⚙️ Environment Configuration
Configure environment variables and device access for your container, including GPU support and custom environment settings.
🌍 Environment Variables
Set Custom Environment Variables
Configure environment variables that will be available in the container
Format: KEY=VALUE (e.g., NODE_ENV=production)
Common Examples
• NODE_ENV=production
• DEBUG=true
• PYTHONPATH=/app/src
• PORT=3000
• DATABASE_URL=postgresql://...
Quick Add
Current Environment Variables
NODE_ENV=production
DEBUG=true
💡 Tip: To delete a variable, you can also set it to an empty value (KEY=)
🎮 Device Configuration
Enable GPU Support
Enable NVIDIA GPU access for the container
⚠️ GPU Requirements
Prerequisites:
- NVIDIA Docker runtime installed
- Compatible GPU drivers
- CUDA-compatible base image
Note: We do not detect GPU availability automatically. Enable only if you have the required setup.
🐳 Recommended GPU Base Images
For GPU workloads, consider these base images:
nvidia/cuda:12.6.3-cudnn-devel-ubuntu24.04
nvidia/cuda:12.6.3-cudnn-runtime-ubuntu24.04
pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel
tensorflow/tensorflow:2.13.0-gpu
👀 Environment Configuration Preview
Docker Run Command
docker run -d \
--name my-awesome-project \
-e NODE_ENV=production \
-e DEBUG=true \
my-awesome-project:stage-2
Environment Inside Container
$ env | grep -E "(NODE_ENV|DEBUG)"
NODE_ENV=production
DEBUG=true
$ nvidia-smi
NVIDIA-SMI 535.86.10
Stage-1 Configuration Impact
⚠️ Important: Environment variables and device configuration will only be set in stage-1 of user_config.
For stage-2 configuration, you'll need to edit the config file directly after saving.