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User guide

This guide provides a step-by-step walkthrough to help you launch and manage your GPU instance efficiently, allowing you to customize your environment and minimize running costs.

Steps to Use​

1. Select a GPU​

Begin by selecting a GPU from the available list that best suits your project requirements. Consider the following when making your choice:

Select GPU

  • GPU Model: Different models such as NVIDIA RTX-series, Tesla, or others offer varying performance levels.
  • Memory Capacity: Ensure the GPU has enough VRAM for your workload.
  • Compute Power: Look at CUDA cores, tensor cores, or other specs important for your tasks.
  • Pricing: Compare hourly costs to fit your budget.

Choosing an appropriate GPU is critical for ensuring that your project runs efficiently.

2. Choose or Modify a Template​

Next, you will be directed to a page where you can choose a pre-configured template or customize one to match your needs by clicking change Templates. Templates contain software stacks and environment settings specific to various workflows. Examples include:

  • TensorFlow: A setup optimized for machine learning and deep learning projects.
  • Ubuntu: A clean, general-purpose Linux environment that you can tailor as needed.
  • Langflow: A template designed for language processing workflows.

You can select the template as-is or modify configurations such as installed packages, environment variables, and startup scripts to better fit your tasks.

3. (Optional) Enable SSH Access​

If you want secure command-line access to your instance, you can enable SSH access:

SSH ACCESS

  • Provide your SSH public key to allow secure login.
  • This lets you connect remotely and manage files, run commands, or configure your instance.
  • If you do not want to enable SSH, you may continue with the platform’s default access options.

Adding SSH access enhances flexibility for advanced users needing remote command-line control.

4. Set Autostop Time​

To avoid incurring unnecessary charges when your instance is idle:

  • Specify an autostop duration such as 1 hour, 4 hours, or a custom timeframe.
  • After this time elapses, the instance will automatically shut down.
  • This feature helps control costs by ensuring resources are not consumed unintentionally.

Setting an autostop timer is a good practice to optimize resource utilization and budget management.

5. Click Launch and Begin Your Work​

Once you’ve completed the above steps:

  • Click the Launch button.
  • The instance will initialize using your chosen GPU and template settings.
  • You can start working immediately on your project.
  • Monitor the instance’s status and manage it via the user dashboard.

Additional Recommendations​

  • Backup important data frequently.
  • Monitor resource usage to optimize performance.
  • Customize templates further as needed for specialized workflows.
  • Contact support if you need help during setup or operation.
    • Join our Discord Group to connect with the community and share your feedback directly or email us at digital@qubrid.com.

Following these steps will enable you to set up a tailored and cost-effective GPU-powered instance quickly and efficiently.