified when instantiating the controller, while `start_locally` runs the tasks locally.
Start a `HyperParameterOptimizer` instance using either [`HyperParameterOptimizer.start()`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md#start)
or [`HyperParameterOptimizer.start_locally()`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md#start_locally).
Both methods run the optimizer controller locally. `start()` launches the base task clones through a queue
specified when instantiating the controller, while `start_locally()` runs the tasks locally.
:::tip Remote Execution
You can also launch the optimizer controller through a queue by using the [`Task.execute_remotely`](../references/sdk/task.md#execute_remotely)
method before starting the optimizer.
You can also launch the optimizer controller through a queue by using [`Task.execute_remotely()`](../references/sdk/task.md#execute_remotely)
before starting the optimizer.
:::
@ -149,3 +149,7 @@ ClearML also provides `clearml-param-search`, a CLI utility for managing the hyp
ClearML provides the [Hyperparameter Optimization GUI application](../webapp/applications/apps_hpo.md) for launching and
managing the hyperparameter optimization process.
:::info Pro Plan Offering
The ClearML HPO App is available under the ClearML Pro plan
:::

6
docs/integrations/hydra.md
View File

@ -26,10 +26,10 @@ The agent executes the code with the modifications you made in the UI, even over
Clone your experiment, then modify your Hydra parameters via the UI in one of the following ways:
* Modify the OmegaConf directly:
1. In the experiment’s **CONFIGURATION > HYPERPARAMETERS > HYDRA** section, set `_allow_omegaconf_edit_` to `True`
1. In the experiment’s **CONFIGURATION > CONFIGURATION OBJECTS > OmegaConf** section, modify the OmegaConf values
1. In the experiment’s **CONFIGURATION > HYPERPARAMETERS > HYDRA** section, set `_allow_omegaconf_edit_` to `True`
1. In the experiment’s **CONFIGURATION > CONFIGURATION OBJECTS > OmegaConf** section, modify the OmegaConf values
* Add an experiment hyperparameter:
1. In the experiment’s **CONFIGURATION > HYPERPARAMETERS > HYDRA** section, make sure `_allow_omegaconf_edit_` is set
1. In the experiment’s **CONFIGURATION > HYPERPARAMETERS > HYDRA** section, make sure `_allow_omegaconf_edit_` is set
to `False`
1. In the same section, click `Edit`, which gives you the option to add parameters. Input parameters from the OmegaConf
that you want to modify using dot notation. For example, if your OmegaConf looks like this:

2
docs/integrations/yolov5.md
View File

@ -142,7 +142,7 @@ New dataset created id=<dataset-id>
```
### Run Training Using a ClearML Dataset
Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 models:
Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 models:
```commandline
python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache