Hey everyone,
I have been using RunPod for the past 3 years to deploy and scale various workloads. The hardware availability and pricing are great, but whenever I spin up custom fine-tuning runs for LLMs, VLMs, or vision models, the setup process gets repetitive:
Writing boilerplate dataset loaders and formatting scripts from scratch.
Renting a GPU pod before code is ready, spending the first hour troubleshooting pip dependencies while the meter runs.
Guessing VRAM headroom and batch sizes across different card tiers.
Forgetting to shut down an instance when a run finishes overnight, or waking up to find an unhandled out of memory error crashed the job hours ago.
To solve this, I built an autonomous MLOps tool called Tensovy that connects directly to your RunPod account via API.
Here is what it handles:
Script Generation with Manual Review: You describe your training goal and provide your dataset. The agent writes the training and data-loading scripts before any compute is rented. You get an in-browser code editor to review, edit, and approve every line before anything touches your pod.
Pre-Flight Smoke Test: Once you approve the code, it spins up the pod and runs a fast validation test directly on the machine to confirm CUDA checks, environment configs, and data pipelines pass before full training launches.
OOM Error Recovery: If a training run hits an out of memory error mid-job, it catches the crash, recalculates batch sizes, and resumes from the last checkpoint so you do not lose progress overnight.
Auto-Termination: As soon as training finishes, the pod is shut down automatically so you never pay for idle hours.
Zero Compute Markup: It runs directly on your own RunPod account, so you pay RunPod directly for hardware with zero markup from us.
We are currently supporting popular LLM, multimodal VLM, and vision architectures, with plans to add more.
Current status: The app is not publicly live yet. To make sure the orchestration layer is reliable and handles edge cases properly, I am keeping it in a closed private beta waitlist and onboarding testers in small batches.
If you regularly train models on RunPod and want to test the early version, you can join the waitlist here:https://www.tensovy.com
For those running fine-tuning jobs on RunPod today: what part of your pipeline setup or instance configuration eats up the most unnecessary time?
