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63 lines
2.4 KiB
Markdown
63 lines
2.4 KiB
Markdown
---
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title: ClearML Agent on Google Colab
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---
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[Google Colab](https://colab.research.google.com) is a common development environment for data scientists. It offers a convenient IDE as well as
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compute provided by google.
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Users can transform a Google Colab instance into an available resource in ClearML using [ClearML Agent](../../clearml_agent.md).
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This tutorial goes over how to create a ClearML worker node in a Google Colab notebook. Once the worker is up
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and running, users can send Tasks to be executed on Google Colab's HW.
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## Prerequisites
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* Be signed up for ClearML (Or have a server deployed).
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* Have a Google account to access Google Colab
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## Steps
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1. Open up [this Google Colab notebook](https://colab.research.google.com/github/allegroai/clearml/blob/master/examples/clearml_agent/clearml_colab_agent.ipynb).
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1. Run the first cell, which installs all the necessary packages:
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```
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!pip install git+https://github.com/allegroai/clearml
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!pip install clearml-agent
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```
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1. Run the second cell, which exports this environment variable:
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```
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! export MPLBACKEND=TkAg
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```
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This environment variable makes Matplotlib work in headless mode, so it won't output graphs to the screen.
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1. Create new credentials.
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Go to your [**Settings**](https://app.clear.ml/settings/workspace-configuration) page > **WORKSPACE** section.
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Under **App Credentials**, click **+ Create new credentials**, and copy the information that pops up.
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1. Set the credentials.
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In the third cell, enter your own credentials:
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```python
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from clearml import Task
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Task.set_credentials(
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api_host="https://api.clear.ml",
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web_host="https://app.clear.ml",
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files_host="https://files.clear.ml",
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key='6ZHX9UQMYL874A1NE8',
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secret='=2h6#%@Y&m*tC!VLEXq&JI7QhZPKuJfbaYD4!uUk(t7=9ENv'
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)
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```
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1. In the fourth cell, launch a `clearml-agent` that will listen to the `default` queue:
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```
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!clearml-agent daemon --queue default
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```
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For additional options for running `clearml-agent`, see the [clearml-agent reference](../../clearml_agent/clearml_agent_ref.md).
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After cell 4 is executed, the worker should now appear in the [**Workers & Queues**](../../webapp/webapp_workers_queues.md)
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page of your server. Clone experiments and enqueue them to your hearts content! The `clearml-agent` will fetch
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experiments and execute them using the Google Colab hardware.
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