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  • Reference
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    • Industrial Mixer
      • Get Started
      • Deep Reinforcement Learning
      • Strategy Pattern
      • Strategy Pattern with a Perception Layer
      • Plan-Execute Pattern
  • Establish a Simulation Environment
    • Simulation Overview
    • Connect a Simulator to Composabl
    • Composabl Simulation API
  • Build Multi-Agent Systems
    • Anatomy of a Multi-Agent System
    • Create a Use Case
    • Set Goals, Constraints, and Success Criteria
    • Create Skill Agents
      • Create Skill Agents
      • Create Skill Agents with Rewards Using the SDK
      • Configure Programmed Algorithms as Skill Agents
      • Configure API Connections to Third-Party Software as Skill Agents
    • Orchestrate Skill Agents
    • Configure Scenarios
    • Add a Perception Layer
      • Create a New Perceptor
      • Configure an ML Model as a Perceptor
      • Configure an LLM Model as a Perceptor
    • Publish Skill Agent Components to the UI
  • Train Agents
    • Submit a Training Job through the UI
    • Analyze Agent System Behavior
      • View Training Session Information
      • Analyze Data in Detail with the Historian
  • Evaluate Performance
    • Set KPI and ROI
    • Analyze Data
  • Deploy Agents
    • Access a Trained Agent System
    • Deploy an Agent System in a Container
    • Deploy an Agent System as an API
    • Connect Runtime Container to Your Operation
    • Connecting to Agent System Runtime and Plotting Results of Agent System Operations
  • clusters
    • Creating a Cluster
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    • Connecting a Cluster
  • Troubleshooting
    • Resolving Certificate Issues for Installing the Composabl SDK on WSL
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On this page
  • About Cluster Training
  • Ensure that Your Agent System is Ready
  • Choose the Right Cluster
  • Configure Your Training Session
  • Set the Number of Training Cycles
  • Set the Number of Simulators
  • Start Training
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  1. Train Agents

Submit a Training Job through the UI

PreviousPublish Skill Agent Components to the UINextAnalyze Agent System Behavior

Last updated 24 days ago

About Cluster Training

Composabl agent systems use clusters to train at scale. A cluster is a collection of computers that work on large tasks simultaneously. This provides enough compute to complete large training tasks as efficiently as possible.

Composabl offers two options for cluster training:

  • Use Composabl's Training as a Service offering to train on our clusters

  • Use your own compute clusters through Azure, AWS, or another provider

Ensure that Your Agent System is Ready

Before you submit your job for training on a cluster, make sure that your agent is fully configured and all the parameters have been set. That means checking all the agent components:

  • Goals

  • Perceptors

  • Orchestrators, including goals for learned selectors and scenarios

  • Skill agents, including goals for learned skills

  • Scenarios, including scenario flows

Any component of the agent with a warning sign is not fully configured and not ready for training. Go back to edit that agent component and make sure that all of the fields are filled out.

Choose the Right Cluster

You can train on your own cluster or on Composabl’s clusters using training as a service (TaaS) credits. If you want to use Composabl’s clusters, ensure that you have credits available.

To train on your own cluster, make sure that you have set your cluster up and installed Composabl successfully.

Click Train and then choose the cluster option in the menu. You will then have the option to configure your training session.

Configure Your Training Session

Training session configuration options are the same whether you’re using TaaS or training on your own cluster.

Set the Number of Training Cycles

A training cycle is a complete pass through the entire task, with the agent system continuing until it reaches success or some other stop criteria. Your agent system will train each skill one at a time for the selected number of training cycles, starting from the bottom of the agent system design.

A training cycle involves about 1,000 agent decisions. Depending on the complexity of the task, agent systems may need to complete anywhere between 100 and several thousand training cycles to become proficient.

Set the Number of Simulators

You can run multiple simulators in parallel to speed up training. If you run more than one simulator during a training, the number of training cycles selected will be multiplied by the number of simulators, so 5 training cycles with 3 simulators selected would lead to 15 training cycles total.

You can use the Advanced Configuration to choose how powerful each machine running a simulator should be. If you choose Small, each training cycle selected will result in one training cycle completed. If you choose GPU, you will get 4 training cycles for each training cycle.

More training cycles running simultaneously will speed up training, but also increase costs. How long your training takes also depends on the complexity of your agent system and your simulator.

Start Training

When you have configured your settings correctly, click Start Training.

You will then be taken to the Training Sessions page. There you can follow the agent system training progress by viewing the real-time plots or the console output.

Note that it will take a few minutes for the visualization to begin.

Kubernetes