Getting Started
What is NeevCloud GPU Deployment?
NeevCloud provides you a platform to deploy and manage GPU instances for your AI, ML, and high-performance computing projects. You can configure your resources, select pre-built templates, and start computing within minutes without worrying about complex infrastructure setup.
The Traditional GPU Deployment Problem
Before we dive into how NeevCloud works, let's understand the challenges you typically face when setting up GPU instances:
Environment Setup Complexity
When you provision a GPU instance from scratch, you need to:
Install and configure CUDA drivers (ensuring version compatibility with your GPU)
Set up cuDNN libraries for deep learning operations
Install framework-specific dependencies (TensorFlow, PyTorch, JAX)
Configure Python environments with correct package versions
Resolve dependency conflicts between libraries
Set up Jupyter Notebook or other development environments
Time-Consuming Process
This manual setup can take anywhere from 30 minutes to several hours, depending on:
Your familiarity with the frameworks
Compatibility issues between CUDA versions and libraries
Network speed for downloading large packages
Debugging configuration errors
Version Compatibility Headaches
Different frameworks require specific CUDA versions. For example:
PyTorch 2.0 might require CUDA 11.8 or 12.1
TensorFlow 2.13 might need CUDA 11.8
Older projects might depend on legacy CUDA versions
Getting these versions wrong means your code won't run, or worse, runs but produces incorrect results.
Repeated Setup Costs
Every time you spin up a new instance, you repeat this entire process. If you're experimenting with different configurations or running multiple projects, this quickly becomes a productivity bottleneck.
How NeevCloud Solves These Problems
NeevCloud eliminates these challenges through pre-configured templates. These templates are production-ready environments with:
Correct CUDA drivers pre-installed
Framework-specific optimizations already configured
All necessary dependencies resolved
Development environments (Jupyter, ComfyUI) ready to use
This means you go from zero to training in seconds, not hours.
How to Deploy Your First GPU Instance
Follow these steps to get your first GPU up and running:
Initiate deployment: When you signin for the firs time you see "Deploy your First GPU" popup. Click on it to start the deployment process.
Select your resources: You'll be guided through selecting your instance type, template, configuration, and framework
Launch: After configuration, your GPU instance will be ready in seconds
What happens behind the scenes
NeevCloud automatically configures your network and storage settings during your first deployment. This means you don't need to manually set up infrastructure components—you can focus entirely on your AI/ML workloads.
Why This Matters for Your Workflow
The automatic setup eliminates common deployment bottlenecks. You won't spend time:
Configuring networking rules
Setting up storage volumes
Managing security groups
Installing base software packages
NeevCloud handles these foundational elements, allowing you to move directly from configuration to computation. This is especially valuable when you're iterating quickly on experiments or need to scale up your workloads on demand.
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