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

Update docs (#330)

Browse Source
This commit is contained in:
pollfly
2022-09-15 16:10:11 +03:00
committed by GitHub
parent c8f96230bf
commit 5a65a2654b
43 changed files with 182 additions and 104 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
docs/getting_started/ds/ds_first_steps.md
View File

@@ -14,6 +14,8 @@ pip install clearml
```
## Connect ClearML SDK to the Server
### Local Python
1. Execute the following command to run the ClearML setup wizard:
```bash
@@ -35,7 +37,10 @@ pip install clearml
```
1. Get ClearML credentials. Open the ClearML Web UI in a browser. On the [**SETTINGS > WORKSPACE**](https://app.clear.ml/settings/workspace-configuration)
page, click **Create new credentials** **>** **Copy to clipboard**.
page, click **Create new credentials**.
The **LOCAL PYTHON** tab shows the data required by the setup wizard (a copy to clipboard action is available on
hover)
1. At the command prompt `Paste copied configuration here:`, copy and paste the ClearML credentials.
The setup wizard confirms the credentials.
@@ -74,7 +79,17 @@ pip install clearml
CLEARML setup completed successfully.
```
Now you can integrate ClearML into your code!
Now you can integrate ClearML into your code! Continue [here](#auto-log-experiment).
### Jupyter Notebook
To use ClearML with Jupyter Notebook, you need to configure ClearML Server access credentials for your notebook.
1. Get ClearML credentials. Open the ClearML Web UI in a browser. On the [**SETTINGS > WORKSPACE**](https://app.clear.ml/settings/workspace-configuration)
page, click **Create new credentials**. The **JUPYTER NOTEBOOK** tab shows the commands required to configure your
notebook (a copy to clipboard action is available on hover)
1. Add these commands to your notebook
Now you can use ClearML in your notebook!
## Auto-log Experiment
@@ -99,7 +114,6 @@ Then initialize the Task object in your `main()` function, or the beginning of t
task = Task.init(project_name='great project', task_name='best experiment')
```
Task name is not unique, it's possible to have multiple experiments with the same name.
If the project does not already exist, a new one will be created automatically.
The console should return the following output: