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ClearML AWS Autoscaler Service

The ClearML AWS autoscaler example demonstrates how to use the clearml.automation.auto_scaler module to implement a service that optimizes AWS EC2 instance scaling according to a defined instance budget.

The autoscaler periodically polls your AWS cluster and automatically stops idle instances based on a defined maximum idle time or spins up new instances when there aren't enough to execute pending tasks.

Running the ClearML AWS Autoscaler

run the ClearML AWS autoscaler in one of these ways:

Default AMI

The autoscaler services uses by default the NVIDIA Deep Learning AMI v20.11.0-46a68101-e56b-41cd-8e32-631ac6e5d02b AMI

Running the Script

Self deployed ClearML server

A template AWS Auto-Scaler task is available in the DevOps Services project. You can clone it, adapt its configuration to your needs, and enqueue it for execution directly from the ClearML UI.

Launch the autoscaler locally by executing the following command:

python aws_autoscaler.py --run

When the script runs, a configuration wizard prompts for instance details and budget configuration.

  1. Enter the AWS credentials and AWS region name.

    AWS Autoscaler setup wizard
    ---------------------------
    Follow the wizard to configure your AWS auto-scaler service.
    Once completed, you will be able to view and change the configuration in the clearml-server web UI.
    It means there is no need to worry about typos or mistakes :)

    Enter AWS Access Key ID :
    Enter AWS Secret Access Key :
    Enter AWS region name [us-east-1b]:
  2. Enter Git credentials. These are required by ClearML Agent to set up a Task execution environment in an AWS EC2 instance.

    GIT credentials:
    Enter GIT username for repository cloning (leave blank for SSH key authentication): []
    Enter password for user '<username>':

    The wizard reports the Git credentials it will use.

    Git repository cloning will be using user=*************** password=***********
  3. Enter the default Docker image and parameters to use.

    Enter default docker image/parameters to use [nvidia/cuda:10.1-runtime-ubuntu18.04]:
  4. For each AWS EC2 instance type that will be used in the budget, do the following:

    Configure the machine types for the auto-scaler:
    ------------------------------------------------
    Select Amazon instance type ['g4dn.4xlarge']:
    Use spot instances? [y/N]: y
    Select availability zone ['us-east-1b']:
    Select the Amazon Machine Image id ['ami-07c95cafbb788face']:
    Enter the Amazon EBS device ['/dev/xvda']:
    Enter the Amazon EBS volume size (in GiB) [100]:
    Enter the Amazon EBS volume type ['gp2']:

    Name the instance type that was configured. Later in the configuration, use this name to create the budget.

    Select a name for this instance type (used in the budget section) For example 'aws4gpu':

    The wizard prompts whether to select another instance type.

    Define another instance type? [y/N]:
  5. Enter any bash script to run on newly created instances before launching the ClearML Agent.

    Enter any pre-execution bash script to be executed on the newly created instances []:
  6. Configure the AWS autoscaler budget. For each queue that will be used in the budget, enter the maximum number of instances of a selected type that can be spun up simultaneously.

    Define the machines budget:
    -----------------------------

    Select a queue name (for example: 'aws_4gpu_machines') :
    Select a instance type to attach to the queue ['aws-g4dn.xlarge', 'aws-g4dn.8xlarge', 'aws-g4dn.16xlarge']:
    Enter maximum number of 'aws-g4dn.xlarge' instances to spin simultaneously (example: 3) :
  7. If needed, add another instance type to the same queue. The previous step repeats.

    Do you wish to add another instance type to queue? [y/N]:         
  8. The ClearML AWS autoscaler polls instances, and if instances have been idle for the maximum idle time that was specified, the autoscaler spins them down.

    Enter maximum idle time for the auto-scaler to spin down an instance (in minutes) [15]:
    Enter instances polling interval for the auto-scaler (in minutes) [5]:

The configuration is complete, and a new task called AWS Auto-Scaler is created in the DevOps project. The service begins, and the script prints a hyperlink to the Task's log.

CLEARML Task: created new task id=d0ee5309a9a3471d8802f2561da60dfa
CLEARML Monitor: GPU monitoring failed getting GPU reading, switching off GPU monitoring
CLEARML results page: https://app.clear.ml/projects/142a598b5d234bebb37a57d692f5689f/experiments/d0ee5309a9a3471d8802f2561da60dfa/output/log
Running AWS auto-scaler as a service
Execution log https://app.clear.ml/projects/142a598b5d234bebb37a57d692f5689f/experiments/d0ee5309a9a3471d8802f2561da60dfa/output/log

Remote Execution

Using the --remote command line option will enqueue the autoscaler to your services queue once the configuration wizard is complete:

python aws_autoscaler.py --remote

Make sure a clearml-agent is assigned to that queue.

WebApp

Configuration

The values configured through the wizard are stored in the task’s hyperparameters and configuration objects by using the Task.connect and Task.set_configuration_object methods respectively. They can be viewed in the WebApp, in the task’s CONFIGURATION page under HYPER PARAMETERS and CONFIGURATION OBJECTS > General.

ClearML automatically logs command line arguments defined with argparse. View them in the experiments CONFIGURATION page under HYPER PARAMETERS > General.

Autoscaler configuration

The task can be reused to launch another autoscaler instance: clone the task, then edit its parameters for the instance types and budget configuration, and enqueue the task for execution (you’ll typically want to use a ClearML Agent running in services mode for such service tasks).

Console

All other console output appears in the experiment’s CONSOLE.

Autoscaler console