GPU Go VSCode Extension

Use GPU Like NFS - Seamlessly manage AI/ML development environments with remote GPU access directly from VS Code.

This extension is part of the TensorFusion ecosystem, allowing you to manage your GPU resources and development environments directly from your IDE.

🚀 Quick Start

Step 1: Register & Setup Agent

Before using the VS Code extension, you need a TensorFusion account and a running GPU agent.

1. Go to the TensorFusion Dashboard.
2. Register for an account.
3. Follow the dashboard instructions to install and start the GPUGo Agent on your GPU machine (Server).

Step 2: Install Extension

Install this extension from the VS Code Marketplace.

Step 3: Login

1. Click on the GPU Go icon in the Activity Bar.
2. Click "Login to GPU Go".
3. Go to the Dashboard to generate a Personal Access Token (PAT).
4. Paste the token into the VS Code prompt.

Step 4: Create Studio

Once logged in, you can create a new Studio environment:

1. Click the "Create Studio" button or use the command palette (GPU Go: Create Studio Environment).
2. Select your preferred environment (e.g., PyTorch, TensorFlow).
3. The extension will automatically set up the local container and connect it to your remote GPU.

✨ Features

🖥️ Studio Environments

• One-Click Creation: Spin up GPU-powered environments instantly.
• SSH Integration: Connect directly from VS Code terminal.
• Pre-configured Images: PyTorch, TensorFlow, Jupyter, and more.
• Automatic SSH Config: No manual configuration needed.

🔧 GPU Workers & Devices

• View and manage GPU workers across your infrastructure.
• Monitor active connections and device utilization in real-time.
• View GPU specifications (model, VRAM, driver version).

🔗 Links

• 📖 Documentation
• 🐛 Report Issues
• 💬 Discord Community
• 🌐 Website

📄 License

Proprietary - Copyright © 2026 NexusGPU PTE. LTD. All rights reserved. See LICENSE for details.