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First Steps

Install ClearML​

First, sign up for free

Install the clearml python package:

pip install clearml

Connect ClearML SDK to the Server​

Local Python​

  1. Execute the following command to run the ClearML setup wizard:

    clearml-init

    If the setup wizard's response indicates that a configuration file already exists, follow the instructions in here. The wizard does not edit or overwrite existing configuration files.

  2. The setup wizard prompts for ClearML credentials.

    Please create new clearml credentials through the settings page in your `clearml-server` web app, 
    or create a free account at https://app.clear.ml/settings/webapp-configuration
     
    In the settings > workspace page, press "Create new credentials", then press "Copy to clipboard".
    Paste copied configuration here:

  3. Get ClearML credentials. Open the ClearML Web UI in a browser. On the SETTINGS > WORKSPACE 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)

  4. At the command prompt Paste copied configuration here:, copy and paste the ClearML credentials. The setup wizard confirms the credentials. 

    Detected credentials key="********************" secret="*******"

  5. Enter the ClearML Server web server URL, or press Enter to accept the default which is detected from the credentials.

    WEB Host configured to: [https://app.<your-domain>]

  6. Enter the ClearML Server API server URL, or press Enter to accept the default value which is based on the previous response:

    API Host configured to: [https://api.<your-domain>]

  7. Enter the ClearML Server file server URL, or press Enter to accept the default value which is based on the previous response:

    File Store Host configured to: [files.<your-domain>]

    The wizard responds with a configuration and directs to the ClearML Server.

    CLEARML Hosts configuration:
    Web App: https://app.<your-domain>
    API: https://api.<your-domain>
    File Store: https://files.<your-domain>
             
    Verifying credentials ...
    Credentials verified!
     
    New configuration stored in /home/<username>/clearml.conf
    CLEARML setup completed successfully.

Now you can integrate ClearML into your code! Continue here.

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 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)
  2. Add these commands to your notebook

Now you can use ClearML in your notebook!

Auto-log Experiment​

In ClearML, experiments are organized as Tasks.

ClearML automatically logs your experiment and code, including outputs and parameters from popular ML frameworks, once you integrate the ClearML SDK with your code. To control what ClearML automatically logs, see this FAQ.

At the beginning of your code, import the clearml package:

from clearml import Task
Full Automatic Logging

To ensure full automatic logging, it is recommended to import the clearml package at the top of your entry script.

Then initialize the Task object in your main() function, or the beginning of the script.

task = Task.init(project_name='great project', task_name='best experiment')

If the project does not already exist, a new one is created automatically.

The console should display the following output:

ClearML Task: created new task id=1ca59ef1f86d44bd81cb517d529d9e5a
2021-07-25 13:59:09
ClearML results page: https://app.clear.ml/projects/4043a1657f374e9298649c6ba72ad233/experiments/1ca59ef1f86d44bd81cb517d529d9e5a/output/log
2021-07-25 13:59:16

That’s it! You are done integrating ClearML with your code :)

Now, command-line arguments, console output as well as Tensorboard and Matplotlib will automatically be logged in the UI under the created Task.

Sit back, relax, and watch your models converge :) or continue to see what else can be done with ClearML here.

YouTube Playlist​

Or watch the Getting Started playlist on ClearML's YouTube Channel!

Watch the video