Automated Training Job Scheduling on EC2

Question

Pulumi · Accepted Answer

To automate training job scheduling on Amazon EC2, you'll need to create a setup that involves the following steps:

1. **Define an EC2 instance** to run your training jobs. This includes choosing the right instance type, setting up the desired AMI (Amazon Machine Image), and configuring security groups and IAM roles if necessary.
   
2. **Create an auto-scaling group**, optional, if you want to scale your training jobs based on demand or schedule.
   
3. **Schedule actions** on the auto-scaling group to automate the scaling process, which involves setting up start and stop times or defining recurrence patterns based on your job requirements.

4. **Create a CloudWatch event rule** if you need to trigger actions on a schedule beyond just auto-scaling, like triggering a Lambda function to start your training jobs.

In the following Pulumi program, we'll use `pulumi_aws` to define an EC2 instance and an auto-scaling schedule that scales instances according to our training schedule. Here's how you can do it in Python using Pulumi:

```python
import pulumi
import pulumi_aws as aws

# Choose an appropriate EC2 instance type for your training job.
instance_type = "t2.micro"

# Specify the AMI (Amazon Machine Image), ideally one that has your training software pre-installed.
# For example, a Deep Learning AMI or a custom AMI with your ML environment setup.
ami_id = "ami-12345"  # Replace with a valid AMI ID

# Set up a new security group for the EC2 instances if needed.
security_group = aws.ec2.SecurityGroup("training-sg",
    description="Allow SSH inbound traffic",
    ingress=[
        {"protocol": "tcp", "from_port": 22, "to_port": 22, "cidr_blocks": ["0.0.0.0/0"]},
    ],
)

# Create an IAM role that your EC2 instances will assume for the training jobs.
role = aws.iam.Role("training-role",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [
            {
                "Action": "sts:AssumeRole",
                "Effect": "Allow",
                "Principal": {"Service": "ec2.amazonaws.com"}
            }
        ]
    }"""
)

# Create an instance profile to associate with the EC2 instances.
instance_profile = aws.iam.InstanceProfile("training-profile", role=role.name)

# Launch configuration defines the template that the Auto Scaling group uses to launch EC2 instances.
# Note: User data scripts can be added to configure the instances after launch.
launch_configuration = aws.ec2.LaunchConfiguration("training-launch-config",
    image_id=ami_id,
    instance_type=instance_type,
    security_groups=[security_group.id],
    iam_instance_profile=instance_profile.name,
    user_data="""#!/bin/bash
        echo 'Instance started for training job'
        # Your training job startup scripts go here.
    """,  # This script runs on instance start.
)

# Create an Auto Scaling group, which manages the cluster of EC2 instances.
auto_scaling_group = aws.autoscaling.Group("training-asg",
    desired_capacity=1,  # Start with one instance.
    max_size=3,  # Define the maximum number of instances during scaling.
    min_size=1,  # Define the minimum number of instances.
    launch_configuration=launch_configuration.name,
    vpc_zone_identifiers=["subnet-12345"],  # Replace with your VPC subnet IDs.
)

# Create an Auto Scaling schedule to manage scaling actions automatically.
# In this example, we set up a recurring schedule that scales up the auto-scaling group right before the training times.
auto_scaling_schedule = aws.autoscaling.Schedule("training-scale-up-schedule",
    scheduled_action_name="scale-up",
    autoscaling_group_name=auto_scaling_group.name,
    desired_capacity=3,  # Scale up to 3 instances during training time.
    min_size=1,
    max_size=3,
    recurrence="0 2 * * *",  # Recurrence pattern is in cron format, this example represents every day at 2 AM.
    start_time="2023-01-01T02:00:00Z",  # Start date and time in ISO 8601 format.
)

# Output some information about the created resources.
pulumi.export("autoscaling_group_name", auto_scaling_group.name)
pulumi.export("launch_configuration_name", launch_configuration.name)
pulumi.export("scale_up_schedule", auto_scaling_schedule.recurrence)
```

In this program, pulumi_aws resources were used to create a launch configuration, an auto-scaling group, and a scheduled scaling action. Adjust the parameters like instance type, AMI, scaling patterns, and the user data script to fit the requirements of your specific training jobs.

Once the Pulumi program is deployed, it will create an EC2 instance ready to run your training jobs and scale them according to the defined schedule. The output will display the auto-scaling group name and the launch configuration name, which you can use to identify the resources in your AWS console.

Remember to replace placeholder values (e.g., AMI ID and subnet identifiers) with actual values from your AWS environment.

This setup ensures that your EC2 instances will automatically adjust to the desired capacity at the scheduled times, allowing your training jobs to run efficiently.