aitbc/docs/user/guides/job-submission.md

Job Submission Workflow

This guide explains how to submit AI compute jobs to the AITBC network and track their progress.

Overview

The job submission workflow:

1. Prepare - Choose model and parameters
2. Submit - Send job to Coordinator API
3. Queue - Job enters the processing queue
4. Execute - Miner processes your job
5. Complete - Receive results and receipt

Submission Methods

Web Interface

1. Go to Marketplace
2. Select a service (e.g., "Text Generation", "Image Generation")
3. Enter your prompt and configure options
4. Click Submit Job
5. View job status in your dashboard

CLI

# Basic submission
./aitbc-cli.sh submit "Explain machine learning in simple terms"

# With model selection
./aitbc-cli.sh submit "Generate a haiku about coding" --model llama3.2

# With parameters
./aitbc-cli.sh submit "Write a story" --model llama3.2 --max-tokens 500 --temperature 0.7

# Check job status
./aitbc-cli.sh status <job_id>

# List your jobs
./aitbc-cli.sh jobs

Python SDK

from aitbc_sdk import AITBCClient

client = AITBCClient(
    api_url="https://aitbc.bubuit.net/api",
    api_key="your-api-key"  # Optional for authenticated requests
)

# Submit a text generation job
job = client.submit_job(
    prompt="What is the capital of France?",
    model="llama3.2",
    params={
        "max_tokens": 100,
        "temperature": 0.5
    }
)

print(f"Job ID: {job.id}")
print(f"Status: {job.status}")

# Wait for completion
result = client.wait_for_job(job.id, timeout=60)
print(f"Output: {result.output}")

Direct API

# Submit job
curl -X POST https://aitbc.bubuit.net/api/v1/jobs \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Hello, world!",
    "model": "llama3.2",
    "params": {"max_tokens": 50}
  }'

# Check status
curl https://aitbc.bubuit.net/api/v1/jobs/<job_id>

Job Parameters

Parameter	Type	Default	Description
prompt	string	required	Input text or instruction
model	string	llama3.2	AI model to use
max_tokens	int	256	Maximum output tokens
temperature	float	0.7	Creativity (0.0-1.0)
top_p	float	0.9	Nucleus sampling
stream	bool	false	Stream output chunks

Available Models

Model	Type	Use Case
llama3.2	Text	General chat, Q&A, writing
llama3.2:1b	Text	Fast, lightweight tasks
codellama	Code	Code generation, debugging
stable-diffusion	Image	Image generation

Job States

State	Description
pending	Job submitted, waiting for miner
running	Miner is processing the job
completed	Job finished successfully
failed	Job failed (see error message)
cancelled	Job was cancelled by user

Tracking Your Jobs

View in Explorer

Visit Explorer to see:

• Recent jobs and their status
• Your job history (if authenticated)
• Receipt details and proofs

Programmatic Tracking

# Poll for status
import time

while True:
    job = client.get_job(job_id)
    print(f"Status: {job.status}")
    
    if job.status in ["completed", "failed", "cancelled"]:
        break
    
    time.sleep(2)

Cancelling Jobs

# CLI
./aitbc-cli.sh cancel <job_id>

# API
curl -X POST https://aitbc.bubuit.net/api/v1/jobs/<job_id>/cancel

Best Practices

1. Be specific - Clear prompts get better results
2. Set appropriate limits - Use max_tokens to control costs
3. Handle errors - Always check job status before using output
4. Use streaming - For long outputs, enable streaming for faster feedback

Next Steps

• Payments and Receipts - Understanding costs and proofs
• Troubleshooting - Common issues and solutions