swarms/docs/swarms/swarms/autoscaler.md

Enterprise Grade Documentation

---

AutoScaler Class from swarms Package

The AutoScaler class, part of the swarms package, provides a dynamic mechanism to handle agents depending on the workload. This document outlines how to use it, complete with import statements and examples.

---

Importing the AutoScaler Class

Before you can use the AutoScaler class, you must import it from the swarms package:

from swarms import AutoScaler

---

Constructor: AutoScaler.__init__()

Description:
Initializes the AutoScaler with a predefined number of agents and sets up configurations for scaling.

Parameters:

• initial_agents (int): Initial number of agents. Default is 10.
• scale_up_factor (int): Multiplicative factor to scale up the number of agents. Default is 2.
• idle_threshold (float): Threshold below which agents are considered idle. Expressed as a ratio (0-1). Default is 0.2.
• busy_threshold (float): Threshold above which agents are considered busy. Expressed as a ratio (0-1). Default is 0.7.

Returns:

• None

Example Usage:

from swarms import AutoScaler

scaler = AutoScaler(initial_agents=5, scale_up_factor=3, idle_threshold=0.1, busy_threshold=0.8)

---

Method: AutoScaler.add_task(task)

Description:
Enqueues the specified task into the task queue.

Parameters:

• task: The task to be added to the queue.

Returns:

• None

Example Usage:

task_data = "Process dataset X"
scaler.add_task(task_data)

---

Method: AutoScaler.scale_up()

Description:
Scales up the number of agents based on the specified scale-up factor.

Parameters:

• None

Returns:

• None

Example Usage:

# Called internally but can be manually invoked if necessary
scaler.scale_up()

---

Method: AutoScaler.scale_down()

Description:
Scales down the number of agents, ensuring a minimum is always present.

Parameters:

• None

Returns:

• None

Example Usage:

# Called internally but can be manually invoked if necessary
scaler.scale_down()

---

Method: AutoScaler.monitor_and_scale()

Description:
Continuously monitors the task queue and agent utilization to decide on scaling.

Parameters:

• None

Returns:

• None

Example Usage:

# This method is internally used as a thread and does not require manual invocation in most scenarios.

---

Method: AutoScaler.start()

Description:
Initiates the monitoring process and starts processing tasks from the queue.

Parameters:

• None

Returns:

• None

Example Usage:

scaler.start()

---

Full Usage

from swarms import AutoScaler

# Initialize the scaler
auto_scaler = AutoScaler(initial_agents=15, scale_up_factor=2, idle_threshold=0.2, busy_threshold=0.7)

# Start the monitoring and task processing
auto_scaler.start()

# Simulate the addition of tasks
for i in range(100):
    auto_scaler.add_task(f"Task {i}")

Pass in Custom Agent

You can pass any agent class that adheres to the required interface (like having a run() method). If no class is passed, it defaults to using AutoBot. This makes the AutoScaler more flexible and able to handle a wider range of agent implementations.

from swarms import AutoScaler

auto_scaler = AutoScaler(agent=YourCustomAgent)
auto_scaler.start()

for i in range(100):  # Adding tasks
    auto_scaler.add_task(f"Task {i}")

---

Notes:

1. Adjust the thresholds and scaling factors as per your specific requirements and nature of the tasks.
2. The provided implementation is a baseline. Depending on your production environment, you may need additional features, error-handling, and optimizations.
3. Ensure that the swarms package and its dependencies are installed in your environment.

---