Turn Any Laptop into a GPU Machine

Getting started with GPU computing has never been easier. In this guide, we'll walk you through launching your first training session on GaasHub in just 5 minutes.

Step 1: Download & Install

Download the GaasHub app for your platform and run the installer. The setup takes less than a minute.

Step 2: Create an Account

Sign up with your email or GitHub account. No credit card required to start exploring.

Step 3: Select Your GPU

Browse available GPUs and select one that matches your needs. We'll show you real-time pricing and availability.

Step 4: Launch Your Session

Click "Launch" and you'll be connected to your GPU instance in seconds. Your Jupyter notebook or SSH terminal is ready to go!

That's it! You're now ready to train models, run simulations, or do any GPU-intensive work you need.

PyTorch is one of the most popular deep learning frameworks. Let's train a simple image classifier using GaasHub's GPU power.

Setting Up Your Environment

GaasHub comes with PyTorch pre-installed, but you can also install specific versions. We'll use the latest stable release.

Loading Your Dataset

We'll use the CIFAR-10 dataset for this example. You can easily upload your own datasets through the GaasHub interface or download them directly on the GPU instance.

Building the Model

We'll create a simple CNN architecture. The beauty of using a cloud GPU is that you can experiment with larger models without worrying about local hardware limitations.

Training & Monitoring

Start training and watch your model learn in real-time. GaasHub's dashboard shows GPU utilization, memory usage, and training metrics.

By the end of this tutorial, you'll have a trained model and understand how to leverage cloud GPUs for deep learning projects.

Cloud GPU computing offers flexibility, but costs can add up quickly if not managed properly. Here are our top tips for optimizing your GaasHub usage and keeping costs under control.

1. Choose the Right GPU Tier

Don't always go for the most powerful GPU. Match your GPU choice to your workload. Development and testing can often be done on lower-tier GPUs.

2. Use Spot Instances

For non-critical workloads, our spot instances offer up to 70% savings. Perfect for batch processing and training jobs that can handle interruptions.

3. Implement Checkpointing

Save your model checkpoints frequently. This allows you to resume training from where you left off, making it easy to stop and start sessions as needed.

4. Monitor Usage

Use our dashboard to track your GPU usage patterns. Understanding when and how you use resources helps identify optimization opportunities.

By following these tips, our users typically reduce their GPU costs by 40-60% while maintaining the same productivity levels.