Guides & Reference

The ML Operating Ecosystem — run any model on bare GPU with zero DevOps, zero config, zero waiting.

Your auto-provisioned S3 bucket — browse folders, upload datasets, and edit scripts in the browser.

Step-by-step walkthrough of all 7 wizard steps — name, training, federation, model, advanced, validate, launch.

Track running jobs, read training logs, and download the final model checkpoint when training finishes.

Write model_def.py, wire up fl_train_model(), and adapt your existing PyTorch code for federated learning.

How to structure and shard your dataset into zips so workers receive the right data.

Mount your bucket locally with rclone so you can upload files from your machine.

What ResonTech is, why it exists, and how training and inference work in four steps each.

Three GPU cluster types, how the distributed network aggregates compute, and performance characteristics.

How ML researchers, production teams, and enterprises each use ResonTech — pain points, workflow, and benefits.

Origin story, the team, mission, values, and contact information.