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https://github.com/clearml/clearml-docs
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140 lines
5.2 KiB
Markdown
140 lines
5.2 KiB
Markdown
---
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title: First Steps
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---
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## Install ClearML
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First, [sign up for free](https://app.clear.ml)
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Install the `clearml` python package:
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```bash
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pip install clearml
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```
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## Connect ClearML SDK to the Server
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### Local Python
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1. Execute the following command to run the ClearML setup wizard:
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```bash
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clearml-init
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```
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If the setup wizard's response indicates that a configuration file already exists, follow the instructions in
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[here](../../deploying_clearml/clearml_config_for_clearml_server.md#add-clearml-to-a-configuration-file). The wizard
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does not edit or overwrite existing configuration files.
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1. The setup wizard prompts for ClearML credentials.
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```console
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Please create new clearml credentials through the settings page in your `clearml-server` web app,
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or create a free account at https://app.clear.ml/settings/webapp-configuration
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In the settings > workspace page, press "Create new credentials", then press "Copy to clipboard".
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Paste copied configuration here:
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```
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1. Get ClearML credentials. Open the ClearML Web UI in a browser. On the [**SETTINGS > WORKSPACE**](https://app.clear.ml/settings/workspace-configuration)
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page, click **Create new credentials**.
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The **LOCAL PYTHON** tab shows the data required by the setup wizard (a copy to clipboard action is available on
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hover)
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1. At the command prompt `Paste copied configuration here:`, copy and paste the ClearML credentials.
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The setup wizard confirms the credentials.
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```console
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Detected credentials key="********************" secret="*******"
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```
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1. Enter the ClearML Server web server URL, or press **Enter** to accept the default which is detected from the
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credentials.
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```console
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WEB Host configured to: [https://app.<your-domain>]
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```
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1. Enter the ClearML Server API server URL, or press **Enter** to accept the default value which is based on the previous response:
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```console
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API Host configured to: [https://api.<your-domain>]
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```
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1. Enter the ClearML Server file server URL, or press **Enter** to accept the default value which is based on the previous response:
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```console
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File Store Host configured to: [files.<your-domain>]
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```
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The wizard responds with a configuration and directs to the ClearML Server.
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```console
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CLEARML Hosts configuration:
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Web App: https://app.<your-domain>
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API: https://api.<your-domain>
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File Store: https://files.<your-domain>
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Verifying credentials ...
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Credentials verified!
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New configuration stored in /home/<username>/clearml.conf
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CLEARML setup completed successfully.
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```
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Now you can integrate ClearML into your code! Continue [here](#auto-log-experiment).
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### Jupyter Notebook
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To use ClearML with Jupyter Notebook, you need to configure ClearML Server access credentials for your notebook.
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1. Get ClearML credentials. Open the ClearML Web UI in a browser. On the [**SETTINGS > WORKSPACE**](https://app.clear.ml/settings/workspace-configuration)
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page, click **Create new credentials**. The **JUPYTER NOTEBOOK** tab shows the commands required to configure your
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notebook (a copy to clipboard action is available on hover)
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1. Add these commands to your notebook
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Now you can use ClearML in your notebook!
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## Auto-log Experiment
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In ClearML, experiments are organized as [Tasks](../../fundamentals/task.md).
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ClearML will automatically log your experiment and code, including outputs and parameters from popular ML frameworks,
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once you integrate the ClearML [SDK](../../clearml_sdk/clearml_sdk.md) with your code. To control what ClearML automatically logs, see this [FAQ](../../faq.md#controlling_logging).
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At the beginning of your code, import the `clearml` package:
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```python
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from clearml import Task
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```
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:::note Full Automatic Logging
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To ensure full automatic logging, it is recommended to import the `clearml` package at the top of your entry script.
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:::
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Then initialize the Task object in your `main()` function, or the beginning of the script.
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```python
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task = Task.init(project_name='great project', task_name='best experiment')
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```
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If the project does not already exist, a new one will be created automatically.
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The console should return the following output:
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```
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ClearML Task: created new task id=1ca59ef1f86d44bd81cb517d529d9e5a
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2021-07-25 13:59:09
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ClearML results page: https://app.clear.ml/projects/4043a1657f374e9298649c6ba72ad233/experiments/1ca59ef1f86d44bd81cb517d529d9e5a/output/log
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2021-07-25 13:59:16
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```
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**That’s it!** You are done integrating ClearML with your code :)
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Now, [command-line arguments](../../fundamentals/hyperparameters.md#tracking-hyperparameters), [console output](../../fundamentals/logger.md#types-of-logged-results) as well as Tensorboard and Matplotlib will automatically be logged in the UI under the created Task.
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<br/>
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Sit back, relax, and watch your models converge :) or continue to see what else can be done with ClearML [here](ds_second_steps.md).
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## YouTube Playlist
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Or watch the YouTube Getting Started Playlist on our YouTube Channel!
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[](https://www.youtube.com/watch?v=bjWwZAzDxTY&list=PLMdIlCuMqSTnoC45ME5_JnsJX0zWqDdlO&index=2)
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