About TensorTurn: No-Code Machine Learning on Your Own GPUs

TensorTurn is an AI-native, no-code machine learning platform built by DeepQuantica. It lets you go from a raw dataset to a trained model to a deployed prediction API by describing what you want in plain English — no ML engineering required. Its defining feature is decentralized compute: you can train on TensorTurn's cloud GPUs or connect and pool your own machines. TensorTurn is currently in beta.

TensorTurn turns a plain-English description into a full training notebook that runs on isolated cloud GPUs — Modal-hosted T4, L4, and A10G — across tabular and image tasks using scikit-learn, XGBoost, PyTorch, Keras, and YOLO/Ultralytics. Around that sit automated dataset health checks, an AI data editor, self-healing training runs, and one-switch API deployment. The goal is to compress the whole ML lifecycle into something a domain expert can drive without writing training code.

Our mission

DeepQuantica builds TensorTurn to make machine learning practical for people who understand their data but don't want to hand-write training loops, wrangle CUDA, or babysit failing runs. We think the two biggest barriers to shipping a model are the plumbing (environments, GPUs, deployment) and the debugging (runs that error at 2 a.m.). TensorTurn's job is to remove both, so the human can focus on the problem instead of the pipeline.

What makes TensorTurn different

• Chat to build a model — describe it in plain English and TensorTurn writes and runs the entire training notebook on isolated cloud GPUs, for tabular and image tasks.
• Self-healing runs — when a run errors, the AI patches only the broken cells and retries up to 100 times, learning from each verified fix.
• Decentralized compute — connect your own GPU with one outbound-HTTPS-only command (works behind any NAT or firewall) and pool several of your machines into one run via ensemble or fused mode.
• Automated dataset health checks — leakage, outliers, duplicates, blur, exposure, corrupt files, and likely-mislabeled images (via DINOv2 embeddings and a kNN vote), plus a quality score and a preprocessing playbook.
• AI data editor — a spreadsheet-style grid where you describe changes and the AI plans reviewable, whitelisted transforms; only a column profile and a few example values ever leave your browser.
• Deploy as an API — flip one switch to get a live, authenticated /predict endpoint that scales to zero on Modal.

Honest about what it is not

TensorTurn's decentralized compute is your own single-tenant pool of machines — not a marketplace of strangers' GPUs, and not web3 or blockchain. It is embarrassingly parallel (ensemble) or periodic weight-averaging over sharded data (fused). It is not tensor or pipeline parallelism, and it does not do gradient all-reduce across the internet. You cannot split a single layer across machines, so a model too big for one machine is not made to fit by adding more machines. We'd rather set expectations honestly than oversell.

The company: DeepQuantica

TensorTurn is developed and operated by DeepQuantica. The platform is in beta, which means we are actively shipping, and pricing is in Indian rupees — Free at ₹0/month, Pro at ₹899/month, and Annual at ₹6,999/year. If you understand your data, TensorTurn is built to get you from that data to a deployed model without a machine-learning team behind you.