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 (#796)

Browse Source
This commit is contained in:
pollfly
2024-03-12 11:24:42 +02:00
committed by GitHub
parent dce8b12932
commit 67cfbb1ef6
28 changed files with 58 additions and 55 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
docs/getting_started/ds/ds_second_steps.md
View File

@@ -28,7 +28,7 @@ on model performance, saving and comparing these between experiments is sometime
ClearML supports logging `argparse` module arguments out of the box, so once ClearML is integrated into the code, it automatically logs all parameters provided to the argument parser.
You can also log parameter dictionaries (very useful when parsing an external config file and storing as a dict object),
You can also log parameter dictionaries (very useful when parsing an external configuration file and storing as a dict object),
whole configuration files, or even custom objects or [Hydra](https://hydra.cc/docs/intro/) configurations!
```python

2
docs/getting_started/video_tutorials/experiment_management_best_practices.md
View File

@@ -21,7 +21,7 @@ keywords: [mlops, components, Experiment Manager]
<Collapsible type="info" title="Video Transcript">
Welcome to ClearML. In this video, we’ll go deeper into some of the best practices and advanced tricks you can use while working with ClearML experiment management.
<br/>
The first thing to know is that the Task object is the central pillar of both the experiment manager and the orchestration and automation components. This means that if you manage the task well in the experiment phase, it will be much easier to scale to production later down the line.
So let’s take a look at the task object in more detail. We have inputs called hyperparameters and configuration objects for external config files. Outputs can be anything like we saw in the last video. Things like debug images, plots and console output kind of speak for themselves, so the ones we’ll focus on here are scalars and artifacts.