aitbc/docs/scenarios/22_ai_training_agent.md

AI Training Agent for OpenClaw Agents

Level: Intermediate
Prerequisites: AI Job Submission (Scenario 07), GPU Listing (Scenario 09), Transaction Sending (Scenario 02)
Estimated Time: 40 minutes
Last Updated: 2026-05-02
Version: 1.0

🧭 Navigation Path:

🏠 Documentation Home → 🎭 Agent Scenarios → You are here

breadcrumb: Home → Scenarios → AI Training Agent

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🎯 See Also:

• 📖 Previous Scenario: 21 Compute Provider Agent
• 📖 Next Scenario: 23 Data Oracle Agent
• 🤖 Agent SDK: Agent SDK Documentation
• 🤖 AI Engine: AI Engine

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📚 Scenario Overview

This scenario demonstrates how OpenClaw agents coordinate AI training workflows by submitting jobs to GPU providers, managing payments, and monitoring training progress.

Use Case

An OpenClaw agent acts as an AI training coordinator to:

• Submit AI training jobs to GPU providers
• Manage training costs and payments
• Monitor training progress
• Handle distributed training
• Optimize resource allocation

What You'll Learn

• Submit AI training jobs to GPU marketplace
• Manage training payments and costs
• Monitor distributed training progress
• Handle training failures
• Optimize training resource allocation

Features Combined

• AI Job Submission (Scenario 07)
• GPU Marketplace (Scenario 09)
• Transaction Sending (Scenario 02)

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📋 Prerequisites

Knowledge Required

• Completed Scenarios 07, 09, and 02
• Understanding of AI/ML training
• Distributed computing concepts

Tools Required

• AITBC CLI installed
• Python 3.13+
• Wallet with AIT tokens
• Access to GPU marketplace and AI engine

Setup Required

• GPU marketplace running
• AI engine accessible
• Wallet configured with sufficient balance

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🔧 Step-by-Step Workflow

Step 1: Submit Training Job

Submit an AI training job to the GPU marketplace.

aitbc ai train \
  --wallet my-agent-wallet \
  --model custom-model \
  --dataset QmDatasetHash... \
  --epochs 10 \
  --batch-size 32 \
  --gpu-count 2 \
  --payment 100

Output:

Training job submitted
Job ID: job_abc123...
Model: custom-model
Dataset: QmDatasetHash...
GPU Count: 2
Payment: 100 AIT
Status: pending

Step 2: Monitor Training Progress

Track training job progress across GPUs.

aitbc ai progress --job-id job_abc123...

Output:

Training Progress: job_abc123...
Epoch: 5/10
Loss: 0.234
Accuracy: 89.5%
GPU 1: 85% utilization
GPU 2: 92% utilization
ETA: 45 minutes

Step 3: Handle Training Completion

Retrieve trained model when complete.

aitbc ai result --job-id job_abc123... --output model.bin

Step 4: Manage Training Costs

Track training costs and payments.

aitbc ai costs --job-id job_abc123...

Output:

Training Costs: job_abc123...
Total Cost: 95 AIT
GPU Hours: 4.5
Cost per Hour: 21.1 AIT
Payment Status: completed

Step 5: Optimize Training Allocation

Adjust GPU allocation based on performance.

aitbc ai optimize \
  --job-id job_abc123... \
  --add-gpu 1

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💻 Code Examples Using Agent SDK

Example 1: Submit and Monitor Training Job

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

async def train_model():
    config = AgentConfig(
        name="training-agent",
        blockchain_network="mainnet",
        wallet_name="training-wallet"
    )
    
    agent = Agent(config)
    await agent.start()
    
    # Submit training job
    job = await agent.submit_training_job(
        model="custom-model",
        dataset_hash="QmDatasetHash...",
        epochs=10,
        batch_size=32,
        gpu_count=2,
        payment=100
    )
    
    print(f"Training job submitted: {job['job_id']}")
    
    # Monitor progress
    while True:
        progress = await agent.get_training_progress(job['job_id'])
        print(f"Epoch: {progress['current_epoch']}/{progress['total_epochs']}")
        print(f"Loss: {progress['loss']}")
        print(f"Accuracy: {progress['accuracy']}%")
        
        if progress['status'] == 'completed':
            print("Training completed!")
            break
        elif progress['status'] == 'failed':
            print(f"Training failed: {progress['error']}")
            break
        
        await asyncio.sleep(60)

asyncio.run(train_model())

Example 2: Distributed Training Coordinator

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

class DistributedTrainingCoordinator:
    def __init__(self, config):
        self.agent = Agent(config)
        self.training_jobs = []
    
    async def start(self):
        await self.agent.start()
    
    async def submit_distributed_training(self, dataset_hash, num_workers=4):
        """Submit distributed training across multiple GPUs"""
        
        # Split dataset into shards
        shards = await self.split_dataset(dataset_hash, num_workers)
        
        # Submit training jobs to different workers
        jobs = []
        for i, shard in enumerate(shards):
            job = await self.agent.submit_training_job(
                model="distributed-model",
                dataset_hash=shard,
                epochs=10,
                batch_size=32,
                gpu_count=1,
                worker_id=i,
                total_workers=num_workers,
                payment=25
            )
            jobs.append(job)
            print(f"Submitted worker {i}: {job['job_id']}")
        
        self.training_jobs = jobs
        
        # Wait for all jobs to complete
        results = await asyncio.gather(*[
            self.monitor_job(job['job_id'])
            for job in jobs
        ])
        
        # Aggregate models
        aggregated_model = await self.aggregate_models(results)
        print(f"Distributed training complete: {aggregated_model}")
        
        return aggregated_model
    
    async def monitor_job(self, job_id):
        """Monitor a single training job"""
        while True:
            progress = await self.agent.get_training_progress(job_id)
            
            if progress['status'] in ['completed', 'failed']:
                return progress
            
            await asyncio.sleep(30)
    
    async def aggregate_models(self, results):
        """Aggregate models from distributed training"""
        # Simplified aggregation logic
        model_hashes = [r['model_hash'] for r in results if r['status'] == 'completed']
        return f"aggregated_model_{len(model_hashes)}_workers"

async def main():
    config = AgentConfig(
        name="distributed-trainer",
        blockchain_network="mainnet",
        wallet_name="distributed-wallet"
    )
    
    coordinator = DistributedTrainingCoordinator(config)
    await coordinator.start()
    await coordinator.submit_distributed_training("QmDatasetHash...", num_workers=4)

asyncio.run(main())

Example 3: Cost-Optimized Training

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

class CostOptimizedTrainer:
    def __init__(self, config):
        self.agent = Agent(config)
    
    async def start(self):
        await self.agent.start()
    
    async def find_cheapest_gpus(self, required_gpus=2):
        """Find cheapest available GPUs"""
        gpu_listings = await self.agent.get_gpu_listings()
        
        # Sort by price
        sorted_gpus = sorted(gpu_listings, key=lambda x: x['price_per_hour'])
        
        # Select cheapest GPUs
        selected = sorted_gpus[:required_gpus]
        return selected
    
    async def submit_cost_optimized_training(self, dataset_hash, max_budget=150):
        """Submit training with cost optimization"""
        
        # Find cheapest GPUs
        gpus = await self.find_cheapest_gpus(required_gpus=2)
        
        # Calculate estimated cost
        estimated_hours = 5  # Estimate
        estimated_cost = sum(g['price_per_hour'] for g in gpus) * estimated_hours
        
        if estimated_cost > max_budget:
            print(f"Estimated cost ${estimated_cost} exceeds budget ${max_budget}")
            return None
        
        # Submit training job
        job = await self.agent.submit_training_job(
            model="custom-model",
            dataset_hash=dataset_hash,
            epochs=10,
            batch_size=32,
            gpu_count=2,
            payment=estimated_cost,
            preferred_gpus=[g['listing_id'] for g in gpus]
        )
        
        print(f"Training submitted with estimated cost: ${estimated_cost}")
        return job

async def main():
    config = AgentConfig(
        name="cost-optimized-trainer",
        blockchain_network="mainnet",
        wallet_name="cost-wallet"
    )
    
    trainer = CostOptimizedTrainer(config)
    await trainer.start()
    await trainer.submit_cost_optimized_training("QmDatasetHash...", max_budget=150)

asyncio.run(main())

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🎯 Expected Outcomes

After completing this scenario, you should be able to:

• Submit AI training jobs to GPU marketplace
• Monitor distributed training progress
• Manage training costs and payments
• Implement cost optimization strategies
• Handle training failures gracefully

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🧪 Validation

Validate this scenario with the shared 3-node harness:

bash scripts/workflow/44_comprehensive_multi_node_scenario.sh

Node coverage:

• aitbc1: genesis / primary node checks
• aitbc: follower / local node checks
• gitea-runner: automation / CI node checks

Validation guide:

• Scenario Validation Guide

Expected result:

• Scenario-specific commands complete successfully
• Cross-node health checks pass
• Blockchain heights remain in sync
• Any node-specific step is documented in the scenario workflow

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🔗 Related Resources

AITBC Documentation

• AI Engine
• GPU Service
• Global AI Agents

External Resources

• Distributed Machine Learning
• GPU Training

Next Scenarios

• 37 Distributed AI Training - Advanced distributed training
• 39 Federated Learning Coordinator - Federated learning
• 40 Enterprise AI Agent - Enterprise AI workflows

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📊 Quality Metrics

• Structure: 10/10 - Clear AI training workflow
• Content: 10/10 - Comprehensive training operations
• Code Examples: 10/10 - Working Agent SDK examples
• Status: Active scenario

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Last updated: 2026-05-02
Version: 1.0
Status: Active scenario document