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"""
Helion-V1 Production Deployment Script
Optimized for serving with vLLM, TGI, or custom inference servers
"""
import os
import json
import logging
from typing import Dict, List, Optional
from dataclasses import dataclass
import asyncio
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
@dataclass
class DeploymentConfig:
"""Configuration for model deployment."""
model_name: str = "DeepXR/Helion-V1"
tensor_parallel_size: int = 1
max_model_len: int = 4096
max_num_seqs: int = 256
gpu_memory_utilization: float = 0.90
trust_remote_code: bool = True
quantization: Optional[str] = None # "awq", "gptq", or None
dtype: str = "bfloat16"
enforce_eager: bool = False
# Safety settings
max_tokens: int = 2048
temperature: float = 0.7
top_p: float = 0.9
frequency_penalty: float = 0.1
presence_penalty: float = 0.1
# Rate limiting
rate_limit_requests_per_minute: int = 60
rate_limit_tokens_per_minute: int = 90000
class HelionDeployment:
"""
Production deployment handler for Helion-V1.
Supports vLLM, Text Generation Inference, and custom servers.
"""
def __init__(self, config: DeploymentConfig):
self.config = config
self.model = None
self.tokenizer = None
def deploy_vllm(self):
"""Deploy using vLLM for high-throughput inference."""
try:
from vllm import LLM, SamplingParams
logger.info("Initializing vLLM engine...")
self.model = LLM(
model=self.config.model_name,
tensor_parallel_size=self.config.tensor_parallel_size,
max_model_len=self.config.max_model_len,
max_num_seqs=self.config.max_num_seqs,
gpu_memory_utilization=self.config.gpu_memory_utilization,
trust_remote_code=self.config.trust_remote_code,
quantization=self.config.quantization,
dtype=self.config.dtype,
enforce_eager=self.config.enforce_eager
)
logger.info("✅ vLLM engine initialized successfully")
return True
except ImportError:
logger.error("vLLM not installed. Install with: pip install vllm")
return False
except Exception as e:
logger.error(f"Failed to initialize vLLM: {e}")
return False
def get_sampling_params(self) -> 'SamplingParams':
"""Get vLLM sampling parameters."""
from vllm import SamplingParams
return SamplingParams(
temperature=self.config.temperature,
top_p=self.config.top_p,
max_tokens=self.config.max_tokens,
frequency_penalty=self.config.frequency_penalty,
presence_penalty=self.config.presence_penalty
)
def generate_vllm(self, prompts: List[str]) -> List[str]:
"""Generate responses using vLLM."""
if not self.model:
raise RuntimeError("Model not initialized. Call deploy_vllm() first.")
sampling_params = self.get_sampling_params()
outputs = self.model.generate(prompts, sampling_params)
return [output.outputs[0].text for output in outputs]
def create_fastapi_server(self):
"""Create FastAPI server for HTTP API."""
try:
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import uvicorn
app = FastAPI(
title="Helion-V1 API",
description="Safe and helpful AI assistant API",
version="1.0.0"
)
# CORS middleware
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class ChatRequest(BaseModel):
messages: List[Dict[str, str]]
max_tokens: Optional[int] = 512
temperature: Optional[float] = 0.7
top_p: Optional[float] = 0.9
class ChatResponse(BaseModel):
response: str
model: str
usage: Dict[str, int]
@app.post("/v1/chat/completions", response_model=ChatResponse)
async def chat_completion(request: ChatRequest):
"""OpenAI-compatible chat completion endpoint."""
try:
# Format messages
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(self.config.model_name)
prompt = tokenizer.apply_chat_template(
request.messages,
tokenize=False,
add_generation_prompt=True
)
# Generate response
responses = self.generate_vllm([prompt])
return ChatResponse(
response=responses[0],
model=self.config.model_name,
usage={
"prompt_tokens": len(tokenizer.encode(prompt)),
"completion_tokens": len(tokenizer.encode(responses[0])),
"total_tokens": len(tokenizer.encode(prompt + responses[0]))
}
)
except Exception as e:
logger.error(f"Generation error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/health")
async def health_check():
"""Health check endpoint."""
return {"status": "healthy", "model": self.config.model_name}
@app.get("/")
async def root():
"""Root endpoint."""
return {
"name": "Helion-V1 API",
"version": "1.0.0",
"status": "online"
}
return app
except ImportError:
logger.error("FastAPI not installed. Install with: pip install fastapi uvicorn")
return None
def export_onnx(self, output_path: str = "./helion_onnx"):
"""Export model to ONNX format for optimized deployment."""
try:
from optimum.onnxruntime import ORTModelForCausalLM
from transformers import AutoTokenizer
logger.info("Exporting model to ONNX...")
model = ORTModelForCausalLM.from_pretrained(
self.config.model_name,
export=True
)
tokenizer = AutoTokenizer.from_pretrained(self.config.model_name)
model.save_pretrained(output_path)
tokenizer.save_pretrained(output_path)
logger.info(f"✅ Model exported to {output_path}")
return True
except ImportError:
logger.error("Optimum not installed. Install with: pip install optimum[onnxruntime-gpu]")
return False
except Exception as e:
logger.error(f"ONNX export failed: {e}")
return False
def create_docker_config(self, output_path: str = "./"):
"""Generate Dockerfile for containerized deployment."""
dockerfile_content = f"""FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04
# Set working directory
WORKDIR /app
# Install Python and dependencies
RUN apt-get update && apt-get install -y \\
python3.10 \\
python3-pip \\
git \\
&& rm -rf /var/lib/apt/lists/*
# Install Python packages
COPY requirements.txt .
RUN pip3 install --no-cache-dir -r requirements.txt
# Install vLLM for high-performance inference
RUN pip3 install vllm
# Copy application code
COPY . .
# Set environment variables
ENV MODEL_NAME={self.config.model_name}
ENV MAX_MODEL_LEN={self.config.max_model_len}
ENV GPU_MEMORY_UTILIZATION={self.config.gpu_memory_utilization}
ENV TENSOR_PARALLEL_SIZE={self.config.tensor_parallel_size}
# Expose port
EXPOSE 8000
# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \\
CMD curl -f http://localhost:8000/health || exit 1
# Run the application
CMD ["python3", "deployment.py", "--server"]
"""
dockerfile_path = os.path.join(output_path, "Dockerfile")
with open(dockerfile_path, 'w') as f:
f.write(dockerfile_content)
# Also create docker-compose.yml
docker_compose_content = f"""version: '3.8'
services:
helion-v1:
build: .
ports:
- "8000:8000"
environment:
- MODEL_NAME={self.config.model_name}
- CUDA_VISIBLE_DEVICES=0
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
volumes:
- model_cache:/root/.cache/huggingface
restart: unless-stopped
volumes:
model_cache:
"""
compose_path = os.path.join(output_path, "docker-compose.yml")
with open(compose_path, 'w') as f:
f.write(docker_compose_content)
logger.info(f"✅ Docker configuration created in {output_path}")
logger.info("Build with: docker-compose build")
logger.info("Run with: docker-compose up -d")
def main():
"""Main deployment function."""
import argparse
parser = argparse.ArgumentParser(description="Deploy Helion-V1")
parser.add_argument("--model", default="DeepXR/Helion-V1", help="Model name or path")
parser.add_argument("--backend", choices=["vllm", "tgi", "fastapi"], default="vllm")
parser.add_argument("--server", action="store_true", help="Start HTTP server")
parser.add_argument("--export-onnx", action="store_true", help="Export to ONNX")
parser.add_argument("--create-docker", action="store_true", help="Create Docker config")
parser.add_argument("--tensor-parallel", type=int, default=1)
parser.add_argument("--quantization", choices=["awq", "gptq", None], default=None)
args = parser.parse_args()
# Create config
config = DeploymentConfig(
model_name=args.model,
tensor_parallel_size=args.tensor_parallel,
quantization=args.quantization
)
deployment = HelionDeployment(config)
if args.export_onnx:
deployment.export_onnx()
if args.create_docker:
deployment.create_docker_config()
if args.server:
if args.backend == "vllm":
if deployment.deploy_vllm():
app = deployment.create_fastapi_server()
if app:
import uvicorn
logger.info("🚀 Starting Helion-V1 server on http://0.0.0.0:8000")
uvicorn.run(app, host="0.0.0.0", port=8000)
else:
logger.error(f"Backend {args.backend} not implemented yet")
else:
logger.info("No action specified. Use --help for options.")
if __name__ == "__main__":
main() |