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SGLang with DeepSeek R1 Deployment Guide

Introduction​

This guide explains how to deploy SGLang with DeepSeek R1 model on TKE for efficient language model serving.

Prerequisites​

Environment Requirements​

  • Kubernetes cluster with GPU support
  • NVIDIA GPU drivers installed
  • Sufficient GPU memory for model loading

Software Dependencies​

  • SGLang framework
  • DeepSeek R1 model weights
  • Python 3.8+ environment

Deployment Steps​

1. Model Preparation​

Download DeepSeek R1 model weights:

# Download model weights
wget https://example.com/deepseek-r1-weights.tar.gz
tar -xzf deepseek-r1-weights.tar.gz

2. SGLang Configuration​

Create SGLang configuration file:

# sglang_config.py
import sglang as sgl

# Configure SGLang runtime
sgl.init(
    model_path="/path/to/deepseek-r1",
    gpu_memory_utilization=0.8,
    max_num_seqs=64,
    tensor_parallel_size=1
)

3. Kubernetes Deployment​

Create deployment manifest:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: sglang-deepseek-r1
spec:
  replicas: 2
  selector:
    matchLabels:
      app: sglang-deepseek
  template:
    metadata:
      labels:
        app: sglang-deepseek
    spec:
      containers:
      - name: sglang-server
        image: sglang-deepseek-r1:latest
        ports:
        - containerPort: 8000
        resources:
          limits:
            nvidia.com/gpu: 1
            memory: 16Gi
            cpu: 4
          requests:
            nvidia.com/gpu: 1
            memory: 16Gi
            cpu: 4
        volumeMounts:
        - name: model-weights
          mountPath: /models
      volumes:
      - name: model-weights
        persistentVolumeClaim:
          claimName: deepseek-model-pvc

Service Configuration​

Load Balancer Service​

apiVersion: v1
kind: Service
metadata:
  name: sglang-service
spec:
  selector:
    app: sglang-deepseek
  ports:
  - port: 80
    targetPort: 8000
  type: LoadBalancer

API Endpoints​

  • /generate: Text generation endpoint
  • /chat: Chat completion endpoint
  • /health: Health check endpoint

Performance Optimization​

GPU Memory Optimization​

  • Use model quantization
  • Implement memory-efficient attention
  • Configure appropriate batch sizes

Inference Optimization​

  • Enable KV cache optimization
  • Use continuous batching
  • Implement speculative decoding

Monitoring and Logging​

Key Metrics​

  • Request latency
  • Token generation speed
  • GPU utilization
  • Memory usage patterns

Logging Configuration​

import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

Troubleshooting​

Common Issues​

  • GPU memory allocation failures
  • Model loading errors
  • Network connectivity issues

Debug Commands​

# Check GPU status
nvidia-smi

# Check container logs
kubectl logs <pod-name>

# Verify service connectivity
curl http://sglang-service/health