AI/ML Templates

The AI/ML Templates feature streamlines the process of setting up machine learning environments by providing a selection of pre-configured stacks tailored for rapid development and deployment. Whether you're a data scientist, ML engineer, or a developer experimenting with new frameworks, these templates offer a hassle-free jumpstart for your projects.

Key Benefits

• Instant Setup: Deploy complex environments—like TensorFlow, PyTorch, Ubuntu, n8n, ComfyUI, and Langflow—in just a few clicks.
• Zero Manual Configuration: All dependencies and packages come pre-installed and ready to use, removing the friction of setup.
• GPU Optimization: Select your preferred GPU instance for accelerated training and inference.
• Rapid Prototyping: Start your project immediately, focusing on innovation instead of environment issues.
• Flexibility: Suitable for a wide array of projects, from deep learning research to workflow automation.

Available Templates

Name	Description	Typical Use Cases
TensorFlow	Deep learning library for neural networks and ML tasks	Computer vision, NLP, research
PyTorch	Popular ML framework with dynamic computation graphs	AI research, prototyping
Ubuntu	Base Linux OS for custom/legacy setups	General ML/dev environments
n8n	Workflow automation tool with AI integrations	Business automation, ETL
ComfyUI	Modular, graphical UI for AI/ML pipelines	ML pipeline orchestration
Langflow	Tool for building and managing LLM-powered workflows	Chatbots, LLM apps

How It Works

1. Choose Your Template: Pick a stack that suits your project needs.
2. Select GPU Instance: Optimize compute resources by selecting a GPU type.
3. Launch Instantly: Deploy in moments—no manual installation required.
4. Start Building: Dive straight into development, leveraging robust, pre-tuned frameworks.

Why Use AI/ML Templates?

• Minimize setup time and maximize productivity.
• Eliminate compatibility headaches.
• Effortlessly switch between frameworks or experiment with new tools.
• Scale projects from idea to deployment—fast!

Example Workflow

You want to start a PyTorch-based NLP project:

1. Select the "PyTorch" template.
2. Choose a GPU instance (e.g., NVIDIA A100).
3. Configure your GPU instance and No of GPU's.
4. Select Root Storage.
5. Selet Auto-stop duration.
6. Launch and enter your workspace instantly—PyTorch is pre-configured, CUDA is enabled, and you’re ready to code!

Get Started Now

Empower your AI and ML innovation with one-click environment templates. Forget manual setup and start building today!