HyperAI Documentation

Welcome to HyperAI Documentation Center. HyperAI provides a simpler way to execute your machine learning code, so you are no longer hindered by driver installation, complex environment configuration, library management, and insufficient computing resources when creating and using your models.

Quick Start

Quick Start helps you quickly register for HyperAI and understand the most basic process of creating computing containers

Jupyter Workspace Tutorial

Quickly get started with JupyterLab (Jupyter Workspace) through the Deep Learning with PyTorch project and familiarize yourself with basic operations

MNIST Tutorial

You can then continue with the MNIST tutorial to further understand the image classification example

Core Concepts

You can also check out Core Concepts to learn about HyperAI's key concepts and features such as containers, data warehouses, inputs, outputs, and images

FAQ

Here you can find frequently encountered user questions, which we have compiled and answered

Resources and Usage

Learn how HyperAI specifically handles billing, and how to view and purchase corresponding resources

Computing Containers

Detailed introduction to the most basic computing units in HyperAI

Data Warehouse

Detailed introduction to HyperAI's data warehouse, and the differences between datasets and model storage

Open Resources

Introduction to HyperAI's public resources

Hypertuning

Introduces how to create and manage hyperparameter tuning through the CLI command-line tool

CLI Command-Line Tool

Learn how to interact with HyperAI services through the bayes command-line tool

If you encounter any problems during use, or have any good suggestions or improvements, please feel free to contact us.