ClearML Agent on Google Colab

Google Colab is a common development environment for data scientists. It supports a convenient IDE as well as compute provided by Google.

Users can transform a Google Colab instance into an available resource in ClearML using ClearML Agent.

This tutorial goes over how to create a ClearML worker node in a Google Colab notebook. Once the worker is up and running, users can send Tasks to be executed on Google Colab's hardware.

Prerequisites

Be signed up for ClearML (or have a server deployed).
Have a Google account to access Google Colab.

Steps

Open up this Google Colab notebook.

Run the first cell, which installs all the necessary packages:

!pip install git+https://github.com/allegroai/clearml
!pip install clearml-agent

Run the second cell, which exports this environment variable:
```
! export MPLBACKEND=TkAg
```
This environment variable makes Matplotlib work in headless mode, so it won't output graphs to the screen.
Create new credentials. Go to your Settings page > WORKSPACE section. Under App Credentials, click + Create new credentials, and copy the information that pops up.

Set the credentials. In the third cell, enter your own credentials:

from clearml import Task

Task.set_credentials(
     api_host="https://api.clear.ml", 
     web_host="https://app.clear.ml", 
     files_host="https://files.clear.ml", 
     key='6ZHX9UQMYL874A1NE8', 
     secret='=2h6#%@Y&m*tC!VLEXq&JI7QhZPKuJfbaYD4!uUk(t7=9ENv'
)

In the fourth cell, launch a clearml-agent that will listen to the default queue:
```
!clearml-agent daemon --queue default
```
For additional options for running clearml-agent, see the clearml-agent reference.
After executing cell 4, the worker appears in the Orchestration page of your server. Clone experiments and enqueue them to your hearts content! The clearml-agent will fetch experiments and execute them using the Google Colab hardware.

Prerequisites​

Steps​

Prerequisites

Steps