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Change terminology (#1028)

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@@ -15,7 +15,7 @@ All you have to do is install and set up ClearML:
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**),
@@ -25,7 +25,7 @@ All you have to do is install and set up ClearML:
clearml-init
```
That's it! In every training run from now on, the ClearML experiment
That's it! In every training run from now on, the ClearML task
manager will capture:
* Source code and uncommitted changes
* Hyperparameters - PyTorch trainer [parameters](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/trainer#transformers.TrainingArguments)
@@ -52,17 +52,17 @@ You can see all the captured data in the task's page of the ClearML [WebApp](../
![transformers scalars](../img/integrations_transformers_scalars.png)
Additionally, you can view all of your Transformers runs tracked by ClearML in the [Experiments Table](../webapp/webapp_model_table.md).
Additionally, you can view all of your Transformers runs tracked by ClearML in the [Task Table](../webapp/webapp_model_table.md).
Add custom columns to the table, such as mAP values, so you can easily sort and see what is the best performing model.
You can also select multiple experiments and directly [compare](../webapp/webapp_exp_comparing.md) them.
You can also select multiple tasks and directly [compare](../webapp/webapp_exp_comparing.md) them.
See an example of Transformers and ClearML in action [here](../guides/frameworks/huggingface/transformers.md).
## 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:
@@ -82,7 +82,7 @@ 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