Templates
Templates in VoltageGPU are configuration blueprints that define how GPU-enabled Docker containers are created and deployed. They provide a standardized way to specify all necessary parameters for container creation.
Overview
Each template contains essential Docker configuration parameters that determine how the GPU environment will be set up, including the base image, environment variables, storage requirements, and network settings. Templates streamline the process of launching GPU environments by providing a reusable configuration that can be easily deployed.
How It Works
Template Structure
A template in VoltageGPU consists of several required components:
Docker Image Configuration
- Docker image name
- Image tag version
- These determine the base environment for your GPU container
Environment Variables
- Custom environment variables needed for your applications
- System configuration parameters
- Runtime settings
Volume Configuration
- Storage volume specifications
- Mount points for persistent data
- Access permissions
Network Settings
- Port exposure configurations
- Which ports should be accessible
- Network interface settings
Startup Commands
- Container initialization commands
- Service startup parameters
- Runtime configurations
Template Types
Official Templates
Maintained by the VoltageGPU team. Verified and trusted to be secure and functional. Includes PyTorch, TensorFlow, JAX, and more.
Custom Templates
Created by users for specific use cases. Can be public (shared with community) or private (only visible to you).
Public Templates
Community-contributed templates available to all users. Great for discovering new configurations and workflows.
Private Templates
Your personal templates visible only to your account. Perfect for proprietary configurations and internal tools.
Template Verification
Why Verification Matters
Custom templates in VoltageGPU are user-defined configurations for GPU environments. While this flexibility is powerful, it's crucial to ensure these templates work correctly before deployment. VoltageGPU implements a robust template verification system that validates all custom templates before they can be used in production.
Verification Process
The verification process consists of four key steps:
1. Test Deployment
Creates a container on dedicated verification servers using exact template configurations.
2. Container Readiness Check
Monitors container startup process and tracks initialization of required services.
3. SSH Connection Validation
Verifies SSH accessibility, confirms proper credential handling, and tests connection stability.
4. Error Reporting
Provides detailed error logs if verification fails with troubleshooting guidance.
Verification Prerequisites
- Your container must remain running continuously (no immediate exits)
- Docker image should be publicly accessible
Test Your Container Locally
To verify if your container can pass verification:
# Run your container
docker run [-e <env-var>=<value>] -d <your-docker-image>:<image-tag> <your-startup-command>
# Check if container keeps running
docker psTip
If your container exits immediately, check your startup command and ensure any required services are configured to run in the foreground.
Popular Official Templates
| Template | Base Image | CUDA Version | Best For |
|---|---|---|---|
| PyTorch (CUDA) | daturaal/pytorch | 12.8.0 | Deep learning, Computer vision |
| TensorFlow (CUDA) | tensorflow/tensorflow | 12.4.0 | Production ML, Keras workflows |
| JAX (CUDA) | jax/jax | 12.6.0 | Research, Transformers, TPU-like |
| Ubuntu Base | nvidia/cuda | 12.8.0 | Custom setups, Flexibility |
| Jupyter Lab | jupyter/datascience | 12.4.0 | Interactive development, Notebooks |
Creating Custom Templates
Step-by-Step Guide
1. Navigate to Templates
Go to Dashboard → Templates → Create Template
2. Configure Basic Settings
- Template Name: A descriptive name for your template
- Description: What this template is for
- Visibility: Public or Private
3. Docker Configuration
Image: your-registry/your-image
Tag: latest
Registry: Docker Hub / Private Registry
Environment Variables:
CUDA_VISIBLE_DEVICES=all
PYTHONUNBUFFERED=1
Startup Command: /bin/bash -c "service ssh start && tail -f /dev/null"4. Port Configuration
- SSH Port: 22 (required)
- Jupyter: 8888 (optional)
- TensorBoard: 6006 (optional)
- Custom ports as needed
5. Submit for Verification
Click "Create & Verify" to submit your template. The verification process typically takes 2-5 minutes.
Frequently Asked Questions
Can I use private Docker registries?
Yes! Add your Docker credentials in Dashboard → Docker Credentials, then reference your private images in templates.
Why did my template verification fail?
Common reasons: container exits immediately, SSH not configured, image not accessible, or startup command errors. Check the verification logs for details.
Can I edit a template after creation?
Yes, you can edit your custom templates. Changes require re-verification before the updated template can be used.
How do I share my template with others?
Set your template visibility to "Public" when creating or editing. Public templates appear in the community templates list.
Ready to Create Your Template?
Build custom GPU environments tailored to your workflow.