Google Cloud Deployment¶
Deploy GPUX models on Google Cloud Platform with GPU support.
🎯 Overview¶
Complete guide for deploying GPUX on GCP with Cloud GPU and managed services.
In Development
Detailed deployment guide for GCP is being developed. Basic functionality is available.
📚 What Will Be Covered¶
- 🔄 Cloud GPU: T4, V100, A100 instance types
- 🔄 GKE with GPU: Kubernetes on GCP with GPU nodes
- 🔄 Cloud Run: Serverless container deployment
- 🔄 Cloud Functions: Serverless inference patterns
- 🔄 Cloud Storage: Model artifact storage
- 🔄 Cloud Monitoring: Metrics and logging
- 🔄 Cost Optimization: Preemptible instances and auto-scaling
🚀 Quick Start¶
Compute Engine GPU Instance¶
# Create GPU instance
gcloud compute instances create gpux-gpu \
--zone=us-central1-a \
--machine-type=n1-standard-4 \
--accelerator=type=nvidia-tesla-t4,count=1 \
--image-family=ubuntu-2004-lts \
--image-project=ubuntu-os-cloud \
--maintenance-policy=TERMINATE \
--restart-on-failure
# Connect and install GPUX
ssh <instance-ip>
sudo apt update
sudo apt install -y python3 python3-pip
pip3 install gpux
# Pull and serve model
gpux pull microsoft/DialoGPT-medium
gpux serve microsoft/DialoGPT-medium --port 8080
Docker on GCP¶
FROM python:3.11-slim
WORKDIR /app
RUN pip install gpux
# Pull model from Hugging Face Hub
RUN gpux pull microsoft/DialoGPT-medium
EXPOSE 8080
CMD ["gpux", "serve", "microsoft/DialoGPT-medium", "--port", "8080"]
💡 Key Takeaways¶
Success
✅ Cloud GPU Support: T4, V100, A100 instances ✅ GKE Integration: Kubernetes with GPU nodes ✅ Model Registry Integration: Pull models from Hugging Face Hub ✅ Auto-scaling: Managed instance groups ✅ Monitoring: Cloud Monitoring integration