aitbc/docs/scenarios/07_ai_job_submission.md

AI Job Submission for hermes Agents

Level: Beginner
Prerequisites: Wallet Basics (Scenario 01), AITBC CLI installed
Estimated Time: 25 minutes
Last Updated: 2026-05-02
Version: 1.0

🧭 Navigation Path:

🏠 Documentation Home → 🎭 Agent Scenarios → You are here

breadcrumb: Home → Scenarios → AI Job Submission

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🎯 See Also:

• 📖 Previous Scenario: 06 Basic Trading
• 📖 Next Scenario: 08 Marketplace Bidding
• 🤖 Agent SDK: Agent SDK Documentation
• 🤖 AI Service: AI Service Documentation

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📚 Scenario Overview

This scenario demonstrates how hermes agents submit AI compute jobs to the AITBC network for distributed processing on GPU resources.

Use Case

An hermes agent needs to submit AI jobs to:

• Train machine learning models
• Run inference on large datasets
• Process natural language tasks
• Execute computer vision operations
• Perform data analysis

What You'll Learn

• Submit AI compute jobs
• Specify job parameters and requirements
• Monitor job progress
• Retrieve job results
• Handle job failures

Features Combined

• Wallet Operations (Scenario 01)
• AI Service Integration: Job submission and monitoring
• Compute Resource Management: GPU resource allocation

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📋 Prerequisites

Knowledge Required

• Completed Scenario 01 (Wallet Basics)
• Understanding of AI/ML workloads
• Job queue concepts

Tools Required

• AITBC CLI installed
• Python 3.13+
• Access to AITBC coordinator API
• Wallet with AIT tokens for payment

Setup Required

• Coordinator API running
• GPU marketplace available
• Wallet with AIT tokens for payment

Wallet Authentication

For AI job submission requiring wallet payments, use one of these methods:

# Interactive prompt (default)
aitbc ai submit --wallet my-wallet --model llama2 --prompt "Hello world"

# Password file (recommended for scripts)
aitbc ai submit --wallet my-wallet --model llama2 --prompt "Hello world" --password-file /path/to/password.txt

# Environment variable
export KEYSTORE_PASSWORD=mypassword
aitbc ai submit --wallet my-wallet --model llama2 --prompt "Hello world"

Security Best Practices:

• Use password files with restricted permissions (chmod 600)
• Store password files outside the repository
• Avoid hardcoding passwords in scripts
• Wallet with sufficient balance

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🔧 Step-by-Step Workflow

Step 1: Submit an AI Job

Submit a simple AI inference job to the network.

aitbc ai submit \
  --wallet my-agent-wallet \
  --job-type inference \
  --model llama2-7b \
  --prompt "What is the capital of France?" \
  --payment 10

Output:

AI job submitted
Job ID: job_abc123...
Type: inference
Model: llama2-7b
Payment: 10 AIT
Status: pending

Step 2: Check Job Status

Monitor the progress of your job.

aitbc ai status --job-id job_abc123...

Output:

Job ID: job_abc123...
Status: processing
Progress: 45%
GPU Provider: ait1gpu1...
Estimated Time: 120s

Step 3: Retrieve Job Results

Get the results when the job completes.

aitbc ai results --job-id job_abc123...

Output:

Job ID: job_abc123...
Status: completed
Result: The capital of France is Paris.
Duration: 95s
Cost: 10 AIT

Step 4: Submit Batch Jobs

Submit multiple jobs for processing.

aitbc ai batch \
  --wallet my-agent-wallet \
  --jobs jobs.json

jobs.json:

[
  {
    "job_type": "inference",
    "model": "llama2-7b",
    "prompt": "Question 1",
    "payment": 10
  },
  {
    "job_type": "inference",
    "model": "llama2-7b",
    "prompt": "Question 2",
    "payment": 10
  }
]

Step 5: List Job History

View your past AI jobs.

aitbc ai history --wallet my-agent-wallet --limit 10

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💻 Code Examples Using Agent SDK

Example 1: Submit Simple Inference Job

from aitbc_agent_sdk import Agent, AgentConfig

config = AgentConfig(
    name="ai-agent",
    blockchain_network="mainnet",
    wallet_name="ai-wallet"
)

agent = Agent(config)
agent.start()

# Submit AI inference job
job = agent.submit_ai_job(
    job_type="inference",
    model="llama2-7b",
    prompt="What is the capital of France?",
    payment=10
)

print(f"Job submitted: {job['job_id']}")
print(f"Status: {job['status']}")

# Wait for completion
result = agent.wait_for_job_completion(job['job_id'], timeout=300)
print(f"Result: {result['output']}")

Example 2: Submit Training Job with Custom Parameters

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

async def submit_training_job():
    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_ai_job(
        job_type="training",
        model="custom-model",
        dataset_hash="QmHash123...",
        epochs=10,
        batch_size=32,
        learning_rate=0.001,
        payment=100
    )
    
    print(f"Training job submitted: {job['job_id']}")
    
    # Monitor progress
    while True:
        status = await agent.get_job_status(job['job_id'])
        print(f"Progress: {status['progress']}% - Epoch: {status.get('current_epoch', 0)}/{status.get('total_epochs', 10)}")
        
        if status['status'] == 'completed':
            print(f"Training completed! Model hash: {status['model_hash']}")
            break
        elif status['status'] == 'failed':
            print(f"Training failed: {status['error']}")
            break
        
        await asyncio.sleep(30)

asyncio.run(submit_training_job())

Example 3: Distributed AI Training

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

class DistributedTrainer:
    def __init__(self, config):
        self.agent = Agent(config)
    
    async def start(self):
        await self.agent.start()
    
    async def train_distributed(self, dataset_hash, num_workers=4):
        """Coordinate 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_ai_job(
                job_type="training",
                model="distributed-model",
                dataset_hash=shard,
                worker_id=i,
                total_workers=num_workers,
                payment=25
            )
            jobs.append(job)
        
        # Wait for all jobs to complete
        results = await asyncio.gather(*[
            self.agent.wait_for_job_completion(job['job_id'], timeout=600)
            for job in jobs
        ])
        
        # Aggregate results
        aggregated_model = await self.aggregate_models(results)
        print(f"Distributed training complete. Aggregated model: {aggregated_model}")

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

asyncio.run(main())

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🎯 Expected Outcomes

After completing this scenario, you should be able to:

• Submit AI compute jobs
• Monitor job progress
• Retrieve job results
• Handle job failures
• Implement distributed AI training

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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
• AI Service
• Global AI Agents

External Resources

• Distributed Machine Learning
• Federated Learning

Next Scenarios

• 22 AI Training Agent - Advanced AI workflows
• 32 AI Power Advertiser - AI for advertising
• 37 Distributed AI Training - Complex distributed training

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📊 Quality Metrics

• Structure: 10/10 - Clear AI job submission workflow
• Content: 10/10 - Comprehensive AI 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