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🧠 GPU Ops Agent — Autonomous GPU Workflow Orchestrator

💡 Inspiration

Most “AI agents” today can talk — but can’t act. I wanted to build something that actually does work: a system that can plan, launch, monitor, and tear down GPU workloads autonomously.

As a solo builder, I’ve worked on plenty of AI infrastructure setups — Docker configs, GPU jobs, dependency hell, idle-cost leaks — and I realized most of that can be automated. So this project was born: an agent that uses reasoning + tool use to manage GPU workflows like a human DevOps engineer.

The HackUTD NVIDIA challenge — “AI that takes action, not just responds” — was the perfect spark.

⚙️ What It Does

GPU Ops Agent is a Nemotron-powered orchestrator that can:

Parse a natural language objective (e.g. “Train MNIST for one epoch, teardown after 5 minutes idle”)
Plan multi-step GPU workflows:
1. Provision container / instance
2. Clone and install code
3. Execute and monitor the job
4. Auto-teardown based on idle or time caps
Apply guardrails for cost, safety, and reproducibility
Generate structured reports and artifacts at the end of each run

Everything is modular — each “tool” (like create_instance, run_command, or monitor) can be extended for other cloud backends or APIs.

🏗️ How I Built It

This was a solo project built end-to-end in ~24 hours. I wanted to make it easy for anyone to train models and use GPUs by focusing on the task and not specifcs.

Stack & Tools

NVIDIA Nemotron (Meta Llama 3.1 - 70B Instruct) for planning & self-critique reasoning
Python 3.10 + openai compatible client
Rich for UI + terminal animations
Docker (simulated locally for demo) for container lifecycle orchestration
Jinja2 for runtime report generation
Pydantic / JSONSchema for plan validation
Colorama + Click for interactive CLI demo

Architecture The system is split into modular layers:

agent/ → planning, validation, and guardrails
exec/ → orchestrator + monitor + reporting
tools/ → adapters for Docker, WandB, or APIs
demo.py → the showcase script for judges, simulating a full run with logs and GPU telemetry

Mathematically, the planner uses a structured loop:

$$ \text{Objective} \rightarrow \text{Plan} \rightarrow \text{Critic}(Plan) \rightarrow \text{Validated Execution} $$

🚧 Challenges I Faced

1. Running GPU orchestration solo:
Without access to multi-GPU or containerized environments locally, I had to simulate orchestration and GPU telemetry while keeping the logic realistic enough to show to judges.

2. Making it visually engaging:
Most backend demos are invisible. I wanted to make the command-line tell a story — a theatrical “thinking + acting” flow that judges could feel.

3. Designing safe autonomy:
I had to think deeply about how an AI agent might go wrong — infinite loops, rm -rf risks, runaway costs. I built JSON guardrails and regex filters to block unsafe commands and limit runtime.

4. Working entirely solo:
Every subsystem — planning, validation, orchestration, visualization — had to be designed, coded, tested, and refined alone. The hardest part wasn’t code — it was context switching between roles (AI engineer, DevOps, designer, storyteller).

📚 What I Learned

How to structure agentic AI systems around reasoning + action, not just chat.
How to simulate “LLM planning and execution” safely with structured schemas.
Practical insight into GPU lifecycle automation — from provisioning to idle teardown.
That storytelling matters: demos that explain how AI thinks are much more memorable than black-box outputs.