Getting started

This guide takes you from account creation to a deployed and verified GPU instance in about 10 minutes.

Already have an account? Jump to Quick Start for fast deployment.

Prerequisites

Before deploying, complete these setup steps:

• A Spheron AI account (created in Step 1 below)
• An SSH key pair (generated in Step 3 below)
• Credits added to your account (added in Step 2 below)

Step 1: Create your account

Sign up

1. Visit app.spheron.ai
2. Click Sign Up
3. Choose a sign-up method:
   • Google account
   • GitHub account (recommended for developers)
   • Email and password
4. Complete email verification if using email signup

Navigate the dashboard

Once logged in, the dashboard shows:

• Deploy: Create new GPU instances
• Instances: Manage active deployments
• Credits: Add funds and view balance
• Settings: Account and SSH key management

Step 2: Add billing

Add credits

1. Click Credits in the top-right corner
2. Choose a payment method:
   • Credit/Debit Card
   • Cryptocurrency
3. Add initial credits (minimum $15, maximum $5,000 per transaction)

Pricing: Pay-per-second billing with no hidden fees. See Billing for detailed pricing.

Step 3: Set up SSH access

SSH keys are required to access your GPU instances securely.

Generate SSH key

# Generate new SSH key
ssh-keygen -t ed25519 -C "your_email@example.com"
 
# Press Enter to save to default location
# Set a passphrase (optional but recommended)
 
# Display your public key
cat ~/.ssh/id_ed25519.pub

# Generate new SSH key
ssh-keygen -t ed25519 -C "your_email@example.com"
 
# Display your public key
type $env:USERPROFILE\.ssh\id_ed25519.pub

Upload SSH key to Spheron

1. Go to User Settings
2. Navigate to the SSH Keys section
3. Click Add SSH Key
4. Paste your public key (the one ending in .pub)
5. Give it a memorable name (e.g., "My Laptop")
6. Click Save

Step 4: Deploy your first instance

Navigate to deploy

1. Click Deploy in the left sidebar
2. Browse the GPU catalog

Select your GPU

For a first deployment, use:

• GPU: RTX 4090 (24 GB VRAM)
• Cost: ~$0.52/hour
• Good for: Learning, testing, small models

Click on the RTX 4090 to proceed.

Configure instance

1. Region: Choose the region closest to you for lower latency
2. Operating System: Select Ubuntu 22.04 LTS for the most stable GPU driver support
3. Storage: Keep the default (usually sufficient)
4. SSH Key: Select the key you uploaded earlier
5. Startup Script (optional): Leave blank for now. See Startup Scripts for advanced automation.

Review and deploy

1. Check the Order Summary on the right:
   • Hourly cost
   • Current balance
   • Instance configuration
2. Click Deploy Instance
3. Wait 30-60 seconds for provisioning

Step 5: Connect to your instance

Get connection details

Once deployed, the instance dashboard shows:

• IP address
• Username (usually root, ubuntu, or provider-specific)
• SSH connection command

Connect via SSH

ssh root@<your-instance-ip>

If using a custom SSH key location:

ssh -i ~/.ssh/id_ed25519 root@<your-instance-ip>

On first connection, type yes to accept the host fingerprint. Enter your SSH key passphrase if you set one.

Step 6: Verify your setup

Once connected, verify everything is working.

Check GPU

nvidia-smi

Expected output shows GPU model (RTX 4090), memory (24 GB), driver version, and GPU utilization.

Check CUDA

nvcc --version

The output shows the CUDA compiler version.

Check system

# Check CPU and memory
htop
 
# Check disk space
df -h
 
# Check OS version
cat /etc/os-release

Test GPU access

# Simple GPU test
python3 -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"

PyTorch does not need to be pre-installed. Install it when needed for your workload.

Troubleshooting

Issue: Cannot connect via SSH

Symptoms: Connection refused or timeout when running ssh

Diagnosis: Verify the SSH key is loaded:

ssh-add -l

Resolution: If the key is not listed, add it:

ssh-add ~/.ssh/id_ed25519

Additional checks:

• Verify you uploaded the correct public key
• Check that firewall rules allow SSH (port 22)
• Try with an explicit key path: ssh -i ~/.ssh/id_ed25519 <user>@<ip>

Issue: GPU not detected

Symptoms: nvidia-smi returns an error

Diagnosis: Drivers may still be loading after provisioning

Resolution: Wait 30 seconds and run nvidia-smi again. If the error persists, reboot the instance with sudo reboot.

Issue: Instance deployment failed

Symptoms: Deployment status shows "failed"

Resolution:

• Check that your account balance has sufficient credits
• Try a different region (some may be at capacity)
• Contact support if the issue persists

Terminate your instance

When done, terminate the instance to stop charges:

1. Go to the instance dashboard
2. Click Terminate or Delete
3. The instance stops and all data is permanently deleted

What's next

• Quick Start: Condensed deployment flow for users already set up
• SSH Connection Guide: Advanced SSH setup and troubleshooting
• Jupyter Notebook: Browser-based development via SSH tunnel
• VS Code Remote: Remote development directly on the instance
• Security Best Practices: Keep your account secure