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 terminology (#1028)

Browse Source
This commit is contained in:
pollfly
2025-02-06 17:31:11 +02:00
committed by GitHub
parent 30805e474d
commit b12b71d835
158 changed files with 857 additions and 855 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
docs/integrations/tao.md
View File

@@ -14,7 +14,7 @@ logging metrics, model files, plots, debug samples, and more, so you can gain mo
pip install clearml
```
1. To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 server options:
1. To keep track of your tasks and/or data, ClearML needs to communicate to a server. You have 2 server options:
* Sign up for free to the [ClearML Hosted Service](https://app.clear.ml/)
* Set up your own server, see [here](../deploying_clearml/clearml_server.md)
1. Connect the ClearML SDK to the server by creating credentials (go to the top right in the UI to **Settings > Workspace > Create new credentials**),
@@ -69,7 +69,7 @@ logging metrics, model files, plots, debug samples, and more, so you can gain mo
1. Customize the ClearML Task through TAO Toolkit. Under `visualizer.clearml_config` of your training configuration file,
you can set the following:
* `task` - Name of the ClearML Task. In order to maintain a unique name per run, TAO Toolkit appends to the name
string a timestamp of the experiment creation time.
string a timestamp of the task creation time.
* `project` - Project where the task will be stored
* `tags` - Tags to label the task.
@@ -94,10 +94,10 @@ You can view all of this captured information in the [ClearML Web UI](../webapp/
![TAO UI plots](../img/integrations_nvidia_tao_plots.png)
## Remote Execution
ClearML logs all the information required to reproduce an experiment on a different machine (installed packages,
ClearML logs all the information required to reproduce a task on a different machine (installed packages,
uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is
enqueued, the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the
experiment manager.
task manager.
Deploy a ClearML Agent onto any machine (e.g. a cloud VM, a local GPU machine, your own laptop) by simply running
the following command on it:
@@ -117,7 +117,7 @@ and shuts down instances as needed, according to a resource budget that you set.
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:
* Clone the experiment
* Clone the task
* Edit the hyperparameters and/or other details
* Enqueue the task