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Introduction

Quick Start

Introduction

07/30/202634531 views

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👉 The goal of WebCal is to provide users with stable, reliable, and reasonably priced GPU computing power, ensuring that GPUs are no longer a roadblock on your path to becoming a data scientist.

👉Must-Read Documentation:

  1. Quick Start
  2. Instance Data Retention Guidelines
  3. Start the daemon

👉Frequently Used Documentation:

  1. How to Choose a GPU
  2. Upload Data
  3. Download Data
  4. Set Up Your Environment
  5. Public Cloud Storage (Highly Recommended)
  6. VSCode
  7. PyCharm
Next articleCUDA/cuDNN
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Quick Start
Introduction
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JetBrain Projector
Tmp
WebCal Scholars Program @2026
Introduction to the Public Beta/Suqian Zone A
About Us
Copying Data Between Instances
Analysis of Server Performance Metrics
Tidal Computing Power
Load Balancing
Domestic Chips
Using Huawei MindIE
Huawei Ascend NPU
MooreThread GPU
Environment Setup
CUDA/cuDNN
Miniconda
Python3.X
Installing Dependencies
Overview
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Flexible Deployment
Elastic Deployment Release Notes
Best Practices for Elastic Deployment
Performance Metrics Monitoring
Container Instances
JupyterLab
Remote SSH Connection
Save the image
Scaling Configuration
Multi-machine, multi-GPU parallel processing
Daemon
Overview
Change the billing method
Migration Example (Same Region)
Migration Examples
Remote Desktop
Reset the system
Choosing a GPU
GPU Selection
Performance Testing
Data
Upload Data
Download Data
Public Data
Public Cloud Storage (Highly Recommended)
Compression / Decompression
File Storage
Local data disk
Overview
Best Practices
FileZilla
Git
Gromacs
HuggingFace
KataGo
Linux Basics
MPI
OpenCL
PyCharm Remote Development
R (RStudio) Installation
SSH Tunnel
TensorBoard
Remote Development with VSCode
Visdom
Vulkan
XShell
Open Ports
WeChat Messages
Performance
Expose multiple services
Money-Saving Tips
Computation Precision Issues
Software Sources